{"id":1125,"date":"2021-02-17T12:13:30","date_gmt":"2021-02-17T03:13:30","guid":{"rendered":"http:\/\/54.178.40.148\/?page_id=1125"},"modified":"2026-03-28T20:56:24","modified_gmt":"2026-03-28T11:56:24","slug":"%e6%ad%b4%e4%bb%a3%e5%8f%97%e8%b3%9e%e8%80%85","status":"publish","type":"page","link":"https:\/\/orsj.org\/award-history","title":{"rendered":"OR\u5b66\u4f1a\u5404\u8cde \u6b74\u4ee3\u53d7\u8cde\u8005"},"content":{"rendered":"

\u53d7\u8cde\u8005\u306e\u6240\u5c5e\u306f\u53d7\u8cde\u6642\u306e\u3082\u306e\u3067\u3059\uff0e
\n\u5404\u8cde\u306e\u5e74\u5ea6\u306e\u30ea\u30f3\u30af\u5148\u306f\uff0c\u6a5f\u95a2\u8a8c\u306e\u5b66\u4f1a\u30cb\u30e5\u30fc\u30b9\u3068\u306a\u3063\u3066\u304a\u308a\uff0c\u6388\u8cde\u7406\u7531\u304c\u8a18\u8f09\u3055\u308c\u3066\u3044\u307e\u3059\uff0e<\/p>\n

\u5404\u8cde\u306b\u95a2\u3059\u308b\u8a73\u3057\u3044\u8aac\u660e\uff0c\u304a\u3088\u3073\u52df\u96c6\u8981\u9805\u306f\u3053\u3061\u3089\u306e\u30da\u30fc\u30b8<\/a>\u306b\u66f8\u3044\u3066\u3042\u308a\u307e\u3059\uff0e<\/p>\n

\u8fd1\u85e4\u8cde<\/span>\u7814\u7a76\u8cde<\/span>\u7814\u7a76\u8cde\u5968\u52b1\u8cde<\/span>\u696d\u7e3e\u8cde<\/span>\u666e\u53ca\u8cde<\/span>\u5b9f\u65bd\u8cde<\/span>\u4e8b\u4f8b\u7814\u7a76\u8cde<\/span>\u8ad6\u6587\u8cde<\/span>\u5b66\u751f\u8ad6\u6587\u8cde<\/span>\u7279\u5225\u8cde<\/span><\/div>
\n
\u8fd1\u85e4\u8cde Kondo Prize<\/h5>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u56de<\/th>\n\u5e74\u5ea6<\/th>\n\u53d7\u8cde\u8005<\/th>\n\u6240\u5c5e<\/th>\n\u53d7\u8cde\u7406\u7531<\/th>\n<\/tr>\n<\/thead>\n
10<\/td>\n2025<\/td>\n\u8a72\u5f53\u8005\u306a\u3057<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
9<\/td>\n2023<\/a><\/td>\n\u52a0\u85e4\u76f4\u6a39<\/td>\n\u5175\u5eab\u770c\u7acb\u5927\u5b66\u6559\u6388\u30fb\u4eac\u90fd\u5927\u5b66\u540d\u8a89\u6559\u6388<\/td>\n
\n\u52a0\u85e4\u76f4\u6a39\u6c0f\u306f\uff0cOR\u306e\u4e3b\u8981\u306a\u5206\u91ce\u3067\u3042\u308b\u6570\u7406\u6700\u9069\u5316\uff0c\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3084\u8a08\u7b97\u6a5f\u79d1\u5b66\u306b\u304a\u3051\u308b\u7406\u8ad6\u7684\u306a\u7814\u7a76\uff0c\u306a\u3089\u3073\u306bOR\u306e\u8af8\u624b\u6cd5\u3092\u5efa\u7bc9\u3084\u30c7\u30fc\u30bf\u30de\u30a4\u30cb\u30f3\u30b0\u5206\u91ce\u306a\u3069\u306b\u9069\u7528\u3057\u305f\u7814\u7a76\u306b\u304a\u3044\u3066\u9855\u8457\u306a\u696d\u7e3e\u3092\u6b8b\u3057\u3066\u304d\u305f\uff0e\u52a0\u85e4\u6c0f\u306e\u4e3b\u8981\u306a\u7814\u7a76\u696d\u7e3e\u3068\u3057\u3066\uff0c\u8a08\u7b97\u5e7e\u4f55\u5b66\u3084\u7d44\u5408\u305b\u6700\u9069\u5316\u306e\u5206\u91ce\u3067\u306e\u7406\u8ad6\u7684\u306a\u8ca2\u732e\u304c\u6319\u3052\u3089\u308c\u308b\uff0e\u307e\u305f\uff0c\u52a0\u85e4\u6c0f\u306e\u5fdc\u7528\u7684\u7814\u7a76\u306f\u975e\u5e38\u306b\u591a\u5c90\u306b\u6e21\u308b\u3068\u3068\u3082\u306b\uff0c\u69d8\u3005\u306a\u6559\u79d1\u66f8\u3092\u57f7\u7b46\u3057\u3066\u304a\u308a\uff0c\u6700\u9069\u5316\u3092\u4e2d\u5fc3\u3068\u3059\u308bOR\u7684\u624b\u6cd5\u306e\u4ed6\u5206\u91ce\u3078\u62e1\u5927\u306b\u95a2\u3057\u3066\u591a\u5927\u306a\u8ca2\u732e\u3092\u679c\u305f\u3057\u305f\uff0e\u65e5\u672c\u30aa\u30da\u30ec\u30fc\u30b7\u30e7\u30f3\u30ba\u30fb\u30ea\u30b5\u30fc\u30c1\u5b66\u4f1a\u306b\u304a\u3044\u3066\u306f\uff0c\u526f\u4f1a\u9577\uff0c\u7de8\u96c6\u7406\u4e8b\uff0c\u95a2\u897f\u652f\u90e8\u9577\u7b49\u3092\u6b74\u4efb\u3057\uff0c\u6587\u732e\u8cde\uff0c\u4e8b\u4f8b\u7814\u7a76\u8cde\uff0c\u696d\u7e3e\u8cde\u3092\u53d7\u8cde\u3057\u3066\u3044\u308b\uff0e
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
8<\/td>\n2021<\/a><\/td>\n\u5ba4\u7530\u4e00\u96c4<\/td>\n\u6771\u4eac\u90fd\u7acb\u5927\u5b66\u6559\u6388\u30fb\u6771\u4eac\u5927\u5b66\u540d\u8a89\u6559\u6388\u30fb\u4eac\u90fd\u5927\u5b66\u540d\u8a89\u6559\u6388<\/td>\n
\n\u5ba4\u7530\u4e00\u96c4\u6c0f\u306f\uff0c\u6570\u5024\u8a08\u7b97\u6cd5(\u6570\u5024\u7a4d\u5206\uff0c\u7dda\u5f62\u8a08\u7b97)\uff0c\u30de\u30c8\u30ed\u30a4\u30c9\u7406\u8ad6\u306e\u30b7\u30b9\u30c6\u30e0\u89e3\u6790\u3078\u306e\u5fdc\u7528\uff0c\u7fa4\u8ad6\u7684\u5206\u5c90\u7406\u8ad6\u306e\u69cb\u9020\u5de5\u5b66\u3078\u306e\u5fdc\u7528\uff0c\u8a08\u7b97\u5e7e\u4f55\u5b66\uff0c\u7d4c\u6e08\u5730\u7406\u5b66\u30fb\u7a7a\u9593\u7d4c\u6e08\u5b66\u306a\u3069\u6570\u7406\u5de5\u5b66\u306e\u69d8\u3005\u306a\u5206\u91ce\u306b\u6e21\u3063\u3066\uff0c\u512a\u308c\u305f\u7814\u7a76\u6210\u679c\u3092\u6319\u3052\u308b\u3068\u5171\u306b\uff0c\u6570\u3005\u306e\u8457\u66f8\u3092\u901a\u3058\u3066\uff0c\u6570\u7406\u5de5\u5b66\u306e\u6559\u80b2\u30fb\u666e\u53ca\u306b\u591a\u5927\u306a\u8ca2\u732e\u3092\u3057\u3066\u304d\u305f\uff0e\u7279\u306b\u96e2\u6563\u51f8\u89e3\u6790\u3068\u3044\u3046\u5206\u91ce\u3092\u5275\u59cb\u3057\uff0c\u6700\u8fd1\u306e25\u5e74\u9593\u306b\u6e21\u3063\u3066\u4e3b\u5c0e\u7684\u306a\u7acb\u5834\u3067\u7814\u7a76\u3092\u9032\u3081\u3066\u3044\u308b\uff0e
\n\u5ba4\u7530\u4e00\u96c4\u6c0f\u306f\uff0c2014\u5e74\u306b\u7b2c15\u56de\u696d\u7e3e\u8cde\u3092\u53d7\u8cde\u3057\u3066\u304a\u308a\uff0c\u6570\u7406\u5de5\u5b66\u306e\u69d8\u3005\u306a\u5206\u91ce\u3067\u9855\u8457\u306a\u696d\u7e3e\u3092\u6319\u3052\u3066\u304d\u305f\u3053\u3068\u304c\u9ad8\u304f\u8a55\u4fa1\u3055\u308c\u3066\u3044\u308b\uff0e2014\u5e74\u304b\u30892\u5e74\u9593\u306f\u526f\u4f1a\u9577\u3092\u52d9\u3081\u308b\u306a\u3069\uff0c\u65e5\u672c\u30aa\u30da\u30ec\u30fc\u30b7\u30e7\u30f3\u30ba\u30fb\u30ea\u30b5\u30fc\u30c1\u5b66\u4f1a\u306e\u767a\u5c55\u306b\u3082\u5bc4\u4e0e\u3057\u3066\u3044\u308b\uff0e\u307e\u305f\uff0c\u6771\u4eac\u5927\u5b66\uff0c\u4eac\u90fd\u5927\u5b66\uff0c\u7b51\u6ce2\u5927\u5b66\uff0c\u6771\u4eac\u90fd\u7acb\u5927\u5b66\u306b\u304a\u3044\u3066\u9577\u5e74\u6559\u80b2\u306b\u643a\u308f\u308a\uff0c\u4f01\u696d\u30fb\u6559\u80b2\u6a5f\u95a2\u7b49\u306b\u591a\u304f\u306e\u512a\u79c0\u306a\u4eba\u6750\u3092\u8f29\u51fa\u3057\uff0c\u30aa\u30da\u30ec\u30fc\u30b7\u30e7\u30f3\u30ba\u30fb\u30ea\u30b5\u30fc\u30c1\u5206\u91ce\u306e\u4eba\u6750\u80b2\u6210\u306b\u5927\u304d\u304f\u8ca2\u732e\u3057\u3066\u3044\u308b\uff0e
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
7<\/td>\n2019<\/a><\/td>\n\u5c71\u4e0b\u6d69<\/td>\n\uff08\u682a\uff09NTT\u30c7\u30fc\u30bf\u6570\u7406\u30b7\u30b9\u30c6\u30e0<\/td>\n
\n\u5c71\u4e0b\u6d69\u6c0f\u306f\uff0c\u30aa\u30da\u30ec\u30fc\u30b7\u30e7\u30f3\u30ba\u30fb\u30ea\u30b5\u30fc\u30c1\u306b\u304a\u3051\u308b\u7406\u8ad6\u3068\u5b9f\u8df5\u306e\u4e21\u7acb\u3092\u8eab\u3092\u3082\u3063\u3066\u793a\u3057\u305f\u7a00\u6709\u306e\u5b58\u5728\u3067\u3042\u308b\uff0e1982\u5e744\u6708\u306b\uff08\u682a\uff09\u6570\u7406\u30b7\u30b9\u30c6\u30e0\uff08\u73fe\uff08\u682a\uff09NTT\u30c7\u30fc\u30bf\u6570\u7406\u30b7\u30b9\u30c6\u30e0\uff09\u3092\u8a2d\u7acb\u3057\uff0c\u6570\u7406\u79d1\u5b66\u3068\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u3092\u6d3b\u7528\u3057\u3066\u3055\u307e\u3056\u307e\u306a\u73fe\u5b9f\u554f\u984c\u3092\u89e3\u6c7a\u3059\u308b\u65e5\u672c\u6709\u6570\u306e\u6280\u8853\u529b\u3092\u3082\u3063\u305f\u4f01\u696d\u306b\u80b2\u3066\u4e0a\u3052\u305f\uff0e\u305d\u306e\u4e00\u65b9\u3067\uff0c\u7814\u7a76\u8005\u3068\u3057\u3066\u975e\u7dda\u5f62\u6700\u9069\u5316\u3068\u6570\u7406\u30e2\u30c7\u30ea\u30f3\u30b0\u306e\u7814\u7a76\u3092\u884c\u3044\uff0c\u305d\u306e\u7814\u7a76\u6210\u679c\u3092\u56fd\u969b\u5b66\u8853\u8a8c\u306b\u8ad6\u6587\u3068\u3057\u3066\u767a\u8868\u3059\u308b\u3068\u3068\u3082\u306b\uff0c\u6570\u7406\u8a08\u753b\u6cd5\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2NUOPT\uff08\u73feNumerical Optimizer\uff09\u3092\u901a\u3058\u3066\u793e\u4f1a\u306b\u9084\u5143\u3057\u3066\u304d\u305f\uff0e
\n\u3053\u308c\u3089\u306e\u696d\u7e3e\u306b\u3088\u308a\uff0c\u5c71\u4e0b\u6c0f\u306f\uff0c2004\u5e74\u306b\u65e5\u672c\u30aa\u30da\u30ec\u30fc\u30b7\u30e7\u30f3\u30ba\u30fb\u30ea\u30b5\u30fc\u30c1\u5b66\u4f1a\u696d\u7e3e\u8cde\u3092\u53d7\u8cde\u3057\uff0c\u540c\u5b66\u4f1a\u306e\u30d5\u30a7\u30ed\u30fc\u306b\u3082\u5217\u305b\u3089\u308c\u3066\u3044\u308b\uff0e\u307e\u305f\uff0c\u6570\u7406\u30b7\u30b9\u30c6\u30e0\uff082013\u5e74\u3088\u308a\u5546\u53f7\u5909\u66f4\u306b\u3088\u308aNTT\u30c7\u30fc\u30bf\u6570\u7406\u30b7\u30b9\u30c6\u30e0\uff09\u306f\uff0c 1999\u5e74\u304a\u3088\u30732016\u5e74\u306b\u540c\u5b66\u4f1a\u5b9f\u65bd\u8cde\u3092\u53d7\u8cde\u3057\u3066\u3044\u308b\uff0e\u3055\u3089\u306b\uff0c\u4ed6\u5b66\u4f1a\u304b\u3089\u3082\u305d\u306e\u696d\u7e3e\u3092\u8a8d\u3081\u3089\u308c\uff0c\u65e5\u672c\u8a08\u7b97\u6a5f \u7d71\u8a08\u5b66\u4f1a\u8ca2\u732e\u8cde\u30922\u56de\u53d7\u8cde\u3057\u3066\u3044\u308b\uff0e
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
6<\/td>\n2017<\/a><\/td>\n\u7530\u53e3\u6771<\/td>\n\u4e2d\u592e\u5927\u5b66\u6559\u6388<\/td>\n
\n\u7530\u53e3\u6771\u6c0f\u306f\uff0c30\u6570\u5e74\u9593\u306b\u308f\u305f\u308a\uff0c\u73fe\u5b9f\u793e\u4f1a\u306e\u8ab2\u984c\u306bOR\u306e\u624b\u6cd5\u3092\u9069\u7528\u3059\u308b\uff0cOR\u672c\u6765\u306e\u5b9f\u7528\u7814\u7a76\u3092\u5b9f\u8df5\u3057\u3066\u304d\u305f\uff0e\u73fe\u5b9f\u306e\u30c7\u30fc\u30bf\u306b\u57fa\u3065\u304f\u7cbe\u7dfb\u306a\u30e2\u30c7\u30eb\u5316\u3092\u884c\u3044\uff0cOR\u306e\u9ad8\u5ea6\u306a\u624b\u6cd5\u3092\u99c6\u4f7f\u3057\u3066\u89e3\u6c7a\u7b56\u3092\u5c0e\u304d\u793e\u4f1a\u3078\u30d5\u30a3\u30fc\u30c9\u30d0\u30c3\u30af\u3059\u308b\u3068\u3044\u3046\uff0c\u6c0f\u306e\u4e00\u8cab\u3057\u305f\u7814\u7a76\u30b9\u30bf\u30a4\u30eb\u306b\u3088\u308a\uff0c\u7530\u5712\u90fd\u5e02\u7dda\u306e\u9045\u308c\u306e\u539f\u56e0\u306e\u7a76\u660e\uff0c\u6771\u65e5\u672c\u5927\u9707\u707d\u5730\u57df\u306b\u304a\u3051\u308b\u30d0\u30b9\u6642\u523b\u8868\u306e\u63d0\u6848\uff0c\u6771\u4eac\u30aa\u30ea\u30f3\u30d4\u30c3\u30af\u89b3\u6226\u5ba2\u8f38\u9001\u306e\u6df7\u96d1\u5206\u6790\u306a\u3069\uff0c\u610f\u7fa9\u306e\u3042\u308b\u4e8b\u4f8b\u7814\u7a76\u3092\u6570\u591a\u304f\u767a\u8868\u3057\uff0cOR\u306e\u6709\u7528\u6027\u3092\u4e16\u306e\u4e2d\u306b\u77e5\u3089\u3057\u3081\u305f\uff0e
\n\u307e\u305f\uff0c\u300c\u90fd\u5e02\u306eOR\u300d\u7814\u7a76\u4f1a\u3067\u306f\u4e2d\u5fc3\u7684\u306a\u5f79\u5272\u3092\u679c\u305f\u3057\uff0c\u90fd\u5e02\u3084\u5730\u57df\u306e\u73fe\u8c61\u306b\u95a2\u3059\u308b\u8af8\u554f\u984c\u306bOR\u624b\u6cd5\u3092\u9069\u7528\u3059\u308b\u65b0\u3057\u3044\u7814\u7a76\u5206\u91ce\u3092\u78ba\u7acb\u3057\uff0c\u591a\u304f\u306e\u7814\u7a76\u8005\u304c\u96c6\u3046\u6d3b\u6c17\u3042\u308b\u5206\u91ce\u306b\u6210\u9577\u3055\u305b\u305f\u529f\u7e3e\u306f\u5927\u304d\u3044\uff0e
\n\u672c\u5b66\u4f1a\u306b\u304a\u3044\u3066\u3082\uff0c\u5eb6\u52d9\u7406\u4e8b\uff0c\u7406\u4e8bOR\u8a8c\u7de8\u96c6\u59d4\u54e1\u9577\uff0c\u526f\u4f1a\u9577\u3092\u6b74\u4efb\u3057\uff0c\u5b66\u4f1a\u306e\u767a\u5c55\u306b\u5927\u3044\u306b\u8ca2\u732e\u3057\u305f\uff0e
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
5<\/td>\n2015<\/a><\/td>\n\u798f\u5cf6\u96c5\u592b<\/td>\n\u5357\u5c71\u5927\u5b66\u6559\u6388\u30fb\u4eac\u90fd\u5927\u5b66\u540d\u8a89\u6559\u6388<\/td>\n
\n\u798f\u5cf6\u96c5\u592b\u6c0f\u306f\uff0c\u975e\u7dda\u5f62\u8a08\u753b\u6cd5\u3092\u4e2d\u5fc3\u3068\u3057\u3066\uff0c\u5909\u5206\u4e0d\u7b49\u5f0f\u554f\u984c\u3084\u76f8\u88dc\u6027\u554f\u984c\u306a\u3069\u306e\u5747\u8861\u554f\u984c\u306e\u7814\u7a76\u306b\u53d6\u308a\u7d44\u307f\uff0c200\u7de8\u3092\u8d85\u3048\u308b\u5b66\u8853\u8ad6\u6587\u3092\u767a\u8868\u3057\uff0c\u65e5\u672c\u3092\u4ee3\u8868\u3059\u308b\u7814\u7a76\u8005\u3068\u3057\u3066\uff0c\u4e16\u754c\u7684\u306b\u9ad8\u3044\u8a55\u4fa1\u3092\u5f97\u3066\u3044\u308b\uff0e\u307e\u305f\uff0c\u4eac\u90fd\u5927\u5b66\uff0c\u5948\u826f\u5148\u7aef\u79d1\u5b66\u6280\u8853\u5927\u5b66\u9662\u5927\u5b66\u306b\u304a\u3044\u3066\uff0c\u591a\u304f\u306e\u512a\u308c\u305f\u7814\u7a76\u8005\u3092\u80b2\u6210\u3059\u308b\u3068\u3068\u3082\u306b\uff0c\u4e2d\u56fd\uff0c\u9999\u6e2f\uff0c\u7c73\u56fd\u306a\u3069\u306e\u8457\u540d\u7814\u7a76\u8005\u3068\u3068\u3082\u306bPacific Optimization Research Activity Group\u3092\u7acb\u3061\u4e0a\u3052\uff0c\u305d\u306e\u4f1a\u9577\u3092\u52d9\u3081\uff0cPacific Journal of Optimization\u8a8c\u3092\u767a\u884c\u3057\uff0c\u305d\u306e\u7de8\u96c6\u9577\u3092\u52d9\u3081\u308b\u306a\u3069\uff0c\u30a2\u30b8\u30a2\u592a\u5e73\u6d0b\u5730\u57df\u306b\u304a\u3051\u308b\u6570\u7406\u6700\u9069\u5316\u5206\u91ce\u306e\u767a\u5c55\u306b\u4e2d\u5fc3\u7684\u306a\u5f79\u5272\u3092\u679c\u305f\u3057\u3066\u304d\u305f\uff0e\u307e\u305f\uff0c15\u8a8c\u306b\u3082\u53ca\u3076\u56fd\u969b\u5b66\u8853\u96d1\u8a8c\u306e\u7de8\u96c6\u306b\u643a\u308f\u308b\u306a\u3069\uff0c\u305d\u306e\u529f\u7e3e\u306f\u6975\u3081\u3066\u5927\u304d\u3044\uff0e
\n\u672c\u5b66\u4f1a\u3067\u306f\uff0c\u6570\u7406\u8a08\u753b\u5c02\u9580\u90e8\u4f1a\uff08RAMP\uff09\u4e3b\u67fb\uff0c\u8ad6\u6587\u8a8c\u7de8\u96c6\u59d4\u54e1\u3092\u52d9\u3081\u308b\u306a\u3069\uff0c\u5b66\u4f1a\u306e\u767a\u5c55\u306b\u591a\u5927\u306a\u8ca2\u732e\u3092\u3055\u308c\u3066\u3044\u308b\uff0e
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
4<\/td>\n2013<\/a><\/td>\n\u85e4\u91cd\u609f<\/td>\n\u4eac\u90fd\u5927\u5b66\u6570\u7406\u89e3\u6790\u7814\u7a76\u6240\u7279\u4efb\u6559\u6388\u30fb\u5927\u962a\u5927\u5b66\u540d\u8a89\u6559\u6388\u30fb\u4eac\u90fd\u5927\u5b66\u540d\u8a89\u6559\u6388<\/td>\n
\n\u85e4\u91cd\u609f\u6c0f\u306f\uff0c\u6570\u7406\u8a08\u753b\u6cd5\u306b\u304a\u3051\u308b\u52a3\u30e2\u30b8\u30e5\u30e9\u95a2\u6570\u306e\u5206\u91ce\u3067\u591a\u304f\u306e\u5353\u8d8a\u3057\u305f\u7814\u7a76\u696d\u7e3e\u3092\u3042\u3052\u3066\u304d\u305f\uff0e\u6700\u5927\u306e\u696d\u7e3e\u306f\uff0c\u52a3\u30e2\u30b8\u30e5\u30e9\u95a2\u6570\u306e\u7406\u8ad6\u306e\u4f53\u7cfb\u5316\u3067\u3042\u308a\uff0c\u591a\u9762\u4f53\u7406\u8ad6\u3092\u901a\u3058\u3066\u52a3\u30e2\u30b8\u30e5\u30e9\u95a2\u6570\u3092\u7814\u7a76\u3059\u308b\u67a0\u7d44\u307f\u3092\u78ba\u7acb\u3057\u305f\u3053\u3068\u3067\u3042\u308b\uff0e\u6570\u7406\u8a08\u753b\u6cd5\u306b\u304a\u3051\u308b\u96e2\u6563\u6700\u9069\u5316\u554f\u984c\u306e\u591a\u304f\u306f\uff0c\u4f55\u3089\u304b\u306e\u610f\u5473\u3067\u52a3\u30e2\u30b8\u30e5\u30e9\u95a2\u6570\u306b\u95a2\u9023\u3057\u3066\u304a\u308a\uff0c\u85e4\u91cd\u6c0f\u306f\u6570\u7406\u8a08\u753b\u6cd5\u306b\u304a\u3051\u308b\u6700\u3082\u57fa\u790e\u7684\u306a\u90e8\u5206\u306e\u9032\u6b69\u306b\u8ca2\u732e\u3057\u305f\u3068\u8a00\u3048\u308b\uff0e
\n\u85e4\u91cd\u6c0f\u304c\u8457\u3057\u305f\u8ad6\u6587\u306f90\u7bc7\u3092\u8d8a\u3048\uff0c\u4e16\u754c\u7684\u306b\u6a29\u5a01\u306e\u3042\u308b\u5b66\u8853\u8a8c\u306b\u63b2\u8f09\u3055\u308c\u3066\u3044\u308b\uff0e\u8457\u66f8\u3082\u591a\u304f\uff0c\u305d\u308c\u3089\u3092\u901a\u3058\u3066\u96e2\u6563\u6700\u9069\u5316\u3084\u30de\u30c8\u30ed\u30a4\u30c9\u306e\u7406\u8ad6\u306b\u76ee\u3092\u958b\u304b\u308c\u305f\u82e5\u3044\u4eba\u3005\u306f\u591a\u3044\uff0e\u4eca\u65e5\u306e\u96e2\u6563\u6700\u9069\u5316\u306e\u5206\u91ce\u306b\u304a\u3044\u3066\uff0c\u4e16\u754c\u306b\u901a\u7528\u3059\u308b\u7814\u7a76\u8005\u304c\u65e5\u672c\u304b\u3089\u591a\u6570\u8f29\u51fa\u3055\u308c\u3066\u3044\u308b\u306e\u306f\uff0c\u85e4\u91cd\u6c0f\u306e\u8ca2\u732e\u306b\u3088\u308b\u3068\u3053\u308d\u304c\u5927\u304d\u3044\uff0e
\n\u672c\u5b66\u4f1a\u306b\u304a\u3044\u3066\u306f\uff0c\u5e73\u621020\u5e74\u5ea6\u304b\u30892\u5e74\u9593\uff0c\u7de8\u96c6\u62c5\u5f53\u7406\u4e8b\u30fb\u8ad6\u6587\u8a8c\u7de8\u96c6\u59d4\u54e1\u9577\u3092\u52e4\u3081\uff0c\u672c\u5b66\u4f1a\u8ad6\u6587\u8a8c\u306e\u5145\u5b9f\u306b\u5c3d\u529b\u3057\u305f\uff0e
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
3<\/td>\n2011<\/a><\/td>\n\u5bae\u6ca2\u653f\u6e05<\/td>\n\u6771\u4eac\u7406\u79d1\u5927\u5b66\u6559\u6388<\/td>\n
\n\u5bae\u6ca2\u6c0f\u306f\uff0c\u5f85\u3061\u884c\u5217\u30fb\u5fdc\u7528\u78ba\u7387\u8ad6\u306e\u5206\u91ce\u306b\u304a\u3051\u308b\u7406\u8ad6\u9762\u3092\u4e2d\u5fc3\u3068\u3057\u305f\u7814\u7a76\u3067\u591a\u304f\u306e\u5353\u8d8a\u3057\u305f\u884c\u653f\u3092\u6319\u3052\u3089\u308c\uff0c\u6d77\u5916\u306e\u7814\u7a76\u8005\u3068\u306e\u5171\u540c\u7814\u7a76\u306e\u6210\u679c\u3082\u591a\u304f\uff0c\u4e16\u754c\u3092\u30ea\u30fc\u30c9\u3059\u308b\u30c8\u30c3\u30d7\u30af\u30e9\u30b9\u306e\u7814\u7a76\u8005\u3068\u3057\u3066\u77e5\u3089\u308c\u3066\u3044\u308b\uff0e\u5bae\u6ca2\u6c0f\u304c\u8457\u3057\u305f\u82f1\u6587\u8ad6\u6587\u306f80\u7bc7\u3092\u6570\u3048\uff0c\u4e16\u754c\u7684\u306b\u6a29\u5a01\u306e\u3042\u308b\u5b66\u8853\u8a8c\u306b\u63b2\u8f09\u3055\u308c\u3066\u3044\u308b\uff0e
\n\u4e3b\u8981\u306a\u7814\u7a76\u30c6\u30fc\u30de\u306f\uff0c1980\u5e74\u4ee3\u524d\u534a\u304b\u308990\u5e74\u4ee3\u524d\u534a\u307e\u3067\u306f\uff0c\u70b9\u904e\u7a0b\u3092\u7528\u3044\u305f\u5f85\u3061\u884c\u5217\u306e\u30e2\u30c7\u30eb\u5316\u3068\u305d\u306e\u5fdc\u7528\uff0c1990\u5e74\u4ee3\u5f8c\u534a\u304b\u3089\u306f\uff0c\u5f85\u3061\u884c\u5217\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306b\u95a2\u3059\u308b\u7814\u7a76\uff0c2000\u5e74\u4ee3\u306b\u5165\u3063\u3066\u304b\u3089\u306f\uff0c\u975e\u7a4d\u5f62\u5f0f\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u30e2\u30c7\u30eb\u306b\u304a\u3051\u308b\u5b9a\u5e38\u5206\u5e03\u306e\u89e3\u6790\u3067\uff0c\u4ed6\u306e\u7814\u7a76\u8005\u306e\u591a\u304f\u306e\u7814\u7a76\u3092\u523a\u6fc0\u3057\u3066\u3044\u308b\uff0e
\n\u672c\u5b66\u4f1a\u300c\u5f85\u3061\u884c\u5217\u300d\u7814\u7a76\u90e8\u4f1a\u306730\u5e74\u9593\u306b\u308f\u305f\u308a\u7814\u7a76\u3092\u30ea\u30fc\u30c9\u3057\u3066\u3053\u3089\u308c\uff0c\u5185\u5916\u306e\u5b66\u8853\u96d1\u8a8c\u306e\u7de8\u96c6\u8005\u3068\u3057\u3066\u3082\u6d3b\u8e8d\u3055\u308c\u3066\u3044\u308b\uff0e
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
2<\/td>\n2009<\/a><\/td>\n\u5c0f\u5cf6\u653f\u548c<\/td>\n\u6771\u4eac\u5de5\u696d\u5927\u5b66\u6559\u6388<\/td>\n
\n\u5c0f\u5cf6\u6c0f\u306f\uff0c\u304a\u3088\u305d40\u5e74\u9593\u306b\u308f\u305f\u3063\u3066\uff0c\u6570\u7406\u8a08\u753b\u6cd5\u306e\u5206\u91ce\u3067\uff0c\u4e16\u754c\u3092\u30ea\u30fc\u30c9\u3059\u308b\u7814\u7a76\u6d3b\u52d5\u3092\u7d9a\u3051\u3066\u3053\u3089\u308c\u305f\uff0e\u7814\u7a76\u30c6\u30fc\u30de\u306f\uff0c\u521d\u671f\u306e\u76f8\u88dc\u6027\u554f\u984c\u306b\u59cb\u307e\u308a\uff0c\u4e0d\u52d5\u70b9\u554f\u984c\u306b\u5bfe\u3059\u308b\u30db\u30e2\u30c8\u30d4\u30fc\u6cd5\uff0c\u5185\u70b9\u6cd5\uff0c\u534a\u6b63\u5b9a\u5024\u8a08\u753b\u6cd5\uff0c\u5927\u57df\u7684\u6700\u9069\u5316\uff0c\u591a\u9805\u5f0f\u8a08\u753b\u554f\u984c\u306a\u3069\u591a\u5c90\u306b\u308f\u305f\u308b\u304c\uff0c\u7279\u7b46\u3059\u3079\u304d\u696d\u7e3e\u3068\u3057\u3066\u6b21\u306e3\u3064\u3092\u6319\u3052\u308b\u3053\u3068\u304c\u3067\u304d\u308b\uff0e
\n\u30fb\u4e0d\u52d5\u70b9\u306b\u304a\u3051\u308b\u72ed\u7fa9\u5b89\u5b9a\u6027\u306e\u6982\u5ff5\u306e\u975e\u7dda\u5f62\u8a08\u753b\u6cd5\u3078\u306e\u5c0e\u5165
\n\u30fb\u7dda\u5f62\u8a08\u753b\u554f\u984c\u306b\u304a\u3051\u308b\u53cc\u5bfe\u5185\u70b9\u6cd5\u306e\u8a2d\u8a08\u3068\u7dda\u5f62\u76f8\u88dc\u6027\u554f\u984c\u3078\u306e\u62e1\u5f35
\n\u30fb\u534a\u6b63\u5b9a\u5024\u8a08\u753b\u554f\u984c\u306b\u5bfe\u3059\u308b\u4e3b\u30fb\u53cc\u5bfe\u5185\u70b9\u6cd5\u306e\u8a2d\u8a08\u3068\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2SDPA\u306e\u958b\u767a
\n\u4ee5\u4e0a\u306e\u7814\u7a76\u696d\u7e3e\u306b\u52a0\u3048\u3066\uff0c\u5353\u8d8a\u3057\u305f\u7814\u7a76\u6307\u5c0e\u529b\u306b\u3088\u3063\u3066\uff0c\u591a\u304f\u306e\u512a\u308c\u305f\u7814\u7a76\u8005\u3092\u80b2\u3066\u305f\u529f\u7e3e\u3082\u7279\u7b46\u3059\u3079\u304d\u3082\u306e\u304c\u3042\u308b\uff0e
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
1<\/td>\n2007<\/a><\/td>\n\u8328\u6728\u4fca\u79c0<\/td>\n\u95a2\u897f\u5b66\u9662\u5927\u5b66\u6559\u6388\u30fb\u4eac\u90fd\u5927\u5b66\u540d\u8a89\u6559\u6388<\/td>\n
\n\u8328\u6728\u6c0f\u306f40\u5e74\u9593\u306b\u308f\u305f\u3063\u3066\uff0c\u7d44\u5408\u305b\u6700\u9069\u5316\uff0c\u96e2\u6563\u6570\u5b66\u306b\u95a2\u3059\u308b\u7814\u7a76\u3092\u4e2d\u5fc3\u306b\uff0cOR\u3068\u305d\u306e\u5468\u8fba\u5206\u91ce\u3067400\u7de8\u3092\u8d85\u3048\u308b\u5b66\u8853\u8ad6\u6587\u3092\u767a\u8868\u3055\u308c\uff0c\u65e5\u672c\u3092\u4ee3\u8868\u3059\u308b\u7814\u7a76\u8005\u3068\u3057\u3066\u4e16\u754c\u7684\u306b\u9ad8\u3044\u8a55\u4fa1\u3092\u7372\u5f97\u3055\u308c\u3066\u3044\u308b\uff0e
\n\u307e\u305f\uff0c\u4eac\u90fd\u5927\u5b66\uff0c\u8c4a\u6a4b\u6280\u8853\u79d1\u5b66\u5927\u5b66\uff0c\u95a2\u897f\u5b66\u9662\u5927\u5b66\u306b\u304a\u3044\u3066\u591a\u304f\u306e\u5f8c\u7d99\u8005\u3092\u80b2\u3066\u3089\u308c\u308b\u3068\u3068\u3082\u306b\uff0c20\u8a8c\u306b\u53ca\u3076\u56fd\u969b\u5b66\u8853\u96d1\u8a8c\u306e\u7de8\u96c6\u306b\u643a\u308f\u308a\uff0c\u30a4\u30ea\u30ce\u30a4\u5927\u5b66\uff0c\u30a6\u30a9\u30fc\u30bf\u30fc\u30eb\u30fc\u5927\u5b66\uff0c\u30b5\u30a4\u30e2\u30f3\u30fb\u30d5\u30ec\u30fc\u30b6\u30fc\u5927\u5b66\uff0c\u30e9\u30c8\u30ac\u30fc\u30b9\u5927\u5b66\u306a\u3069\u306e\u5ba2\u54e1\u6559\u6388\u3082\u52d9\u3081\u3066\u3044\u308b\uff0e
\n\u672c\u5b66\u4f1a\u306b\u304a\u3044\u3066\u3082\uff0c\u5404\u7a2e\u59d4\u54e1\uff0c\u7406\u4e8b\uff0c\u8a55\u8b70\u54e1\uff0c\u4ee3\u8b70\u54e1\uff0c\u526f\u4f1a\u9577\u306a\u3069\u3092\u6b74\u4efb\u3055\u308c\uff0c\u305d\u306e\u767a\u5c55\u306b\u591a\u5927\u306a\u8ca2\u732e\u3092\u3055\u308c\u3066\u3044\u308b\uff0e
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
\n
\u7814\u7a76\u8cde Research Award<\/h5>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u56de<\/th>\n\u5e74\u5ea6<\/th>\n\u53d7\u8cde\u8005<\/th>\n\u6240\u5c5e<\/th>\n\u7814\u7a76\u5185\u5bb9<\/th>\n<\/tr>\n<\/thead>\n
15<\/td>\n2025<\/td>\n\u5c0f\u91ce\u5ee3\u9686<\/td>\n\u540d\u53e4\u5c4b\u5927\u5b66<\/td>\n
\n[1] Tesshu Hanaka, Hirotaka Ono, Kosuke Sugiyama, “Solving Distance-constrained Labeling Problems for Small Diameter Graphs via TSP”, International Journal of Networking and Computing, Vol. 14 (1) (2024), pp. 26\u201339.
\n[2] Tesshu Hanaka, Hironori Kiya, Hirotaka Ono, Kanae Yoshiwatari, “Winner Determination Algorithms for Graph Games with Matching Structures”, Algorithmica, Vol. 86 (3) (2024), pp. 808\u2013824.
\n[3] Tesshu Hanaka, Hironori Kiya, Michael Lampis, Hirotaka Ono, Kanae Yoshiwatari, “Faster Winner Determination Algorithms for (Colored) Arc Kayles”, Proceedings of the International Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM 2024), pp. 297\u2013310.
\n[4] Takashi Horiyama, Yasuaki Kobayashi, Hirotaka Ono, Kazuhisa Seto, Ryu Suzuki, “Theoretical Aspects of Generating Instances with Unique Solutions: Pre-assignment Models for Unique Vertex Cover”, Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2024), pp. 20726\u201320734.
\n[5] Tatsuya Gima, Tesshu Hanaka, Yasuaki Kobayashi, Ryota Murai, Hirotaka Ono, Yota Otachi, “Structural parameterizations of vertex integrity”, Theoretical Computer Science, Vol. 1024 (2025), Article 114954.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
15<\/td>\n2025<\/td>\n\u5bd2\u91ce\u5584\u535a<\/td>\n\u6771\u4eac\u5927\u5b66<\/td>\n
\n[1] Y. Kanno, “Data-driven confidence bound for structural response using segmented least squares: a mixed-integer programming approach”, Japan Journal of Industrial and Applied Mathematics, 41, pp. 1501\u20131534 (2024).
\n[2] Y. Kanno, “Accelerated proximal gradient method for bi-modulus static elasticity”, Optimization and Engineering, 23, pp. 453\u2013477 (2022).
\n[3] W. Shimizu, Y. Kanno, “A note on accelerated proximal gradient method for elastoplastic analysis with Tresca yield criterion”, Journal of the Operations Research Society of Japan, 63, pp. 78\u201392 (2020).
\n[4] S. Fukasawa, Y. Kanno, “Numerical simulation of base-isolated buildings in collisions with surrounding moat walls during earthquakes: a nonsmooth mechanics approach”, Optimization and Engineering, 21, pp. 1423\u20131457 (2020).
\n[5] Y. Kanno, “On three concepts in robust design optimization: absolute robustness, relative robustness, and less variance”, Structural and Multidisciplinary Optimization, 62, pp. 979\u20131000 (2020).
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
14<\/td>\n2024<\/a><\/td>\n\u57a3\u6751\u5c1a\u5fb3<\/td>\n\u6176\u61c9\u7fa9\u587e\u5927\u5b66<\/td>\n
\n[1] C.-C. Huang and N. Kakimura, \u201cImproved streaming algorithms for maximizing monotone submodular functions under a knapsack constraint,\u201d Algorithmica, 83, pp. 879\u2013902, 2021.
\n[2] C.-C. Huang and N. Kakimura, \u201cMulti-pass streaming algorithms for monotone submodular function maximization,\u201d Theory of Computing Systems, 66, pp. 354\u2013394, 2022.
\n[3] C.-C. Huang, N. Kakimura, S. Mauras and Y. Yoshida, \u201cApproximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model,\u201d SIAM Journal on Discrete Mathematics, 36, pp. 355\u2013382, 2022.
\n[4] H. Sumita, S. Ito, K. Takemura, D. Hatano, T. Fukunaga, N. Kakimura and K. Kawarabayashi, \u201cOnline task assignment problems with reusable resources,\u201d In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI 2022), pp. 5199\u20135207, 2022.
\n[5] N. Kakimura and D. Zhu, \u201cDynamic bipartite matching market with arrivals and departures,\u201d In Proceedings of the 17th Conference on Web and Internet Economics (WINE 2021), p. 544, 2021.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
14<\/td>\n2024<\/a><\/td>\n\u798f\u7530\u30a8\u30ec\u30f3\u79c0\u7f8e<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\n
\n[1] E. H. Fukuda and B. F. Louren\u00e7o, \u201cExact augmented Lagrangian functions for nonlinear semidefinite programming,\u201d Computational Optimization and Applications, 71, pp. 457\u2013482, 2018.
\n[2] B. F. Louren\u00e7o, E. H. Fukuda and M. Fukushima, \u201cOptimality conditions for problems over symmetric cones and a simple augmented Lagrangian method,\u201d Mathematics of Operations Research, 43, pp. 1233\u20131251, 2018.
\n[3] R. Andreani, E. H. Fukuda, G. Haeser, D. O. Santos and L. D. Secchin, \u201cOptimality conditions for nonlinear second-order cone programming and symmetric cone programming,\u201d Journal of Optimization Theory and Applications, 200, pp. 1\u201333, 2024.
\n[4] E. H. Fukuda, L. M. Mito and G. Haeser, \u201cOn the weak second-order optimality condition for nonlinear semidefinite and second-order cone programming,\u201d Set-Valued and Variational Analysis, 31, No. 15, 2023.
\n[5] E. H. Fukuda, L. M. Gra\u00f1a Drummond and F. M. P. Raupp, \u201cA barrier-type method for multiobjective optimization,\u201d Optimization, 69, pp. 2471\u20132487, 2020.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
13<\/td>\n2023<\/a><\/td>\n\u5c0f\u6797\u4f51\u8f14<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\n
\n[1] Y. Kobayashi, \u201cWeighted triangle-free 2-matching problem with edge-disjoint forbidden triangles,\u201d Mathematical Programming, B, 192, pp. 675\u2014702, 2022.
\n[2] S. Iwata and Y. Kobayashi, \u201cA weighted linear matroid parity algorithm,\u201d SIAM Journal on Computing, 51, pp. 238\u2014280, 2022.
\n[3] K. Kawarabayashi and Y. Kobayashi, \u201cLinear min-max relation between the treewidth of an H-minor-free graph and its largest grid minor,\u201d Journal of Combinatorial Theory, B, 141, pp. 165\u2014180, 2020.
\n[4] Y. Kobayashi, \u201cNP-hardness and fixed-parameter tractability of the minimum spanner problem,\u201d Theoretical Computer Science, 746, pp. 88\u201497, 2018.
\n[5] K. Kawarabayashi and Y. Kobayashi, \u201cAll-or-nothing multicommodity flow problem with bounded fractionality in planar graphs,\u201d SIAM Journal on Computing, 47, pp. 1483\u20141504, 2018.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
13<\/td>\n2023<\/a><\/td>\n\u897f\u539f\u7406<\/td>\n\u5927\u962a\u5927\u5b66<\/td>\n
\n[1] M. Nishihara, \u201cTarget-initiated takeover with search frictions,\u201d European Journal of Operational Research, 305, pp. 1480\u20141497, 2023.
\n[2] M. Nishihara and T. Shibata, \u201cOptimal capital structure and simultaneous bankruptcy of firms in corporate networks,\u201d Journal of Economic Dynamics and Control, 133, 104264, 2021.
\n[3] M. Nishihara and T. Shibata, \u201cThe effects of asset liquidity on dynamic sell-out and bankruptcy decisions,\u201d European Journal of Operational Research, 288, pp. 1017\u20141035, 2021.
\n[4] M. Nishihara and T. Shibata, \u201cLiquidation, fire sales, and acquirers’ private information,\u201d Journal of Economic Dynamics and Control, 108, 103769, 2019.
\n[5] T. Shibata and M. Nishihara, \u201cInvestment timing, reversibility, and financing constraints,\u201d Journal of Corporate Finance, 48, pp. 771\u2014796, 2018.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
12<\/td>\n2022<\/a><\/td>\n\u8c37\u5ddd\u771e\u4e00<\/td>\n\u6771\u4eac\u5927\u5b66<\/td>\n
\n[1] R. Oba and S. Tanigawa, Characterizing the universal rigidity of generic tensegrities, Mathematical Programming, 197 (2023), 109-145.
\n[2] K. Clinch, B. Jackson and S. Tanigawa, Abstract 3-rigidity and bivariate C_2^1-splines II: combinatorial characterization, Discrete Analysis, 2022:3 (2022), 1\u201331.
\n[3] T. Jordan and S. Tanigawa, Global rigidity of triangulations with braces, Journal of Combinatorial Theory, Series B, 136 (2019), 249\u2013288.
\n[4] S. Fujishige and S. Tanigawa, Polynomial combinatorial algorithms for skew-bisubmodular function minimization, Mathematical Programming, 171 (2018), 87\u2013114.
\n[5] S. Tanigawa, Singularity degree of the positive semidefinite matrix completion problem, SIAM Journal on Optimization, 27 (2017), 986\u20131009.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
11<\/td>\n2021<\/a><\/td>\n\u6765\u5d8b\u79c0\u6cbb<\/td>\n\u4e5d\u5dde\u5927\u5b66<\/td>\n
\n[1] S. Kijima, N. Shimizu and T. Shiraga, “How many vertices does a random walk miss in a network with moderately increasing the number of vertices?,”
\n[2] T. Fujii and S. Kijima, “Every finite distributive lattice is isomorphic to the minimizer set of an M\u266e-concave set function,”
\n[3] E. Ando and S. Kijima, “An FPTAS for the volume of some V-polytopes — It is hard to compute the volume of the intersection of two cross-polytopes,”
\n[4] J. Nakashima, Y. Yamauchi, S. Kijima and M. Yamashita, “Finding submodularity hidden in symmetric difference,”
\n[5] S. Kijima, K. Koga and K. Makino, “Deterministic random walks on finite graphs, Random Structures & Algorithms,”
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
10<\/td>\n2020<\/a><\/td>\n\u5ca1\u672c\u5409\u592e<\/td>\n\u96fb\u6c17\u901a\u4fe1\u5927\u5b66<\/td>\n
\n\uff3b1\uff3d P. Carmi, M. K. Chiu, M. J. Katz, M. Korman, Y. Okamoto, A. van Renssen, M. Roeloffzen, T. Shiitada and S. Smorodinsky, \u201cBalanced line separators of unit disk graphs,\u201d
\n\uff3b2\uff3d T. Ito, N. Kakimura, N. Kamiyama, Y. Kobayashi and Y. Okamoto, \u201cMinimum-cost b-edge dominating sets on trees,\u201d
\n\uff3b3\uff3d T.\u00a0 Ito,\u00a0 N.\u00a0 Kakimura,\u00a0 N.\u00a0 Kamiyama,\u00a0 Y.\u00a0 Kobayashi\u00a0 and\u00a0 Y.\u00a0 Okamoto,\u00a0 \u201cEfficient\u00a0 stabilization\u00a0 of cooperative matching games,\u201d
\n\uff3b4\uff3d S. Iwata, N. Kamiyama, N. Katoh, S. Kijima and Y. Okamoto, \u201cExtended formulations for sparsity matroids,\u201d
\n\uff3b5\uff3d M. Cygan, H. Dell, D. Lokshtanov, D. Marx, J. Nederlof, Y. Okamoto, R. Paturi, S. Saurabh and M. Wahlstr\u00f6m, \u201cOn problems as hard as CNF-SAT,\u201d
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
9<\/td>\n2019<\/a><\/td>\n\u98ef\u585a\u79c0\u660e<\/td>\n\u660e\u6cbb\u5927\u5b66<\/td>\n
\n[1] Iiduka, H. (2018). Distributed Optimization for Network Resource Allocation with Nonsmooth Utility Functions.
\n[2] Iiduka, H. (2016). Convergence analysis of iterative methods for nonsmooth convex optimization over fixed point sets of quasi-nonexpansive mappings.
\n[3] Iiduka, H. (2016). Proximal point algorithms for nonsmooth convex optimization with fixed point constraints.
\n[4] Iiduka, H. (2015). Acceleration method for convex optimization over the fixed point set of a nonexpansive mapping.
\n[5] Iiduka, H., & Hishinuma, K. (2014). Acceleration method combining broadcast and incremental distributed optimization algorithms.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
9<\/td>\n2019<\/a><\/td>\n\u54c1\u91ce\u52c7\u6cbb<\/td>\nZuse Institute Berlin<\/td>\n
\n[1] T. Koch, T. Ralphs, Y. Shinano. Could we use a million cores to solve an integer program?
\n[2] Y. Shinano, T. Achterberg, T. Berthold, S. Heinz, T. Koch, M.Winkler. Solving hard MIPLIB2003 problems with ParaSCIP on supercomputers: An update.
\n[3] G. Gamrath, T. Koch, S. J. Maher, D. Rehfeldt, Y. Shinano. SCIP-Jack – A solver for STP and variants with parallelization extensions.
\n[4] Y. Shinano, T. Achterberg, T. Berthold, S. Heinz, T. Koch, M. Winkler: Solving open MIP instances with ParaSCIP on supercomputers using up to 80,000 cores.
\n[5] Y. Shinano, S. Heinz, S.Vigerske, M.Winkler. FiberSCIP – A shared memory parallelization of SCIP.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
8<\/td>\n2018<\/td>\n\u8a72\u5f53\u8005\u306a\u3057<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
7<\/td>\n2017<\/a><\/td>\n\u5897\u5c71\u535a\u4e4b<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\n
\n[1] H. Masuyama. \u201cTail asymptotics for cumulative processes sampled at heavy-tailed random times with applications to queueing models in Markovian environments,\u201d
\n[2] H. Masuyama. \u201cError bounds for augmented truncations of discrete-time block-monotone Markov chains under geometric drift conditions,\u201d
\n[3] H. Masuyama. \u201cA sufficient condition for the subexponential asymptotics of GI\/G\/1-type Markov chains with queueing applications,\u201d
\n[4] H. Masuyama. \u201cError bounds for augmented truncations of discrete-time block-monotone Markov chains under subgeometric drift conditions,\u201d
\n[5] H. Masuyama. \u201cContinuous-time block-monotone Markov chains and their block-augmented truncations,\u201d
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
7<\/td>\n2017<\/a><\/td>\n\u5f8c\u85e4\u9806\u54c9<\/td>\n\u4e2d\u592e\u5927\u5b66<\/td>\n
\n\uff3b1\uff3d J. Gotoh, and A. Takeda. \u201cMinimizing loss probability bounds for portfolio selection,\u201d
\n\uff3b2\uff3d J. Gotoh, K. Shinozaki, and A. Takeda. \u201cRobust portfolio techniques for mitigating the fragility of CVaR minimization and generalization to coherent risk measures,\u201d
\n\uff3b3\uff3d A. Takeda, M. Niranjan, J. Gotoh and Y. Kawahara. \u201cSimultaneous pursuit of out-of-sample performance and sparsity in index tracking portfolios,\u201d
\n\uff3b4\uff3d J. Gotoh, and S. Uryasev. \u201cTwo pairs of families of polyhedral norms versus lp-norms: proximity and applications in optimization,\u201d
\n\uff3b5\uff3d J. Gotoh, and S. Uryasev. \u201cSupport vector machines based on convex risk functions and general norms,\u201d
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
6<\/td>\n2016<\/a><\/td>\n\u6b66\u7530\u6717\u5b50<\/td>\n\u7d71\u8a08\u6570\u7406\u7814\u7a76\u6240<\/td>\n
\n[1] A. Takeda, H. Mitsugi and T. Kanamori: A unified classification model based on robust optimization,
\n[2] S. Okido and A. Takeda: Economic and environmental analysis of photovoltaic energy systems via robust optimization,
\n[3] A. Barbero, A. Takeda and J. Lopez: Geometric intuition and algorithms for E\u03bd-SVM
\n[4] D. Bertsimas and A. Takeda: Optimizing over coherent risk measures and non-convexities: a robust mixed integer optimization approach,
\n[5] S. Iwata, Y. Nakatsukasa and A. Takeda: Computing the signed distance between overlapping ellipsoids,
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
5<\/td>\n2015<\/td>\n\u8a72\u5f53\u8005\u306a\u3057<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
4<\/td>\n2014<\/a><\/td>\n\u5e73\u4e95\u5e83\u5fd7<\/td>\n\u6771\u4eac\u5927\u5b66<\/td>\n
\n[1] \u201cTree metrics and edge-disjoint S-paths\u201d,
\n[2] \u201cDiscrete Convexity and Polynomial Solvability in Minimum 0-Extension Problems\u201d,
\n[3] \u201cBounded fractionality of the multiflow feasibility problem for demand graph K3+K3 and related maximization problems\u201d,
\n[4] \u201cFolder complexes and multiflow combinatorial dualities\u201d,
\n[5] \u201cMetric packing for K3+K3\u201d,
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
3<\/td>\n2013<\/a><\/td>\n\u85e4\u6fa4\u514b\u6a39<\/td>\n\u4e2d\u592e\u5927\u5b66<\/td>\n
\n[1] High-Performance General Solver for Extremely Large-Scale Semidefinite Programming Problems,
\n[2] Convex Optimization Approaches to Maximally Predictable Portfolio Selection Optimization:
\n[3] The Second-order Reduced Density Matrix Method and the Two-dimensional Hubbard Model,
\n[4] NETAL: High-performance Implementation of Network Analysis Library Considering Computer Memory Hierarchy,
\n[5] A Performance Characteristics of Graph500 on Large-Scale Distributed Environment,
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
2<\/td>\n2012<\/a><\/td>\n\u5869\u6d66\u662d\u7fa9<\/td>\n\u6771\u5317\u5927\u5b66<\/td>\n
\n[1] M-convex Function Minimization by Continuous Relaxation Approach: Proximity Theorem and Algorithm. Satoko Moriguchi, Akiyoshi shioura, and Nobuyuki Tsuchimura
\n[2] Polynomial-Time Approximation Schemes for Maximizing Gross Substitutes Utility under Budget Constraints. Akiyoshi Shioura
\n[3] Neighbor Systems, Jump Systems, and Bisubmodular Polyhedra. Akiyoshi Shioura
\n[4] New Algorithms for Convex Cost Tension Problem with Application to Computer Vision. Vladimir Kolmogorov and Akiyoshi Shioura
\n[5] Polynomial-time Algorithms for Linear and Convex Optimization on Jump Systems. Akiyoshi Shioura and Ken’ichiro Tanaka
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
1<\/td>\n2011<\/a><\/td>\n\u677e\u4e95\u77e5\u5df1<\/td>\n\u4e2d\u592e\u5927\u5b66<\/td>\n
\n\uff11\uff0e\u5b89\u5b9a\u30de\u30c3\u30c1\u30f3\u30b0\u306e\u6226\u7565\u7684\u64cd\u4f5c\u53ef\u80fd\u6027\u306b\u95a2\u3059\u308b\u7814\u7a76;\u201cCheating Strategies for the Gale-Shapley Algorithm with Complete Preference Lists,\u201d
\n\u201cSuccessful Manipulation in Stable Marriage Model with Complete Preference Lists,\u201d
\n\uff12\uff0e\u30b0\u30e9\u30d5\u306e\u591a\u91cd\u5f69\u8272\u6570\u306b\u95a2\u3059\u308b\u7814\u7a76; \u201cPerfectness and Imperfectness of Unit Disk Graphs on Triangular Lattice Points,\u201d
\n\uff13\uff0e\u591a\u6b21\u5143\u5272\u308a\u5f53\u3066\u554f\u984c\u306e\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306b\u95a2\u3059\u308b\u7814\u7a76; \u201cAn Approximation Algorithm for Multidimensional Assignment Problems Minimizing the Sum of Squared Errors,\u201d
\n\uff14\uff0e\uff12\u884c\u5206\u5272\u8868\u3092\u751f\u6210\u3059\u308b\u30d1\u30fc\u30d5\u30a7\u30af\u30c8\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u6cd5\u306b\u95a2\u3059\u308b\u7814\u7a76\uff1b\u201cPolynomial Time Perfect Sampling Algorithm for Two-Rowed Contingency Tables,\u201d
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\u6587\u732e\u8cde Best Paper of the Year<\/h5>\n

\uff08\u7814\u7a76\u8cde\u306e\u524d\u8eab\uff09<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u56de<\/th>\n\u5e74\u5ea6<\/th>\n\u53d7\u8cde\u8005<\/th>\n\u6240\u5c5e<\/th>\n\u7814\u7a76\u5185\u5bb9<\/th>\n<\/tr>\n<\/thead>\n
38<\/td>\n2010<\/a><\/td>\n\u795e\u5c71\u76f4\u4e4b<\/td>\n\u4e2d\u592e\u5927\u5b66<\/td>\nArc-disjoint in-trees in directed graphs<\/td>\n<\/tr>\n
37<\/td>\n2009<\/a><\/td>\n\u5c71\u4e0b\u4fe1\u96c4<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\nISparse quasi-Newton updates with positive definite matrix completion<\/td>\n<\/tr>\n
36<\/td>\n2008<\/td>\n\u8a72\u5f53\u306a\u3057<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
35<\/td>\n2007<\/a><\/td>\n\u5409\u702c\u7ae0\u5b50<\/td>\n\u7b51\u6ce2\u5927\u5b66<\/td>\nInterior Point Trajectories and a Homogeneous Model for Nonlinear Complementarity Problems over Symmetric Cones<\/td>\n<\/tr>\n
34<\/td>\n2006<\/a><\/td>\n\u6c38\u6301\u4ec1<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\n1. A 4\/3-approximation for the minimum 2-local-vertex-connectivity augmentation in a connected graph
\n2. Graph algorithms for network connectivity problems<\/td>\n<\/tr>\n
33<\/td>\n2005<\/a><\/td>\n\u4e09\u597d\u76f4\u4eba<\/td>\n\u6771\u4eac\u5de5\u696d\u5927\u5b66<\/td>\nOn the subexponential properties in stationary single-server queues:A Palm-martingale approach<\/td>\n<\/tr>\n
33<\/td>\n2005<\/a><\/td>\n\u67f3\u6d66\u7766\u61b2<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\nAn Ejection Chain Approach for the Generalized Assignment Problem<\/td>\n<\/tr>\n
32<\/td>\n2004<\/a><\/td>\n\u7267\u91ce\u548c\u4e45<\/td>\n\u5927\u962a\u5927\u5b66<\/td>\nNew Results on Monotone Dualization and Generating Hypergrap Transversals<\/td>\n<\/tr>\n
31<\/td>\n2003<\/a><\/td>\n\u6751\u677e\u6b63\u548c<\/td>\n\u96fb\u6c17\u901a\u4fe1\u5927\u5b66<\/td>\nOn a Commutative Class of Search Directions for Linear Programming over Symmetric Cones<\/td>\n<\/tr>\n
30<\/td>\n2002<\/a><\/td>\n\u6787\u3005\u6728\u898f\u96c4<\/td>\n\u6176\u61c9\u7fa9\u587e\u5927\u5b66<\/td>\n\u6226\u7565\u7684\u8cc7\u7523\u914d\u5206\u554f\u984c\u306b\u5bfe\u3059\u308b\u591a\u671f\u9593\u78ba\u7387\u8a08\u753b\u30e2\u30c7\u30eb<\/td>\n<\/tr>\n
29<\/td>\n2001<\/a><\/td>\n\u5ca9\u7530\u899a<\/td>\n\u6771\u4eac\u5927\u5b66<\/td>\nA Combinatorial, Strongly Polynomial-Time Algorithm for Minimizing Submodular Functions<\/td>\n<\/tr>\n
29<\/td>\n2001<\/a><\/td>\n\u5927\u6fa4\u7fa9\u660e<\/td>\n\u7b51\u6ce2\u5927\u5b66<\/td>\nBicriteria Euclidean Location Associated with Maximin and Minimax Criteria<\/td>\n<\/tr>\n
28<\/td>\n2000<\/a><\/td>\n\u95a2\u8c37\u548c\u4e4b<\/td>\n\u9759\u5ca1\u5927\u5b66<\/td>\nA Logical Interpretation for the Eigenvalue Method in AHP<\/td>\n<\/tr>\n
27<\/td>\n1999<\/a><\/td>\n\u4e45\u91ce\u8a89\u4eba<\/td>\n\u7b51\u6ce2\u5927\u5b66<\/td>\nA Finite Algorithm for Globally Optimizing a Class of Rank-Two Reverse Convex Programs<\/td>\n<\/tr>\n
26<\/td>\n1998<\/a><\/td>\n\u658e\u85e4\u6d0b<\/td>\n\uff2e\uff34\uff34\u30de\u30eb\u30c1\u30e1\u30c7\u30a3\u30a2\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u7814\u7a76\u6240<\/td>\n\u30d9\u30a4\u30ba\u6027\u80fd\u63a8\u5b9a\u6cd5\u306e\uff36\uff30\u5bb9\u91cf\u5236\u5fa1\u3078\u306e\u9069\u7528\u65b9\u6cd5<\/td>\n<\/tr>\n
26<\/td>\n1998<\/a><\/td>\n\u7530\u6751\u660e\u4e45<\/td>\n\u96fb\u6c17\u901a\u4fe1\u5927\u5b66<\/td>\nThe Generalized Stable Set Problem for Perfect Bidirected Graphs<\/td>\n<\/tr>\n
25<\/td>\n1997<\/a><\/td>\n\u6edd\u6839\u54f2\u54c9<\/td>\n\u5927\u962a\u5927\u5b66<\/td>\nA Nonpreemptive Priority MAP\/G\/1 Queue with Two Classes of Customers<\/td>\n<\/tr>\n
25<\/td>\n1997<\/a><\/td>\n\u571f\u8c37\u9686<\/td>\n\u7d71\u8a08\u6570\u7406\u7814\u7a76\u6240<\/td>\nSuperlinear convergence of the affine scaling algorithm<\/td>\n<\/tr>\n
24<\/td>\n1996<\/td>\n\u8a72\u5f53\u306a\u3057<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
23<\/td>\n1995<\/a><\/td>\n\u9ad8\u6a4b\u656c\u9686<\/td>\n\uff2e\uff34\uff34\u901a\u4fe1\u7db2\u7814\u7a76\u6240<\/td>\nRelationship between Queue-Length and Waiting Time Distributions in a Priority Queue with Batch Arrivals<\/td>\n<\/tr>\n
22<\/td>\n1994<\/a><\/td>\n\u5ba4\u7530\u4e00\u96c4<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\nHierarchical Decomposition of Symmetric Discrete Systems by Matroid and Group Theories<\/td>\n<\/tr>\n
21<\/td>\n1993<\/a><\/td>\n\u798f\u7530\u516c\u660e<\/td>\n\u7b51\u6ce2\u5927\u5b66<\/td>\nLinear Complementarity and Oriented Matroid<\/td>\n<\/tr>\n
20<\/td>\n1992<\/a><\/td>\n\u5ddd\u5d0e\u82f1\u6587<\/td>\n\u4e5d\u5dde\u5927\u5b66<\/td>\nSecond Order Necessary Optimality Conditions for Minimizing a Sup-type Function<\/td>\n<\/tr>\n
20<\/td>\n1992<\/a><\/td>\n\u6728\u6751\u4fca\u4e00<\/td>\n\u5317\u6d77\u9053\u5927\u5b66<\/td>\nApproximations for the Waiting Time in the GI\/G\/s Queue<\/td>\n<\/tr>\n
19<\/td>\n1991<\/a><\/td>\n\u6c34\u91ce\u771f\u6cbb<\/td>\n\u7d71\u8a08\u6570\u7406\u7814\u7a76\u6240<\/td>\nAn 0(n3L) Algorithm Using a Sequence for Linear Complementarity Problem<\/td>\n<\/tr>\n
18<\/td>\n1990<\/a><\/td>\n\u6728\u5cf6\u6b63\u660e<\/td>\n\u7b51\u6ce2\u5927\u5b66<\/td>\n1. Upper Bounds of a Measure of Dependence and the Relaxation Time for Finite\u3000State Markov Chains<\/td>\n<\/tr>\n
18<\/td>\n1990<\/a><\/td>\n\u52a0\u85e4\u76f4\u6a39<\/td>\n\u795e\u6238\u5546\u79d1\u5927\u5b66<\/td>\nAn Efficient Algorithm for Bicriteria Minimum-Cost\u3000Circulation Problem<\/td>\n<\/tr>\n
17<\/td>\n1989<\/td>\n\u8a72\u5f53\u306a\u3057<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
16<\/td>\n1988<\/a><\/td>\n\u4eca\u4e95\u6d69<\/td>\n\u4e5d\u5dde\u5927\u5b66<\/td>\nExtensions of the Multiplicative Penalty Function Method for Linear Programming<\/td>\n<\/tr>\n
16<\/td>\n1988<\/a><\/td>\n\u5c71\u672c\u82b3\u55e3<\/td>\n\u7b51\u6ce2\u5927\u5b66<\/td>\nA Path Following Algorithm for Stationary Point Problems<\/td>\n<\/tr>\n
15<\/td>\n1987<\/a><\/td>\n\u77f3\u4e95\u535a\u662d<\/td>\n\u5927\u962a\u5927\u5b66<\/td>\nTwo Machine Open Shop Scheduling Problems with Controllable Machine Speeds<\/td>\n<\/tr>\n
14<\/td>\n1986<\/a><\/td>\n\u5ddd\u5cf6\u5e78\u4e4b\u52a9<\/td>\n\u96fb\u96fb\u516c\u793e\u6b66\u8535\u91ce\u96fb\u6c17\u901a\u4fe1\u7814\u7a76\u6240<\/td>\nAn Approximation of a Loss System with Two Heterogeneous Types of Calls<\/td>\n<\/tr>\n
13<\/td>\n1985<\/a><\/td>\n\u798f\u5cf6\u96c5\u592b<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\nA Nonsmooth Optimization Approach to Nonlinear Multicommodity Network Flow Problems<\/td>\n<\/tr>\n
12<\/td>\n1984<\/a><\/td>\n\u6cb3\u5408\u4e00<\/td>\n\u5927\u962a\u5e9c\u7acb\u5927\u5b66<\/td>\nAn Optimal Ordering and Replacement Policy of a Markovian Deterioration System Under Incomplete Observation Part\u2161<\/td>\n<\/tr>\n
11<\/td>\n1983<\/td>\n\u8a72\u5f53\u306a\u3057<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
10<\/td>\n1982<\/a><\/td>\n\u7530\u8fba\u570b\u58eb<\/td>\n\u7d71\u8a08\u6570\u7406\u7814\u7a76\u6240<\/td>\n1. Feasibility-improving gradient acute projection method ; A unified approach to nonlinear programming
\n2. A geometric method in nonlinear Programming<\/td>\n<\/tr>\n
9<\/td>\n1981<\/td>\n\u4eca\u91ce\u6d69<\/td>\n\u7b51\u6ce2\u5927\u5b66<\/td>\nMaximizing a Convex Quadratic Function over a Hypercube<\/td>\n<\/tr>\n
8<\/td>\n1980<\/td>\n\u8a72\u5f53\u306a\u3057<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
7<\/td>\n1979<\/td>\n\u5c0f\u5cf6\u653f\u548c<\/td>\n\u6771\u4eac\u5de5\u696d\u5927\u5b66<\/td>\nOn the Homotopic Approach to Systems of Equations with Separable Mapping<\/td>\n<\/tr>\n
7<\/td>\n1979<\/td>\n\u7530\u53e3\u6771<\/td>\n\u6771\u4eac\u5927\u5b66<\/td>\nMaximum-Flow Problem in Discrete-Continuous Compound Systems and its Numerical Approach<\/td>\n<\/tr>\n
6<\/td>\n1978<\/td>\n\u5ca9\u672c\u8aa0\u4e00<\/td>\n\u4e5d\u5dde\u5927\u5b66<\/td>\nA Class of Inverse Theorems on Recursive Programming with Monotonicity<\/td>\n<\/tr>\n
5<\/td>\n1977<\/td>\n\u8a72\u5f53\u306a\u3057<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
4<\/td>\n1976<\/td>\n\u68ee\u96c5\u592b<\/td>\n\u8328\u57ce\u5927\u5b66<\/td>\nSome Bounds for Queues<\/td>\n<\/tr>\n
3<\/td>\n1975<\/td>\n\u9ad8\u6a4b\u5e78\u96c4<\/td>\n\u6771\u5317\u5927\u5b66<\/td>\nA Sequencing Model with an Application to Speed Class Sequencing in Air Traffic Control<\/td>\n<\/tr>\n
2<\/td>\n1974<\/td>\n\u8a72\u5f53\u306a\u3057<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
1<\/td>\n1973<\/td>\n\u8328\u6728\u4fca\u79c0<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\n\u76f8\u88dc\u7684\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\u5927\u897f\u8cde<\/h5>\n

\uff08\u6587\u732e\u8cde\u306e\u524d\u8eab\uff09<\/p>\n\n\n\n\n\n\n\n\n\n
\u56de<\/th>\n\u5e74\u5ea6<\/th>\n\u53d7\u8cde\u8005<\/th>\n\u6240\u5c5e<\/th>\n\u7814\u7a76\u5185\u5bb9<\/th>\n<\/tr>\n<\/thead>\n
5<\/td>\n1972<\/td>\n\u95a2\u53e3\u5149\u6674<\/td>\n\u4e09\u548c\u9280\u884c<\/td>\nModels of the Human Forecasting Behavior<\/td>\n<\/tr>\n
4<\/td>\n1971<\/td>\n\u5c3e\u5d0e\u4fca\u6cbb<\/td>\n\u5e83\u5cf6\u5927\u5b66<\/td>\nSystem Reliability by Markov Renewal Processes<\/td>\n<\/tr>\n
3<\/td>\n1970<\/td>\n\u9752\u5c71\u5409\u9686<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\n\u90fd\u5e02\u570f\u306b\u304a\u3051\u308b\u30de\u30b9\u30fb\u30c8\u30e9\u30f3\u30b9\u30dd\uff0d\u30c6\u30a4\u30b7\u30e7\u30f3\u306e\u6700\u9069\u8a08\u753b<\/td>\n<\/tr>\n
2<\/td>\n1969<\/td>\n\u963f\u90e8\u4fca\u4e00<\/td>\n\u9244\u9053\u6280\u7814<\/td>\nMulti-Stage Rearrangement Problem and its Applications to Multiple -System Reliability<\/td>\n<\/tr>\n
1<\/td>\n1968<\/td>\n\u67f3\u4e95\u6d69<\/td>\n\u6176\u61c9\u7fa9\u587e\u5927\u5b66<\/td>\nOn a Class of Optimal Stopping Rule Problem<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
\n
\u7814\u7a76\u8cde\u5968\u52b1\u8cde Research Encourage Award<\/h5>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u56de<\/th>\n\u5e74\u5ea6<\/th>\n\u53d7\u8cde\u8005<\/th>\n\u6240\u5c5e<\/th>\n\u7814\u7a76\u5185\u5bb9<\/th>\n<\/tr>\n<\/thead>\n
15<\/td>\n2025<\/td>\n\u99ac\u539f\u51cc\u6cb3<\/td>\n\u6771\u4eac\u5927\u5b66<\/td>\n
\n[1] Mahara, “Extension of Additive Valuations to General Valuations on the Existence of EFX”, Mathematics of Operations Research, 49 (2023), 1263-1277.
\n[2] Y. Kobayashi, R. Mahara, and S. Sakamoto, “EFX Allocations for Indivisible Chores: Matching-Based Approach”, Theoretical Computer Science, 1026 (2025), 115010.
\n[3] Y. Kobayashi and R. Mahara, “Proportional Allocation of Indivisible Goods up to the Least Valued Good on Average”, SIAM Journal on Discrete Mathematics, 39 (2025)\uff0c533\u2014549.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
15<\/td>\n2025<\/td>\n\u4e38\u8302\u76f4\u8cb4<\/td>\n\u6771\u4eac\u5927\u5b66<\/td>\n
\n[1] N. Marumo and A. Takeda, “Parameter-free accelerated gradient descent for nonconvex minimization”, SIAM Journal on Optimization, 34(2):2093\u20132120, 2024.
\n[2] N. Marumo and A. Takeda, “Universal heavy-ball method for nonconvex optimization under H\u00f6lder continuous Hessians”, Mathematical Programming, 2024.
\n[3] N. Marumo, T. Okuno, and A. Takeda, “Accelerated-gradient-based generalized Levenberg\u2013Marquardt method with oracle complexity bound and local quadratic convergence”, Mathematical Programming, 2024.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
14<\/td>\n2024<\/a><\/td>\n\u4e2d\u5c71\u821c\u6c11<\/td>\n\u96fb\u6c17\u901a\u4fe1\u5927\u5b66<\/td>\n
\n[1] S. Nakayama and J. Gotoh, \u201cOn the superiority of PGMs to PDCAs in nonsmooth nonconvex sparse regression,\u201d Optimization Letters, 15, pp. 2831\u20132860, 2021.
\n[2] S. Nakayama, Y. Narushima and H. Yabe, \u201cInexact proximal DC Newton-type method for nonconvex composite functions,\u201d Computational Optimization and Applications, 87, pp. 611\u2013640, 2024.
\n[3] Y. Narushima, S. Nakayama, M. Takemura and H. Yabe, \u201cMemoryless quasi-Newton methods based on the spectral-scaling Broyden family for Riemannian optimization,\u201d Journal of Optimization Theory and Applications, 197, pp. 639\u2013664, 2023.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
14<\/td>\n2024<\/a><\/td>\n\u5c71\u5ddd\u96c4\u4e5f<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\n
\n[1] Y. Yamakawa and T. Okuno, \u201cA stabilized sequential quadratic semidefinite programming method for nonlinear semidefinite programming problems,\u201d Computational Optimization and Applications, 83, pp. 1027\u20131064, 2022.
\n[2] Y. Yamakawa, \u201cA stabilized sequential quadratic programming method for optimization problems in function spaces,\u201d Numerical Functional Analysis and Optimization, 44, pp. 867\u2013905, 2023.
\n[3] Y. Yamakawa, T. Ikegami, E. H. Fukuda and N. Yamashita, \u201cAn equivalent nonlinear optimization model with triangular low-rank factorization for semidefinite programs,\u201d Optimization Methods and Software, 38, pp. 1296\u20131310, 2023.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
13<\/td>\n2023<\/a><\/td>\n\u5c0f\u6797\u5065<\/td>\n\u6771\u4eac\u5de5\u696d\u5927\u5b66<\/td>\n
\n[1] K. Kobayashi and Y. Takano, \u201cA branch-and-cut algorithm for solving mixed-integer semidefinite optimization problems,\u201d Computational Optimization and Applications, 75, pp. 493\u2014513, 2020.
\n[2] K. Kobayashi, Y. Takano and K. Nakata, \u201cBilevel cutting-plane algorithm for solving cardinality-constrained mean-CVaR portfolio optimization,\u201d Journal of Global Optimization, 81, pp. 493\u2014528, 2021.
\n[3] K. Kobayashi, Y. Takano and K. Nakata, \u201cCardinality-constrained distributionally robust portfolio optimization,\u201d European Journal of Operational Research, 309, pp. 1173\u20141182, 2023.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
13<\/td>\n2023<\/a><\/td>\n\u912d\u4fca\u4fca<\/td>\n\u5927\u962a\u5927\u5b66<\/td>\n
\n[1] J. Zheng, H. Okamura and T. Dohi, \u201cSensitivity analysis for a Markov regenerative software rejuvenation model,\u201d Stochastic Models, 2022 (latest articles).
\n[2] J. Zheng, H. Okamura and T. Dohi, \u201cAge replacement with Markovian opportunity process,\u201d Reliability Engineering & System Safety, 216, 107949, 2021.
\n[3] J. Zheng, H. Okamura, T. Dohi and K. S. Trivedi, \u201cQuantitative security evaluation of intrusion tolerant systems with Markovian arrivals,\u201d IEEE Transactions on Reliability, 70, pp. 547\u2014562, 2021.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
13<\/td>\n2023<\/a><\/td>\nLiu Tianxiang<\/td>\n\u6771\u4eac\u5de5\u696d\u5927\u5b66<\/td>\n
\n[1] T. Liu, I. Markovsky, T. K. Pong and A. Takeda, \u201cA hybrid penalty method for a class of optimization problems with multiple rank constraints,\u201d SIAM Journal on Matrix Analysis, 41, pp. 1260\u20141283, 2020.
\n[2] T. Liu and A. Takeda, \u201cAn inexact successive quadratic approximation method for a class of difference-of-convex optimization problems,\u201d Computational Optimization and Applications, 82, pp. 141\u2014173, 2022.
\n[3] T. Liu and B. F. Louren\u00e7o, \u201cConvergence analysis under consistent error bounds,\u201d Foundations of Computational Mathematics, 2022 (latest articles).
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
12<\/td>\n2022<\/a><\/td>\n\u4e94\u5341\u5d50\u6b69\u7f8e<\/td>\n\u56fd\u7acb\u60c5\u5831\u5b66\u7814\u7a76\u6240<\/td>\n
\n[1] M. Chakraborty, A. Igarashi, W. Suksompong and Y. Zick, Weighted envy-freeness in indivisible item allocation, ACM Transactions on Economics and Computation, 9 (2021), pages 1\u201339.
\n[2] X. Bei, A. Igarashi, X. Lu, and W. Suksompong, The price of connectivity in fair division, SIAM Journal on Discrete Mathematics, 36 (2022), pages 1156\u20131186.
\n[3] H. Goko, A. Igarashi, Y. Kawase, K. Makino, H. Sumita, A. Tamura, Y. Yokoi and M. Yokoo, Fair and truthful allocation with limited subsidy, Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, IFAAMAS, 2022, pages 534\u2013542.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
12<\/td>\n2022<\/a><\/td>\n\u5ca9\u653f\u52c7\u4ec1<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\n
\n[1] H. Hirai, Y. Iwamasa, K. Murota and S. \u017divn\u00fd, A tractable class of binary VCSPs via M-convex intersection, ACM Transactions on Algorithms, 15 (2019), 44:1\u201344:41.
\n[2] H. Hirai and Y. Iwamasa, A combinatorial algorithm for computing the rank of a generic partitioned matrix with 2\u00d72 submatrices, Mathematical Programming, Series A, \u63b2\u8f09\u6c7a\u5b9a\u6e08\u307f.
\n[3] Y. Iwamasa, A combinatorial algorithm for computing the degree of the determinant of a generic partitioned polynomial matrix with 2\u00d72 submatrices, Proceedings of the
\n22nd Conference on Integer Programming and Combinatorial Optimization, LNCS 12707, 119\u2013133, 2021.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
12<\/td>\n2022<\/a><\/td>\n\u7530\u4e2d\u672a\u6765<\/td>\n\u7d71\u8a08\u6570\u7406\u7814\u7a76\u6240<\/td>\n
\n[1] M. Tanaka and T. Okuno, Extension of the LP-Newton method to conic programming problems via semi-infinite representation, Numerical Algorithms, 86 (2021), 1285\u20131302.
\n[2] R. Sato, M. Tanaka and A. Takeda, A gradient method for multilevel optimization, Advances in Neural Information Processing Systems, 34 (2021), 7522\u20137533.
\n[3] M. Toyoda and M. Tanaka, Fast iterative method for SOAV minimization problem with linear equality and box constraints and its linear convergence, Journal of The Franklin Institute, 359 (2022), 2206\u20132228.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
12<\/td>\n2022<\/a><\/td>\nJeon Haejun<\/td>\n\u6771\u4eac\u7406\u79d1\u5927\u5b66<\/td>\n
\n[1] H. Jeon, Investment and financing decisions in the presence of time-to-build, European Journal of Operational Research, 288 (2021), 1068\u20131084.
\n[2] H. Jeon, Investment timing and capacity decisions with time-to-build in a duopoly market, Journal of Economic Dynamics and Control, 122 (2021), 104028.
\n[3] H. Jeon, Licensing and information disclosure under asymmetric information, European Journal of Operational Research, 276 (2019), 314\u2013330.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
11<\/td>\n2021<\/a><\/td>\n\u4f0a\u85e4\u52dd<\/td>\n\u65e5\u672c\u5927\u5b66<\/td>\n
\n[1] M. Ito and M. Fukuda, Nearly optimal first-order methods for convex optimization under gradient norm measure: An adaptive regularization approach, Journal of Optimization Theory and Applications, 188 (2021), 770-804.
\n[2] M. Ito and B. F. Louren\u00e7o, The automorphism group and the non-self-duality of p-cones, Journal of Mathematical Analysis and Applications, 471 (2019), 392-410.
\n[3] M. Ito and B. F. Louren\u00e7o, A bound on the Carath\u00e9odory number, Linear Algebra and its Applications, 532 (2017), 347-363.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
11<\/td>\n2021<\/a><\/td>\n\u6a2a\u4e95\u512a<\/td>\n\u56fd\u7acb\u60c5\u5831\u5b66\u7814\u7a76\u6240<\/td>\n
\n[1] Y. Yokoi, Envy-free matchings with lower quotas, Algorithmica, 82 (2020), 188-211.
\n[2] S. Iwata and Y. Yokoi, Finding a stable allocation in polymatroid intersection, Mathematics of Operations Research, 45 (2020), 63-85.
\n[3] Y. Yokoi, Matroidal choice functions, SIAM Journal on Discrete Mathematics, 33 (2019), 1712-1724.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
10<\/td>\n2020<\/a><\/td>\n\u6771\u5ddd\u96c4\u54c9<\/td>\n\u5175\u5eab\u770c\u7acb\u5927\u5b66<\/td>\n
\n\uff3b1\uff3d R. Benkoczi, B. Bhattacharya, Y. Higashikawa, T. Kameda and N. Katoh, \u201cMinsum k-Sink Problem on Path Networks, \u201d Theoretical Computer Science, 806, pp. 388\u2013401, 2020.
\n\uff3b2\uff3d B. Bhattacharya, Y. Higashikawa, T. Kameda and N. Katoh, \u201cAn O\uff08n2 log2 n\uff09Time Algorithm for Minmax Regret Minsum Sink on Path Networks,\u201d In 29th International Symposium on Algorithms and Computation\uff08ISAAC 2018\uff09\uff0cSchloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2018.
\n\uff3b3\uff3d Y.\u00a0 Hanawa,\u00a0 Y.\u00a0 Higashikawa,\u00a0 N.\u00a0 Kamiyama,\u00a0 N.\u00a0 Katoh\u00a0 and\u00a0 A.\u00a0 Takizawa,\u00a0 \u201cThe\u00a0 Mixed\u00a0 Evacuation Problem,\u201d Journal of Combinatorial Optimization, 36, pp. 1299\u20131314, 2018.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
10<\/td>\n2020<\/a><\/td>\n\u9b8f\u5ddd\u77e9\u7fa9<\/td>\n\u6771\u4eac\u7406\u79d1\u5927\u5b66<\/td>\n
\n\uff3b1\uff3d N. Sukegawa, \u201cAn asymptotically improved upper bound on the diameter of polyhedra,\u201d Discrete & Computational Geometry, 62, pp. 690\u2013699, 2019.
\n\uff3b2\uff3d T. Kitahara and N. Sukegawa, \u201cA simple projection algorithm for linear programming problems,\u201d Algorithmica, 81, pp. 167\u2013178, 2019.
\n\uff3b3\uff3d N. Nishimura, N. Sukegawa, Y. Takano and J. Iwanaga, \u201cA latent-class model for estimating product- choice probabilities from clickstream data,\u201d Information Sciences, 429, pp. 406\u2013420, 2018.
\n<\/div>
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\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
10<\/td>\n2020<\/a><\/td>\n\u4e95\u4e0a\u6587\u5f70<\/td>\n\u5927\u962a\u5927\u5b66<\/td>\n
\n\uff3b1\uff3d Y. Inoue, O. Boxma, D. Perry and S. Zacks, \u201cAnalysis of Mx\/G\/1 queues with impatient customers,\u201d Queueing Systems, 89, pp. 303\u2013350, 2018.
\n\uff3b2\uff3d Y. Inoue, \u201cComparison results for M\/G\/1 queues with waiting and sojourn time deadlines,\u201d Journal of Applied Probability, 56, pp. 524\u2013532, 2019.
\n\uff3b3\uff3d Y. Inoue, H. Masuyama, T. Takine and T. Tanaka, \u201cA general formula for the stationary distribution of the age of information and its application to single-server queues,\u201d IEEE Transactions on Information Theory, 65, pp. 8305\u20138324, 2019.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
10<\/td>\n2020<\/a><\/td>\n\u5bae\u5185\u6566\u53f2<\/td>\n\u6771\u4eac\u5927\u5b66<\/td>\n
\n\uff3b1\uff3d A. Miyauchi and A. Takeda, \u201cRobust densest subgraph discovery,\u201d In Proceedings of the 18th IEEE International Conference on Data Mining\uff08ICDM 2018\uff09, pp. 1188\u20131193, 2018.
\n\uff3b2\uff3d A. Miyauchi and N. Kakimura, \u201cFinding a dense subgraph with sparse cut,\u201d In Proceedings of the 27th ACM International Conference on Information and Knowledge Management\uff08CIKM 2018\uff09, pp. 547\u2013 556, 2018.
\n\uff3b3\uff3d A. Miyauchi, T. Sonobe and N. Sukegawa, \u201cExact clustering via integer programming and maximum satisfiability,\u201d In Proceedings of the 32nd AAAI Conference on Artificial Intelligence\uff08AAAI 2018\uff09, pp. 1387\u20131394, 2018.
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\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
9<\/td>\n2019<\/a><\/td>\n\u4f50\u85e4\u5bdb\u4e4b<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\n
\n[1] Sato, H. (2016). A Dai-Yuan-type Riemannian conjugate gradient method with the weak Wolfe conditions. Computational Optimization and Applications, 64(1), 101-118.
\n[2] Sato, H. (2017). Riemannian Newton-type methods for joint diagonalization on the Stiefel manifold with application to independent component analysis. Optimization, 66(12), 2211-2231.
\n[3] Sato, H., & Aihara, K. (2019). Cholesky QR-based retraction on the generalized Stiefel manifold. Computational Optimization and Applications, 72(2), 293-308.
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\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
9<\/td>\n2019<\/a><\/td>\n\u767d\u9aea\u4e08\u6674<\/td>\n\u4e2d\u592e\u5927\u5b66<\/td>\n
\n[1] Colin Cooper, Tomasz Radzik, Nicolas Rivera, Takeharu Shiraga, “Fast plurality consensus in regular expanders, ” in Proceedings of the 31st International Symposium on Distributed Computing (DISC 2017), 13:1-13:16\uff0c2017.
\n[2] Colin Cooper, Andrew McDowell, Tomasz Radzik, Nicolas Rivera, Takeharu Shiraga, “Dispersion processes, ” Random Structures and Algorithms, 53(4), 561-585, 2018.
\n[3] Takeharu Shiraga, Yukiko Yamauchi, Shuji Kijima, Masafumi Yamashita, “Deterministic random walks for rapidly mixing chains, ” SIAM Journal on Discrete Mathematics, 32(3), 2180-2193, 2018.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
9<\/td>\n2019<\/a><\/td>\nBruno Figueira Lourenco<\/td>\n\u6771\u4eac\u5927\u5b66<\/td>\n
\n[1] Bruno F. Lourenco, Masakazu Muramatsu, and Takashi Tsuchiya. Facial Reduction and Partial Polyhedrality, SIAM Journal on Optimization, 28 (2018), 2304-2326.
\n[2] Bruno F. Lourenco, Tomonari Kitahara, Masakazu Muramatsu, and Takashi Tsuchiya. An extension of Chubanov’s algorithm to symmetric cones, Mathematical Programming, 173 (2019), 117-149.
\n[3] Bruno F. Lourenco, Ellen H. Fukuda, and Masao Fukushima. Optimality Conditions for Nonlinear Semidefinite Programming via Squared Slack Variables, Mathematical Programming, 168 (2018), 177-200.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
8<\/td>\n2018<\/a><\/td>\n\u9ad8\u677e\u745e\u4ee3<\/td>\n\u4e2d\u592e\u5927\u5b66<\/td>\n
\n\uff3b1\uff3d S. Iwata and M. Takamatsu. \u201cOn the Kronecker canonical form of singular mixed matrix pencils,\u201d SIAM Journal on Control and Optimization, vol. 55, pp. 2134\u20132150, 2017.
\n\uff3b2\uff3d T. Yamauchi, M. Takamatsu and S. Imahori. \u201cOptimizing train stopping patterns for congestion management,\u201d Proceedings of the 17th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems \uff08ATMOS\uff09, no. 13 \uff0815 pages\uff09, 2017.
\n\uff3b3\uff3d M. Takamatsu and A. Taguchi. \u201cTrain and bus timetable design to ensure smooth transfer in areas with low-frequency public transportation services,\u201d Proceedings of the 6th International Conference on Railway Operations Modelling and Analysis, no. 34 \uff0820 pages\uff09, 2015.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
7<\/td>\n2017<\/a><\/td>\n\u6cb3\u702c\u5eb7\u5fd7<\/td>\n\u6771\u4eac\u5de5\u696d\u5927\u5b66<\/td>\n
\n\uff3b1\uff3d\u3000Y. Kawase, K. Makino, and K. Seimi, \u201cOptimal composition ordering problems for piecewise linear functions,\u201d The 27th International Symposium on Algorithms and Computation \uff08ISAAC2016\uff09, LIPICS64\uff08 2016\uff09 42:1\u201342:13.
\n\uff3b2\uff3d\u3000X. Han, Y. Kawase, and K. Makino, \u201cRandomized algorithms for online knapsack problems,\u201d Theoretical Computer Science, 562\uff082015\uff09, pp. 395\u2013405.
\n\uff3b3\uff3d\u3000Y. Kawase. \u201cThe secretary problem with a choice function,\u201d The 26th International Symposium on Algorithms and Computation \uff08ISAAC2015\uff09, LNCS 9472 \uff082015\uff09, pp. 129\u2013139.
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\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
7<\/td>\n2017<\/a><\/td>\n\u4f50\u3005\u6728\u5eb7\u6717<\/td>\n\u5317\u9678\u5148\u7aef\u79d1\u5b66\u6280\u8853\u5927\u5b66\u9662\u5927\u5b66<\/td>\n
\n\uff3b1\uff3d Y. Sasaki. \u201cAn equivalence result on the reduction of games with unawareness,\u201d International Game Theory Review, 18\uff082016\uff09, 1650009 \uff0827 pages\uff09.
\n\uff3b2\uff3d Y. Sasaki. \u201cGeneralized Nash equilibrium with stable belief hierarchies in static games with unawareness,\u201d Annals of Operations Research\uff08 2016\uff09.\uff08 In Press, \u30aa\u30f3\u30e9\u30a4\u30f3\u7248\u516c\u958b\u6e08\u307f\uff09
\n\uff3b3\uff3d Y. Sasaki, R. P. H\u00e4m\u00e4l\u00e4inen, and E. Saarinen. \u201cModeling systems of holding back as hypergames and their connections with systems intelligence,\u201d Systems Research and Behavioral Science, 32\uff082015\uff09, pp. 93\u2013602.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
6<\/td>\n2016<\/a><\/td>\n\u8c37\u5ddd\u771e\u4e00<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\n
\n[1] Naoki Katoh and Shin-ichi Tanigawa: Rooted-tree decompositions with matroid constraints and the infinitesimal rigidity of frameworks with boundaries, SIAM Journal on Discrete Mathematics, 27(2013), 155-185.
\n[2] Shin-ichi Tanigawa: Matroids of gain graphs in applied discrete geometry, Transactions of the American Mathematical Society, 367(2015), 8597-8641.
\n[3] Shin-ichi Tanigawa: Sufficient conditions for globally rigidity of graphs, Journal of Combinatorial Theory, Series B, 113(2015), 123-140.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
6<\/td>\n2016<\/a><\/td>\n\u6cb3\ufa11\u4eae<\/td>\n\u6771\u4eac\u5de5\u696d\u5927\u5b66<\/td>\n
\n[1] Kawasaki, R.: Roth-Postlewaite stability and von Neumann-Morgenstern stability, Journal of Mathematical Economics, 58(2015), 1-6.
\n[2] Kawasaki, R.: Maximin, minimax, and von Neumann-Morgenstern farsighted stable sets, Mathematical Social Sciences, 74(2015), 8-12.
\n[3] Yamamura, H. and R. Kawasaki :Generalized average rules as stable Nash mechanisms to implement generalized median\u3000 rules, Social Choice and Welfare, 40(2013), 815-832.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
5<\/td>\n2015<\/a><\/td>\n\u798f\u7530\u30a8\u30ec\u30f3\u79c0\u7f8e<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\n
\n[1] E. H. Fukuda and L. M. Grana Drummond: Inexact projected gradient method for vector optimization, Computational Optimization and Applications, 54(2013) , 473-493.
\n[2] R. Andreani, E. H. Fukuda, and P. J. S. Silva: A Gauss-Newton approach for solving constrained optimization problems using differentiable exact penalties, Journal of Optimization Theory and Applications, 156 (2013), 417-449.
\n[3] E. H. Fukuda, P. J. S. Silva, and M. Fukushima: Differentiable exact penalty functions for nonlinear second-order cone programs, SIAM Journal on Optimization, 22 (2012), 1607-1633.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
5<\/td>\n2015<\/a><\/td>\n\u5965\u91ce\u8cb4\u4e4b<\/td>\n\u6771\u4eac\u7406\u79d1\u5927\u5b66<\/td>\n
\n[1] T. Okuno, and M. Fukushima: Local reduction based SQP-type method for semi-infinite programs with an infinite number of second-order cone constraints, Journal of Global Optimization, 60 (2014), 25-48.
\n[2] H. Yamaura, T. Okuno, S. Hayashi, and M. Fukushima: A smoothing SQP method for mathematical programming with linear second-order cone complementarity constraints, Pacific Journal of Optimization, 9 (2013) , 345-372.
\n[3] T. Okuno, S. Hayashi, and M. Fukushima: A regularized explicit exchange method for semi-infinite programs with an infinite number of conic constraints, SIAM Journal on Optimization, 22 (2012), 1009-1028.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
5<\/td>\n2015<\/a><\/td>\n\u5742\u6771\u6842\u4ecb<\/td>\n\u6771\u4eac\u5de5\u696d\u5927\u5b66<\/td>\n
\n[1] K. Bando: On the existence of a strictly strong Nash equilibrium under the student-optimal deferred acceptance algorithm, Games and Economic Behavior, 87 (2014), 269-287.
\n[2] K. Bando: A modified deferred acceptance algorithm for many-to-one matching markets with externalities among firms, Journal of Mathematical Economics, 52 (2014), 173-181.
\n[3] K. Bando: Many-to-one matching markets with externalities among firms, Journal of Mathematical Economics, 48 (2012), 14-20.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
5<\/td>\n2015<\/a><\/td>\n\u5c0f\u6797\u6b63\u5f18<\/td>\n\u6771\u6d77\u5927\u5b66<\/td>\n
\n[1] M. Kobayashi, M. Miyazawa and H. Shimizu: Structure-reversibility of a two dimensional reflecting random walk and its application to queueing network, Probability in the Engineering and Informational Sciences, 29(2015), 1-25.
\n[2] M. Kobayashi and M. Miyazawa : Tail asymptotics of the stationary distribution of a two dimensional reflecting random walk with unbounded upward jumps, Advances in Applied Probability, 46(2014), 365-399.
\n[3] M. Kobayashi, Y. Sakuma and M. Miyazawa: Join the shortest queue among k parallel queues: tail asymptotics of its stationary distribution, Queueing Systems, 74 (2013), 303-333.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
5<\/td>\n2015<\/a><\/td>\n\u4f50\u91ce\u826f\u592b<\/td>\n\u7b51\u6ce2\u5927\u5b66<\/td>\n
\n[1] Suh-Ryung Kim, Jung Yeun Lee, Boram Park, Yoshio Sano: The competition hypergraphs of doubly partial orders, Discrete Applied Mathematics, 165(2014), 185-191.
\n[2] Akihiro Munemasa, Yoshio Sano, Tetsuji Taniguchi: Fat Hoffman graphs with smallest eigenvalue greater than -1-\u03c4, Ars Mathematica Contemporanea, 7(1), (2014), 247-262.
\n[3] Akihiro Munemasa, Yoshio Sano, Tetsuji Taniguchi: Fat Hoffman graphs with smallest eigenvalue greater than -3, Discrete Applied Mathematics, 176(2014), 78-88.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
4<\/td>\n2014<\/a><\/td>\n\u98ef\u585a\u79c0\u660e<\/td>\n\u660e\u6cbb\u5927\u5b66<\/td>\n
\n1. \u201cFixed point optimization algorithms for distributed optimization in networked systems,\u201d SIAM Journal on Optimization, 23 (2013), 1-26.
\n2. \u201cIterative algorithm for triple-hierarchical constrained nonconvex optimization problem and its application to network bandwidth allocation,\u201d SIAM Journal on Optimization, 22 (2012), 862-878.
\n3. \u201cFixed point optimization algorithm and its application to power control in CDMA data networks,\u201d Mathematical Programming, 133 (2012), 227-242.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
4<\/td>\n2014<\/a><\/td>\n\u5cb8\u672c\u4fe1<\/td>\n\u6771\u4eac\u5de5\u696d\u5927\u5b66<\/td>\n
\n1. \u201cBargaining outcomes in patent licensing: Asymptotic results in a general Cournot market,\u201d Mathematical Social Sciences, 61 (2011) 114-123. \uff08\u5171\u8457\u8005 N. Watanabe and S. Muto\uff09
\n2. \u201cFee versus royalty policy in licensing through bargaining: An application of the Nash bargaining solution,\u201d Bulletin of Economic Research, 64 (2012) 293-304.\uff08\u5171\u8457\u8005 S. Muto\uff09
\n3. \u201cStable bargaining outcomes in patent licensing: A cooperative game approach without side payments,\u201d Mathematical Social Sciences, 66 (2013) 183-195.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
4<\/td>\n2014<\/a><\/td>\n\u8096\u9704<\/td>\n\u9996\u90fd\u5927\u5b66\u6771\u4eac<\/td>\n
\n1. \u201cSoftware failure time data analysis via wavelet-based approach,\u201d IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (A), E95-A (2012) 1490-1497\uff0e\uff08\u5171\u8457\u8005 T\uff0eDohi\uff09
\n2. \u201cWavelet shrinkage estimation for NHPP-based software reliability models,\u201dIEEE Transactions on Reliability, 62 (2013) 211-225\uff0e\uff08\u5171\u8457\u8005 T\uff0eDohi\uff09
\n3. \u201cEstimating software intensity function based on translation-invariant Poisson smoothing approach, \u201d IEEE Transactions on Reliability, 62 (2013) 930-945\uff0e\uff08\u5171\u8457\u8005 T\uff0eDohi\uff09
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
4<\/td>\n2014<\/a><\/td>\n\u9ad8\u91ce\u7950\u4e00<\/td>\n\u6771\u4eac\u5de5\u696d\u5927\u5b66<\/td>\n
\n1. \u201cMulti-period portfolio selection using kernel-based control policy with dimensionality reduction,\u201d Expert Systems with Applications, 41 (2014), 3901-3914. \uff08\u5171\u8457\u8005 J. Gotoh\uff09
\n2. \u201cA sequential competitive bidding strategy considering inaccurate cost estimates,\u201d Omega, 42 (2014), 132-140. \uff08\u5171\u8457\u8005N. Ishii and M. Muraki\uff09
\n3. \u201cA polynomial optimization approach to constant rebalanced portfolio selection,\u201d Computational Optimization and Applications, 52 (2012), 645-666. \uff08\u5171\u8457\u8005 R. Sotirov\uff09
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
4<\/td>\n2014<\/a><\/td>\n\u798f\u6c38\u62d3\u90ce<\/td>\n\u56fd\u7acb\u60c5\u5831\u5b66\u7814\u7a76\u6240<\/td>\n
\n1. \u201cDivide-and-conquer algorithms for partitioning hypergraphs and submodular systems,\u201d Algorithmica, 62 (2012), 787-806.\uff08\u5171\u8457\u8005 K. Okumoto and H. Nagamochi\uff09
\n2. \u201cIterative rounding approximation algorithms for degree-bounded node-connectivity network design\u201d, Proceedings of 53rd Annual IEEE Symposium on Foundations of Computer Science, (2012) 263-272. \uff08\u5171\u8457\u8005 R. Ravi\uff09
\n3. \u201cApproximating minimum cost source location problems with local vertex-connectivity demand,\u201d Journal of Discrete Algorithms, 19 (2013), 30-38.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
3<\/td>\n2013<\/a><\/td>\n\u84ee\u6c60\u9686<\/td>\n\u5927\u962a\u5927\u5b66<\/td>\n
\n1. \u201cRobust shortest path problem based on a confidence interval in fuzzy bicriteria decision making,\u201d Information Sciences, 221, pp. 520-533, 2013.
\n2. \u201cInteractive decision making for uncertain minimum spanning tree problems with total importance based on a risk-management approach,\u201d (\u5171\u8457\u8005H. Katagiri), Applied Mathematical Modelling, 37, pp. 4548-4560, 2013.
\n3. \u201cRisk-control approach for a bottleneck spanning tree problem with the total network reliability under uncertainty,\u201d (\u5171\u8457\u8005H. Katagiri and H. Tsuda), Journal of Applied Mathematics, Article ID 364086, doi:10.1155\/2012\/364086, 2012.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
3<\/td>\n2013<\/a><\/td>\n\u4f50\u4e45\u9593\u5927<\/td>\n\u5e83\u5cf6\u5546\u8239\u9ad8\u7b49\u5c02\u9580\u5b66\u6821<\/td>\n
\n1. \u201cStationary distribution of a multi-server vacation queue with constant impatient times, ” (\u5171\u8457\u8005A. Inoie), Operations Research Letters 40 (2012), 239-243.
\n2. \u201cAsymptotic behavior for MArP\/PH\/2 queue with join the shortest queue discipline, \u201d Journal of the Operations Research Society of Japan 54 (2011), 46-64.
\n3. \u201cAsymptotic behavior for MArP\/PH\/c queue with shortest queue discipline and jockeying, \u201d Operations Research Letters 38 (2010), 7-10.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
3<\/td>\n2013<\/a><\/td>\nPhung-Duc Tuan<\/td>\n\u6771\u4eac\u5de5\u696d\u5927\u5b66<\/td>\n
\n1. \u201cState-Dependent M\/M\/c\/c+r Retrial Queues with Bernoulli Abandonment, \u201d (\u5171\u8457\u8005H. Masuyama, S. Kasahara, and Y. Takahashi), Journal of Industrial and Management Optimization, Vol. 6, No. 3, 2010, pp. 517–540.
\n2. \u201cA Matrix Continued Fraction Approach to Multiserver Retrial Queues, \u201d (\u5171\u8457\u8005H. Masuyama, S. Kasahara, and Y. Takahashi), Annals of Operations Research, Vol. 202, No. 1, 2013, pp. 161-183.
\n3. \u201cSingle Server Retrial Queues with Two Way Communication, \u201d (\u5171\u8457\u8005J. R. Artalejo), Applied Mathematical Modelling, Vol. 37, No. 4, 2013, pp. 1811–1822.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
3<\/td>\n2013<\/a><\/td>\n\u5c71\u4e0b\u771f<\/td>\n\u6771\u4eac\u5de5\u696d\u5927\u5b66<\/td>\n
\n1. \u201cParallel solver for semidefinite programming problem having sparse Schur complement matrix, \u201d (\u5171\u8457\u8005K. Fujisawa, M. Fukuda, K. Nakata, and M. Nakata), ACM Transactions on Mathematical Software, Vol. 39 (2012), Article No. 6.
\n2. \u201cSFSDP: a Sparse Version of Full SemiDefinite Programming Relaxation for Sensor Network Localization Problems, \u201d (\u5171\u8457\u8005S. Kim, M. Kojima, and H. Waki), ACM Transactions on Mathematical Software, Vol. 38 (2012), Article No. 27.
\n3. \u201cEnclosing Ellipsoids and Elliptic Cylinders of Semialgebraic Sets and Their Application to Error Bounds in Polynomial Optimization, \u201d (\u5171\u8457\u8005M. Kojima), Mathematical Programming, 138, 333-364 (2013).
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
2<\/td>\n2012<\/a><\/td>\n\u5c0f\u6797\u4f51\u8f14<\/td>\n\u6771\u4eac\u5927\u5b66<\/td>\n
\n1. On Shortest Disjoint Paths in Planar Graphs. Yusuke Kobayashi and Christian Sommer, Discrete Optimization, Vol.7 (2010), pp. 234-245.
\n2. An Improved Algorithm for the Half-disjoint PathsProblem. Ken-ichi Kawarabayashi and Yusuke Kobayashi, SIAM Journal on Discrete Mathematics, Vol.25 (2011), pp. 1322-1330.
\n3. Breaking O(n1\/2)-approximation Algorithms for the Edge-disjoint Paths Problem with Congestion Two. Ken-ichi Kawarabayashi and and Yusuke Kobayashi, Proceedings of the 43rd ACM Symposium on Theory of Computing (STOC 2011), 2011, pp. 81-88.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
2<\/td>\n2012<\/a><\/td>\n\u5ca1\u672c\u5409\u592e<\/td>\n\u96fb\u6c17\u901a\u4fe1\u5927\u5b66<\/td>\n
\n1. The Geodesic Diameter of Polygonal Domains. Sang Won Bae, Matias Korman, and Yoshio Okamoto, Lecture Notes in Computer Science, 6346 (2010) 500-511.
\n2. A Polynomial-time-delay Polynomial-space Algorithm for Enumeration Problems in Multi-criteria Optimization. Yoshio Okamoto and Takeaki Uno, European Journal of Operational Research, Vol.210 (2011), pp.48-56
\n3. Hardness Results and an Exact Exponential Algorithm for the Spanning Tree Congestion Problem. Yoshio Okamoto, Yota Otachi, Ryuhei Uehara, and Takeaki Uno, Journal of Graph Algorithms and Applications, Vol.15 (2011), pp.727-751
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
2<\/td>\n2012<\/a><\/td>\n\u5317\u539f\u77e5\u5c31<\/td>\n\u6771\u4eac\u5de5\u696d\u5927\u5b66<\/td>\n
\n1. A Bound for the Number of Different Basic Solutions Generated by the Simplex Method. Tomonari Kitahara and Shinji Mizuno, Mathematical Programming \u306b\u63b2\u8f09\u6c7a\u5b9a\u6e08\u307f\uff08Online \u7248\u306f2011\u5e748\u67083\u65e5\u306b\u516c\u958b\uff09
\n2. Klee-Minty\u2019s LP and Upper Bounds for Dantzig\u2019s Simplex Method. Tomonari Kitahara and Shinji Mizuno, Operations Research Letters, Vol.39 (2011), pp.88-91
\n3. Proximity of Weighted and Layered Least Squares Solutions. Tomonari Kitahara and Takashi Tsuchiya, SIAM Journal on Matrix Analysis and Applications, Vol.31 (2009), pp1172-1186.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
2<\/td>\n2012<\/a><\/td>\n\u5bae\u4ee3\u9686\u5e73<\/td>\n\u6771\u4eac\u8fb2\u5de5\u5927\u5b66<\/td>\n
\n1. An Approximation Algorithm for the Traveling Tournament Problem. Ryuhei Miyashiro, Tomomi Matsui,and Shinji Imahori, Annals of Operations Research, Vol.194, No.1 (2012), pp.317-324.
\n2. An Improved Approximation Algorithm for the Traveling Tournament Problem. Daisuke Yamaguchi, Shinji Imahori, Ryuhei Miyashiro, and Tomomi Matsui, Algorithmica, Vol.61 (2011), pp.1077-1091.
\n3. An Approximation Algorithm for the Unconstrained Traveling Tournament Problem. Shinji Imahori, Tomomi Matsui, and Ryuhei , (PATAT 2010) (2010), pp.508-512.Miyashiro, Proc. 8th International Conference on the Practice and Theory of Automated Timetabling.
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n
1<\/td>\n2011<\/a><\/td>\n\u8107\u96bc\u4eba<\/td>\n\u96fb\u6c17\u901a\u4fe1\u5927\u5b66<\/td>\n
\n1. \u201cSparsePOP: a Sparse Semidefinite Programming Relaxation of Polynomial Optimization Problems,\u201d ACM Transactions on Mathematical Software, 15 (2008) (\u5171\u8457\u8005 S. Kim, M. Kojima, M. Muramatsu, H. Sugimoto).
\n2. \u201cExploiting Sparsity in SDP Relaxation for Sensor Network Localization,\u201d SIAM Journal on Optimization, 20 (2009) (\u5171\u8457\u8005 S. Kim, M. Kojima).
\n3. \u201cA Facial Reduction Algorithm for Finding Sparse SOS Representations,\u201d Operations Research Letters, 38 (2010) (\u5171\u8457\u8005 M. Muramatsu).
\n<\/div>
\u7d9a\u304d\u3092\u8868\u793a<\/span><\/a><\/div>
\u5c0f\u3055\u3044\u8868\u793a<\/span><\/a><\/div><\/div><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\u6587\u732e\u8cde\u5968\u52b1\u8cde Best Paper of the Year among Young Researchers<\/h5>\n

\u203b\u7814\u7a76\u8cde\u5968\u52b1\u8cde\u306e\u524d\u8eab<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u56de<\/th>\n\u5e74\u5ea6<\/th>\n\u53d7\u8cde\u8005<\/th>\n\u6240\u5c5e<\/th>\n\u7814\u7a76\u5185\u5bb9<\/th>\n<\/tr>\n<\/thead>\n
5<\/td>\n2010<\/a><\/td>\n\u6885\u8c37\u4fca\u6cbb<\/td>\n\u5927\u962a\u5927\u5b66<\/td>\nSolving the irregular strip packing problem via guided local
\nsearch for overlap minimization
\n\uff08International Transactions in Operational Research,
\nVol.16, No.6\uff09<\/td>\n<\/tr>\n
5<\/td>\n2010<\/a><\/td>\n\u6050\u795e\u8cb4\u884c<\/td>\n\u65e5\u672c\u30a2\u30a4\u30fb\u30d3\u30fc\u30fb\u30a8\u30e0\uff08\u682a\uff09<\/td>\nFinding probably best systems quickly via simulations
\n\uff08ACM Transactions on Modeling and Computer
\nSimulation, Vol.19, No.3\uff09<\/td>\n<\/tr>\n
5<\/td>\n2010<\/a><\/td>\n\u6765\u5d8b\u79c0\u6cbb<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\nApproximation algorithm and perfect sampler for closed
\nJackson networks with single servers
\n\uff08SIAM Journal on Computing, Vol.38, No.4\uff09<\/td>\n<\/tr>\n
5<\/td>\n2010<\/a><\/td>\n\u9ad8\u6fa4\u517c\u4e8c\u90ce<\/td>\n\u6771\u4eac\u5927\u5b66<\/td>\nA weighted even factor algorithm
\n\uff08Mathematical Programming, 115\uff09
\nA weighted kt,t-free t-factor algorithm for bipartite graphs
\n\uff08Mathematics of Operations Research, Vol.34, No.2\uff09<\/td>\n<\/tr>\n
5<\/td>\n2010<\/a><\/td>\n\u6797\u4fca\u4ecb<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\nSpectrum management for interference-limited multiuser
\ncommunication systems
\n\uff08IEEE Transactions on Information Theory, Vol.55, No.3\uff09<\/td>\n<\/tr>\n
4<\/td>\n2009<\/a><\/td>\n\u57a3\u6751\u5c1a\u5fb3<\/td>\n\u6771\u4eac\u5927\u5b66<\/td>\nSolving Linear Programs from Sign Patterns
\n\uff08Mathematical Programming, Vol.114, No.2\uff09
\nSign-Solvable Linear \u30fbComplementarity Problems
\n\uff08Linear Algebra and its Applications, Vol.429, Nos. 1-2\uff09<\/td>\n<\/tr>\n
4<\/td>\n2009<\/a><\/td>\n\u52a0\u85e4\u61b2\u4e00<\/td>\n\u6771\u4eac\u5de5\u696d\u5927\u5b66<\/td>\nUpper bound for the decay rate of the joint queue-length distribution in a two-node Markovian queueing system
\n\uff08Queueing Systems, Vol.58, No.3\uff09<\/td>\n<\/tr>\n
4<\/td>\n2009<\/a><\/td>\n\u5e73\u4e95\u5e83\u5fd7<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\nElectric Network Classifiers for Semi-Supervised Learning on Graphs
\n\uff08Journal of the Operations Research Society of Japan, Vol.50, No.3\uff09<\/td>\n<\/tr>\n
3<\/td>\n2008<\/a><\/td>\n\u6210\u5cf6\u5eb7\u53f2<\/td>\n\u6771\u4eac\u7406\u79d1\u5927\u5b66<\/td>\nA Nonmonotone Memory Gradient Method for Unconstrained Optimization
\n\uff08Journal of the Operations Research Society of Japan Vol.50, No.1\uff09<\/td>\n<\/tr>\n
3<\/td>\n2008<\/a><\/td>\n\u798f\u7530\u5149\u6d69<\/td>\n\u6771\u4eac\u5de5\u696d\u5927\u5b66<\/td>\nLarge-scale semidefinite programs in electronic structure calculation
\n\uff08Mathematical Programming Series B, 109\uff09<\/td>\n<\/tr>\n
2<\/td>\n2007<\/a><\/td>\n\u77f3\u4e95\u5229\u660c<\/td>\n\u5c0f\u6a3d\u5546\u79d1\u5927\u5b66<\/td>\nMinimum augmentation of local edge-connectivity between vertices and vertex subsets in undirected graphs
\n\uff08Discrete Applied Mathematics 154\uff09<\/td>\n<\/tr>\n
2<\/td>\n2007<\/a><\/td>\n\u5f8c\u85e4\u9806\u54c9<\/td>\n\u7b51\u6ce2\u5927\u5b66<\/td>\nMinimal Ellipsoid Circumscribing a Polytope Defined by a System of Linea Inequalities
\n\uff08Journal of Global Optimization 34\uff09
\nA linear classification model based on conditional geometric score
\n\uff08Pacific Journal of Optimization Vol.1, No.2\uff09<\/td>\n<\/tr>\n
2<\/td>\n2007<\/a><\/td>\n\u5897\u5c71\u535a\u4e4b<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\nAlgorithmic Computation of the Time-Dependent Solution of Structured Markov Chains and Its Application to Queues
\n\uff08Stochastic Models Vol.21\uff09<\/td>\n<\/tr>\n
1<\/td>\n2006<\/a><\/td>\n\u6cb3\u897f\u61b2\u4e00<\/td>\n\u7fa4\u99ac\u5927\u5b66<\/td>\nOn the Counting Process for a Class of Markovian Arrival Processes with an Application to a Queueing System
\n\uff08Queueing Systems 49\uff09<\/td>\n<\/tr>\n
1<\/td>\n2006<\/a><\/td>\n\u5869\u6d66\u662d\u7fa9<\/td>\n\u6771\u5317\u5927\u5b66<\/td>\nFast Scaling Algorithms for M-convex Function Minimization with Application to the Resource Allocation Problem
\n\uff08Discrete Applied Mathematics 134\uff09<\/td>\n<\/tr>\n
1<\/td>\n2006<\/a><\/td>\n\u85e4\u6fa4\u514b\u6a39<\/td>\n\u6771\u4eac\u96fb\u6a5f\u5927\u5b66<\/td>\nSolving Large Scale Optimization Problems via Grid and Cluster Computing
\n\uff08Journal of the Operations Research Society of Japan Vol.47, No.4\uff09<\/td>\n<\/tr>\n
1<\/td>\n2006<\/a><\/td>\n\u5c71\u4e0b\u4fe1\u96c4<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\nOn the identification of degenerate indices in the nonlinear complementarity problem with the proximal point algorithm
\n\uff08Mathematical Programming Vol.99, No.2\uff09<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
\n
\u696d\u7e3e\u8cde Achievement Award<\/h5>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u56de<\/th>\n\u5e74\u5ea6<\/th>\n\u53d7\u8cde\u8005<\/th>\n\u6240\u5c5e<\/th>\n<\/tr>\n<\/thead>\n
26<\/td>\n2024<\/td>\n\u571f\u8c37\u9686<\/td>\n\u653f\u7b56\u7814\u7a76\u5927\u5b66\u9662\u5927\u5b66<\/td>\n<\/tr>\n
25<\/td>\n2023<\/a><\/td>\n\u6edd\u6839\u54f2\u54c9<\/td>\n\u5927\u962a\u5927\u5b66<\/td>\n<\/tr>\n
25<\/td>\n2023<\/a><\/td>\n\u7530\u6751\u660e\u4e45<\/td>\n\u6176\u61c9\u7fa9\u587e\u5927\u5b66<\/td>\n<\/tr>\n
24<\/td>\n2022<\/a><\/td>\n\u5409\u702c\u7ae0\u5b50<\/td>\n\u7b51\u6ce2\u5927\u5b66<\/td>\n<\/tr>\n
23<\/td>\n2021<\/a><\/td>\n\u677e\u4e95\u77e5\u5df1<\/td>\n\u6771\u4eac\u5de5\u696d\u5927\u5b66<\/td>\n<\/tr>\n
22<\/td>\n2020<\/a><\/td>\n\u6728\u6751\u4fca\u4e00<\/td>\n\u5317\u6d77\u9053\u5927\u5b66<\/td>\n<\/tr>\n
21<\/td>\n2019<\/a><\/td>\n\u9234\u6728\u6566\u592b<\/td>\n\u5357\u5c71\u5927\u5b66<\/td>\n<\/tr>\n
20<\/td>\n2018<\/a><\/td>\n\u6c60\u4e0a\u6566\u5b50<\/td>\n\u6210\u8e4a\u5927\u5b66<\/td>\n<\/tr>\n
19<\/td>\n2017<\/a><\/td>\n\u77e2\u90e8\u535a<\/td>\n\u6771\u4eac\u7406\u79d1\u5927\u5b66<\/td>\n<\/tr>\n
18<\/td>\n2016<\/a><\/td>\n\u6c34\u91ce\u771e\u6cbb<\/td>\n\u6771\u4eac\u5de5\u696d\u5927\u5b66<\/td>\n<\/tr>\n
18<\/td>\n2016<\/a><\/td>\n\u5927\u6fa4\u7fa9\u660e<\/td>\n\u7b51\u6ce2\u5927\u5b66<\/td>\n<\/tr>\n
17<\/td>\n2015<\/a><\/td>\n\u8a72\u5f53\u306a\u3057<\/td>\n<\/td>\n<\/tr>\n
16<\/td>\n2014<\/a><\/td>\n\u5c71\u4e0a\u4f38<\/td>\n\u6771\u4eac\u30ac\u30b9(\u682a)<\/td>\n<\/tr>\n
15<\/td>\n2013<\/a><\/td>\n\u5ba4\u7530\u4e00\u96c4<\/td>\n\u6771\u4eac\u5927\u5b66<\/td>\n<\/tr>\n
14<\/td>\n2012<\/a><\/td>\n\u76f8\u6fa4\u308a\u3048\u5b50<\/td>\n<\/td>\n<\/tr>\n
14<\/td>\n2012<\/a><\/td>\n\u5c71\u672c\u82b3\u55e3<\/td>\n\u7b51\u6ce2\u5927\u5b66<\/td>\n<\/tr>\n
13<\/td>\n2011<\/a><\/td>\n\u52a0\u85e4\u76f4\u6a39<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\n<\/tr>\n
12<\/td>\n2010<\/a><\/td>\n\u7530\u53e3\u6771<\/td>\n\u4e2d\u592e\u5927\u5b66<\/td>\n<\/tr>\n
11<\/td>\n2009<\/a><\/td>\n\u798f\u5cf6\u96c5\u592b<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\n<\/tr>\n
10<\/td>\n2008<\/a><\/td>\n\u6b66\u85e4\u6ecb\u592b<\/td>\n\u6771\u4eac\u5de5\u696d\u5927\u5b66<\/td>\n<\/tr>\n
9<\/td>\n2007<\/a><\/td>\n\u5bae\u6ca2\u653f\u6e05<\/td>\n\u6771\u4eac\u7406\u79d1\u5927\u5b66<\/td>\n<\/tr>\n
8<\/td>\n2006<\/a><\/td>\n\u5ddd\u5cf6\u5e78\u4e4b\u52a9<\/td>\n\u6771\u4eac\u8fb2\u5de5\u5927\u5b66<\/td>\n<\/tr>\n
7<\/td>\n2005<\/a><\/td>\n\u68ee\u6238\u664b<\/td>\n\u65e9\u7a32\u7530\u5927\u5b66<\/td>\n<\/tr>\n
6<\/td>\n2004<\/a><\/td>\n\u5c71\u4e0b\u6d69<\/td>\n(\u682a)\u6570\u7406\u30b7\u30b9\u30c6\u30e0<\/td>\n<\/tr>\n
5<\/td>\n2003<\/a><\/td>\n\u9ad8\u6a4b\u5e78\u96c4<\/td>\n\u6771\u4eac\u5de5\u696d\u5927\u5b66<\/td>\n<\/tr>\n
4<\/td>\n2002<\/a><\/td>\n\u8170\u585a\u6b66\u5fd7<\/td>\n\u7b51\u6ce2\u5927\u5b66<\/td>\n<\/tr>\n
3<\/td>\n2001<\/td>\n\u4eca\u91ce\u6d69<\/td>\n\u4e2d\u592e\u5927\u5b66<\/td>\n<\/tr>\n
2<\/td>\n2000<\/td>\n\u8328\u6728\u4fca\u79c0<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\n<\/tr>\n
1<\/td>\n2000<\/td>\n\u4f0f\u898b\u6b63\u5247<\/td>\n\u5357\u5c71\u5927\u5b66<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
\n
\u666e\u53ca\u8cde Contributions Award for Promoting OR<\/h5>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u56de<\/th>\n\u5e74\u5ea6<\/th>\n\u53d7\u8cde\u8005<\/th>\n\u6240\u5c5e<\/th>\n<\/tr>\n<\/thead>\n
50<\/td>\n2024<\/td>\n\u4f4f\u7530\u6f6e<\/td>\n\u7b51\u6ce2\u5927\u5b66\u540d\u8a89\u6559\u6388<\/td>\n<\/tr>\n
50<\/td>\n2024<\/td>\n\u7267\u672c\u76f4\u6a39<\/td>\n\u7b51\u6ce2\u5927\u5b66<\/td>\n<\/tr>\n
49<\/td>\n2023<\/a><\/td>\n\u6817\u7530\u6cbb<\/td>\n\u6176\u61c9\u7fa9\u587e\u5927\u5b66<\/td>\n<\/tr>\n
49<\/td>\n2023<\/a><\/td>\n\u682a\u5f0f\u4f1a\u793e\u30a8\u30eb\u30c7\u30b7\u30e5\uff08\u4ee3\u8868\uff1a\u5ca9\u6c38\u4e8c\u90ce\uff09<\/td>\n<\/td>\n<\/tr>\n
48<\/td>\n2022<\/a><\/td>\n\u6e21\u8fba\u9686\u88d5<\/td>\n\u6771\u4eac\u90fd\u7acb\u5927\u5b66<\/td>\n<\/tr>\n
48<\/td>\n2022<\/a><\/td>\n\u6771\u4eac\u5927\u5b66\u751f\u7523\u6280\u8853\u7814\u7a76\u6240\u6b21\u4e16\u4ee3\u80b2\u6210\u30aa\u30d5\u30a3\u30b9\u30fb\u65e5\u672c\u822a\u7a7a2020,2021\u5e74\u5ea6\u98db\u884c\u6a5f\u30ef\u30fc\u30af\u30b7\u30e7\u30c3\u30d7\u4f01\u753b\u30c1\u30fc\u30e0\uff08\u4ee3\u8868\uff1a\u672c\u9593\u88d5\u5927\uff09<\/td>\n<\/td>\n<\/tr>\n
47<\/td>\n2021<\/a><\/td>\n\u6a2b\u5c3e\u535a<\/td>\n\u6771\u4eac\u30ac\u30b9\uff08\u682a\uff09<\/td>\n<\/tr>\n
47<\/td>\n2021<\/a><\/td>\n\u5bae\u4ee3\u9686\u5e73<\/td>\n\u6771\u4eac\u8fb2\u5de5\u5927\u5b66<\/td>\n<\/tr>\n
46<\/td>\n2020<\/a><\/td>\n\u9ad9\u6a4b\u8c4a<\/td>\n\u4eac\u90fd\u60c5\u5831\u5927\u5b66\u9662\u5927\u5b66\u30fb\u4eac\u90fd\u5927\u5b66\u540d\u8a89\u6559\u6388<\/td>\n<\/tr>\n
46<\/td>\n2020<\/a><\/td>\n\u5c71\u4e0b\u82f1\u660e<\/td>\n\u6771\u4eac\u90fd\u7acb\u5927\u5b66<\/td>\n<\/tr>\n
45<\/td>\n2019<\/a><\/td>\n\u7530\u8fba\u9686\u4eba<\/td>\n\uff08\u682a\uff09NTT\u30c7\u30fc\u30bf\u6570\u7406\u30b7\u30b9\u30c6\u30e0<\/td>\n<\/tr>\n
45<\/td>\n2019<\/a><\/td>\n\u751f\u7530\u76ee\u5d07<\/td>\n\u4e2d\u592e\u5927\u5b66<\/td>\n<\/tr>\n
44<\/td>\n2018<\/a><\/td>\n\u5cb3\u4e94\u4e00<\/td>\n\u7532\u5357\u5927\u5b66<\/td>\n<\/tr>\n
44<\/td>\n2018<\/a><\/td>\n\u9234\u6728\u6566\u592b<\/td>\n\u5357\u5c71\u5927\u5b66<\/td>\n<\/tr>\n
43<\/td>\n2017<\/a><\/td>\n\u6728\u6751\u4fca\u4e00<\/td>\n\u95a2\u897f\u5927\u5b66<\/td>\n<\/tr>\n
43<\/td>\n2017<\/a><\/td>\n\u658e\u85e4\u52aa<\/td>\n(\u682a)\u30d3\u30fc\u30d7\u30e9\u30a6\u30c9<\/td>\n<\/tr>\n
42<\/td>\n2016<\/a><\/td>\n\u5c71\u7530\u8302<\/td>\n\u9ce5\u53d6\u5927\u5b66<\/td>\n<\/tr>\n
42<\/td>\n2016<\/a><\/td>\n\u9999\u7530\u6b63\u4eba<\/td>\n\u7b51\u6ce2\u5927\u5b66<\/td>\n<\/tr>\n
41<\/td>\n2015<\/a><\/td>\n\u9234\u6728\u4e45\u654f<\/td>\n\u7b51\u6ce2\u5927\u5b66<\/td>\n<\/tr>\n
41<\/td>\n2015<\/a><\/td>\n\u5c71\u4e0b\u52dd\u6bd4\u62e1<\/td>\n(\u682a)\u6771\u829d<\/td>\n<\/tr>\n
40<\/td>\n2014<\/a><\/td>\n\u4e0a\u7530\u5fb9<\/td>\n\u6210\u8e4a\u5927\u5b66<\/td>\n<\/tr>\n
40<\/td>\n2014<\/a><\/td>\n\u9006\u702c\u5ddd\u6d69\u5b5d<\/td>\n\u65e9\u7a32\u7530\u5927\u5b66<\/td>\n<\/tr>\n
39<\/td>\n2013<\/a><\/td>\n\u4e45\u5fd7\u672c\u8302<\/td>\n<\/td>\n<\/tr>\n
39<\/td>\n2013<\/a><\/td>\n\u5927\u5c71\u9054\u96c4<\/td>\n\u653f\u7b56\u7814\u7a76\u5927\u5b66\u9662\u5927\u5b66<\/td>\n<\/tr>\n
38<\/td>\n2012<\/a><\/td>\n\u5c3e\ufa11\u4fca\u6cbb<\/td>\n<\/td>\n<\/tr>\n
38<\/td>\n2012<\/a><\/td>\n\u516b\u5dfb\u76f4\u4e00<\/td>\n\u9759\u5ca1\u5927\u5b66<\/td>\n<\/tr>\n
37<\/td>\n2011<\/a><\/td>\n\u9234\u6728\u9053\u592b<\/td>\n(\u8ca1)\u96fb\u529b\u4e2d\u592e\u7814\u7a76\u6240<\/td>\n<\/tr>\n
37<\/td>\n2011<\/a><\/td>\n\u771f\u934b\u9f8d\u592a\u90ce<\/td>\n\u6587\u6559\u5927\u5b66<\/td>\n<\/tr>\n
36<\/td>\n2010<\/a><\/td>\n\u6fa4\u6728\u52dd\u8302<\/td>\n\u5357\u5c71\u5927\u5b66<\/td>\n<\/tr>\n
36<\/td>\n2010<\/a><\/td>\n\u5c71\u7530\u5584\u9756<\/td>\n\u76ee\u767d\u5927\u5b66<\/td>\n<\/tr>\n
35<\/td>\n2009<\/a><\/td>\n\u68ee\u6e05\u5c2d<\/td>\n\u5143(\u8ca1)\u96fb\u529b\u4e2d\u592e\u7814\u7a76\u6240<\/td>\n<\/tr>\n
35<\/td>\n2009<\/a><\/td>\n\u5c71\u7530\u90c1\u592b<\/td>\n\u5143(\u682a)\u4e09\u83f1\u7dcf\u5408\u7814\u7a76\u6240<\/td>\n<\/tr>\n
34<\/td>\n2008<\/a><\/td>\n\u53e4\u6797\u9686<\/td>\n\u6cd5\u653f\u5927\u5b66<\/td>\n<\/tr>\n
34<\/td>\n2008<\/a><\/td>\n\u68ee\u96c5\u592b<\/td>\n\u6771\u4eac\u5de5\u696d\u5927\u5b66\u540d\u8a89\u6559\u6388<\/td>\n<\/tr>\n
33<\/td>\n2007<\/a><\/td>\n\u6728\u4e0b\u6804\u8535<\/td>\n\u540d\u57ce\u5927\u5b66<\/td>\n<\/tr>\n
33<\/td>\n2007<\/a><\/td>\n\u524d\u7530\u5fe0\u662d<\/td>\n\u6771\u4eac\u30ac\u30b9(\u682a)<\/td>\n<\/tr>\n
32<\/td>\n2006<\/a><\/td>\n\u4e2d\u91ce\u4e00\u592b<\/td>\n(\u682a)\u69cb\u9020\u8a08\u753b\u7814\u7a76\u6240<\/td>\n<\/tr>\n
31<\/td>\n2005<\/a><\/td>\n\u82e5\u5c71\u90a6\u7d18<\/td>\n\u6cd5\u653f\u5927\u5b66<\/td>\n<\/tr>\n
30<\/td>\n2004<\/a><\/td>\n\u67f3\u4e95\u6d69<\/td>\n\u6176\u61c9\u7fa9\u587e\u5927\u5b66\u540d\u8a89\u6559\u6388<\/td>\n<\/tr>\n
29<\/td>\n2003<\/a><\/td>\n\u5927\u91ce\u52dd\u4e45<\/td>\n\u540d\u53e4\u5c4b\u5de5\u696d\u5927\u5b66<\/td>\n<\/tr>\n
29<\/td>\n2003<\/a><\/td>\n\u9ad8\u68ee\u5bdb<\/td>\n\u9752\u5c71\u5b66\u9662\u5927\u5b66<\/td>\n<\/tr>\n
28<\/td>\n2002<\/a><\/td>\n\u4e45\u4fdd\u5e79\u96c4<\/td>\n\u6771\u4eac\u5546\u8239\u5927\u5b66<\/td>\n<\/tr>\n
28<\/td>\n2002<\/a><\/td>\n\u9ad8\u4e95\u82f1\u9020<\/td>\n(\u682a)\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u30b9<\/td>\n<\/tr>\n
27<\/td>\n2001<\/td>\n\u4f0a\u5009\u7fa9\u90ce<\/td>\n(\u682a)\u30b5\u30a4\u30c6\u30c3\u30af\u30fb\u30b8\u30e3\u30d1\u30f3<\/td>\n<\/tr>\n
27<\/td>\n2001<\/td>\n\u5e73\u5c3e\u4fe1\u6b63<\/td>\n(\u682a)\u30ac\u30b9\u30bf\u30fc<\/td>\n<\/tr>\n
26<\/td>\n2000<\/td>\n\u9752\u6cbc\u9f8d\u96c4<\/td>\n\u795e\u6238\u5b66\u9662\u5927\u5b66<\/td>\n<\/tr>\n
26<\/td>\n2000<\/td>\n\u677e\u4e95\u77e5\u5df1<\/td>\n\u6771\u4eac\u5927\u5b66<\/td>\n<\/tr>\n
25<\/td>\n1999<\/td>\n\u6d77\u8fba\u4e0d\u4e8c\u96c4<\/td>\n\u30b3\u30f3\u30b5\u30eb\u30bf\u30f3\u30c8<\/td>\n<\/tr>\n
25<\/td>\n1999<\/td>\n\u5c0f\u7b20\u539f\u6681<\/td>\n\u30ed\u30b4\u30f4\u30a3\u30b9\u30bf(\u682a)<\/td>\n<\/tr>\n
24<\/td>\n1998<\/td>\n\u6a29\u85e4\u5143<\/td>\n\u30aa\u30fc\u30a2\u30fc\u30eb\u3068\u304f\u587e<\/td>\n<\/tr>\n
24<\/td>\n1998<\/td>\n\u7267\u91ce\u90fd\u6cbb<\/td>\n\u6771\u4eac\u7406\u79d1\u5927\u5b66<\/td>\n<\/tr>\n
23<\/td>\n1997<\/td>\n\u4f0a\u7406\u6b63\u592b<\/td>\n\u4e2d\u592e\u5927\u5b66<\/td>\n<\/tr>\n
23<\/td>\n1997<\/td>\n\u9ad8\u6a4b\u78d0\u90ce<\/td>\n\u65e5\u672c\u5927\u5b66<\/td>\n<\/tr>\n
22<\/td>\n1996<\/td>\n\u672c\u544a\u5149\u7537<\/td>\n\u611b\u77e5\u5de5\u696d\u5927\u5b66<\/td>\n<\/tr>\n
22<\/td>\n1996<\/td>\n\u6e21\u8fba\u6d69<\/td>\n\u7b51\u6ce2\u5927\u5b66\u540d\u8a89\u6559\u6388<\/td>\n<\/tr>\n
21<\/td>\n1995<\/td>\n\u5fa1\u5712\u751f\u5584\u5c1a<\/td>\n\u6771\u5317\u5927\u5b66\u540d\u8a89\u6559\u6388<\/td>\n<\/tr>\n
21<\/td>\n1995<\/td>\n\u77e2\u90e8\u771e<\/td>\n\u5de5\u5b66\u9662\u5927\u5b66\u540d\u8a89\u6559\u6388<\/td>\n<\/tr>\n
20<\/td>\n1994<\/td>\n\u5150\u7389\u6b63\u61b2<\/td>\n\u4e5d\u5dde\u5927\u5b66<\/td>\n<\/tr>\n
20<\/td>\n1994<\/td>\n\u9577\u8c37\u5ddd\u5229\u6cbb<\/td>\n\u4eac\u90fd\u5927\u5b66<\/td>\n<\/tr>\n
19<\/td>\n1993<\/td>\n\u5200\u6839\u85ab<\/td>\n\u57fc\u7389\u5927\u5b66<\/td>\n<\/tr>\n
19<\/td>\n1993<\/td>\n\u677e\u5bcc\u6b66\u96c4<\/td>\n\u524d\u8fd1\u757f\u5927\u5b66<\/td>\n<\/tr>\n
18<\/td>\n1992<\/td>\n\u5343\u4f4f\u93ae\u96c4<\/td>\n\u56fd\u969b\u5927\u5b66<\/td>\n<\/tr>\n
18<\/td>\n1992<\/td>\n\u4f9d\u7530\u6d69<\/td>\n\u540d\u53e4\u5c4b\u5de5\u696d\u5927\u5b66\u540d\u8a89\u6559\u6388<\/td>\n<\/tr>\n
17<\/td>\n1991<\/td>\n\u5c0f\u7530\u90e8\u9f4a<\/td>\n\u5171\u6804\u5de5\u696d<\/td>\n<\/tr>\n
17<\/td>\n1991<\/td>\n\u539f\u91ce\u79c0\u6c38<\/td>\n\u6771\u829d\u30a2\u30c9\u30d0\u30f3\u30b9\u30c8\u30b7\u30b9\u30c6\u30e0<\/td>\n<\/tr>\n
16<\/td>\n1990<\/td>\n\u85e4\u68ee\u8b19\u4e00<\/td>\n\u516b\u5dde\u5efa\u8a2d\u30b3\u30f3\u30b5\u30eb\u30bf\u30f3\u30c8<\/td>\n<\/tr>\n
16<\/td>\n1990<\/td>\n\u4e09\u4e0a\u64cd<\/td>\n\u4e5d\u5dde\u5927\u5b66\u540d\u8a89\u6559\u6388<\/td>\n<\/tr>\n
15<\/td>\n1989<\/td>\n\u5510\u6d25\u4e00<\/td>\n\u6771\u6d77\u5927\u5b66<\/td>\n<\/tr>\n
14<\/td>\n1988<\/td>\n\u6885\u6ca2\u8c4a<\/td>\n\u6771\u4eac\u5927\u5b66<\/td>\n<\/tr>\n
14<\/td>\n1988<\/td>\n\u9f4b\u85e4\u5609\u535a<\/td>\n(\u682a)\u65e5\u7acb\u88fd\u4f5c\u6240<\/td>\n<\/tr>\n
13<\/td>\n1987<\/td>\n\u897f\u7530\u4fca\u592b<\/td>\n\u5927\u962a\u5927\u5b66<\/td>\n<\/tr>\n
12<\/td>\n1986<\/td>\n\u8fd1\u85e4\u6b21\u90ce<\/td>\n\u65e5\u672c\u5b66\u8853\u4f1a\u8b70 \u4f1a\u9577<\/td>\n<\/tr>\n
11<\/td>\n1985<\/td>\n\u677e\u7530\u6b66\u5f66<\/td>\n\u7523\u696d\u80fd\u7387\u5927\u5b66 \u5b66\u9577<\/td>\n<\/tr>\n
10<\/td>\n1984<\/td>\n\u4e09\u6839\u4e45<\/td>\n\u95a2\u897f\u5927\u5b66<\/td>\n<\/tr>\n
9<\/td>\n1983<\/td>\n\u897f\u91ce\u5409\u6b21<\/td>\n\u65e9\u7a32\u7530\u5927\u5b66<\/td>\n<\/tr>\n
9<\/td>\n1983<\/td>\n\u65e5\u672c\u56fd\u6709\u9244\u9053<\/td>\n<\/td>\n<\/tr>\n
8<\/td>\n1982<\/td>\n\u56fd\u6fa4\u6e05\u5178<\/td>\n\u6771\u4eac\u7406\u79d1\u5927\u5b66<\/td>\n<\/tr>\n
8<\/td>\n1982<\/td>\n\u65e5\u672c\u96fb\u4fe1\u96fb\u8a71\u682a\u5f0f\u4f1a\u793e<\/td>\n<\/td>\n<\/tr>\n
7<\/td>\n1981<\/td>\n\u65e5\u672c\u30a2\u30a4\u30fb\u30d3\uff0d\u30fb\u30a8\u30e0\u682a\u5f0f\u4f1a\u793e<\/td>\n<\/td>\n<\/tr>\n
6<\/td>\n1980<\/td>\n\u6cb3\u7530\u9f8d\u592b<\/td>\n\u6176\u5fdc\u7fa9\u587e\u5927\u5b66<\/td>\n<\/tr>\n
6<\/td>\n1980<\/td>\n\u5c0f\u6797\u5b8f\u6cbb<\/td>\n\u65e5\u672c\u96fb\u6c17\u682a\u5f0f\u4f1a\u793e \u4f1a\u9577<\/td>\n<\/tr>\n
5<\/td>\n1979<\/td>\n\u6a2a\u5c71\u4fdd<\/td>\n\u5927\u962a\u5927\u5b66<\/td>\n<\/tr>\n
5<\/td>\n1979<\/td>\n\u65e5\u79d1\u6280\u9023\u30b0\u30eb\uff0d\u30d7<\/td>\n<\/td>\n<\/tr>\n
4<\/td>\n1978<\/td>\n\u68ee\u53e3\u7e41\u4e00<\/td>\n\u96fb\u6c17\u901a\u4fe1\u5927\u5b66<\/td>\n<\/tr>\n
3<\/td>\n1977<\/td>\n\u5f8c\u85e4\u6b63\u592b<\/td>\n\u53c2\u8b70\u9662\u8b70\u54e1<\/td>\n<\/tr>\n
2<\/td>\n1976<\/td>\n\u68ee\u6751\u82f1\u5178<\/td>\n\u6771\u4eac\u5de5\u696d\u5927\u5b66<\/td>\n<\/tr>\n
1<\/td>\n1975<\/td>\n\u65b0\u65e5\u672c\u88fd\u9244\u682a\u5f0f\u4f1a\u793e<\/td>\n<\/td>\n<\/tr>\n
1<\/td>\n1975<\/td>\n\u682a\u5f0f\u4f1a\u793e\u5bcc\u58eb\u9280\u884c<\/td>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
\n
\u5b9f\u65bd\u8cde Practice Award<\/h5>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u56de<\/th>\n\u5e74\u5ea6<\/th>\n\u53d7\u8cde\u8005<\/th>\n<\/tr>\n<\/thead>\n
49<\/td>\n2024<\/td>\n\u6771\u90a6\u30ac\u30b9\u682a\u5f0f\u4f1a\u793e<\/td>\n<\/tr>\n
49<\/td>\n2024<\/td>\n\u30e4\u30de\u30c8\u904b\u8f38\u682a\u5f0f\u4f1a\u793e<\/td>\n<\/tr>\n
48<\/td>\n2023<\/a><\/td>\n\u85e4\u6fa4\u514b\u6a39\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\uff0f\u4e5d\u5dde\u5927\u5b66\uff09<\/td>\n<\/tr>\n
48<\/td>\n2023<\/a><\/td>\n\u4e09\u83f1\u91cd\u5de5\u696d\u682a\u5f0f\u4f1a\u793e\u30c7\u30b8\u30bf\u30eb\u30a4\u30ce\u30d9\u30fc\u30b7\u30e7\u30f3\u672c\u90e8<\/td>\n<\/tr>\n
47<\/td>\n2022<\/a><\/td>\n\u5927\u962a\u5927\u5b66\u5927\u5b66\u9662\u60c5\u5831\u79d1\u5b66\u7814\u7a76\u79d1 \u60c5\u5831\u6570\u7406\u5b66\u5c02\u653b \u6570\u7406\u6700\u9069\u5316\u5bc4\u9644\u8b1b\u5ea7<\/td>\n<\/tr>\n
47<\/td>\n2022<\/a><\/td>\n\u682a\u5f0f\u4f1a\u793e \u65e5\u672c\u653f\u7b56\u91d1\u878d\u516c\u5eab \u56fd\u6c11\u751f\u6d3b\u4e8b\u696d\u672c\u90e8 \u30ea\u30b9\u30af\u7ba1\u7406\u90e8<\/td>\n<\/tr>\n
46<\/td>\n2021<\/a><\/td>\n\u571f\u8c37\u9686\uff08\u653f\u7b56\u7814\u7a76\u5927\u5b66\u9662\u5927\u5b66\uff09<\/td>\n<\/tr>\n
45<\/td>\n2020<\/td>\n\u8a72\u5f53\u306a\u3057<\/td>\n<\/tr>\n
44<\/td>\n2019<\/a><\/td>\n\u7b51\u6ce2\u5927\u5b66\u672a\u6765\u793e\u4f1a\u5de5\u5b66\u7814\u7a76\u30bb\u30f3\u30bf\u30fc (\u30bb\u30f3\u30bf\u30fc\u9577\u3000\u9ad9\u539f\u52c7)<\/td>\n<\/tr>\n
43<\/td>\n2018<\/a><\/td>\n\u516c\u76ca\u8ca1\u56e3\u6cd5\u4eba \u9244\u9053\u7dcf\u5408\u6280\u8853\u7814\u7a76\u6240 (\u4f1a\u9577\u3000\u6b63\u7530\u82f1\u4ecb)<\/td>\n<\/tr>\n
42<\/td>\n2017<\/a><\/td>\n\uff08\u682a\uff09\u65e5\u7acb\u88fd\u4f5c\u6240 (\u57f7\u884c\u5f79\u793e\u9577\u517cCEO\u3000\u6771\u539f\u654f\u662d)<\/td>\n<\/tr>\n
42<\/td>\n2017<\/a><\/td>\n\uff08\u682a\uff09NTT\u30c7\u30fc\u30bf\u6570\u7406\u30b7\u30b9\u30c6\u30e0 (\u4ee3\u8868\u53d6\u7de0\u5f79\u793e\u9577\u3000\u7bb1\u5b88\u8070)<\/td>\n<\/tr>\n
41<\/td>\n2016<\/a><\/td>\n\u4e5d\u5dde\u5927\u5b66\u30de\u30b9\u30fb\u30d5\u30a9\u30a2\u30fb\u30a4\u30f3\u30c0\u30b9\u30c8\u30ea\u7814\u7a76\u6240 \u5bcc\u58eb\u901a\u30bd\u30fc\u30b7\u30e3\u30eb\u6570\u7406\u5171\u540c\u7814\u7a76\u90e8\u9580<\/td>\n<\/tr>\n
40<\/td>\n2015<\/a><\/td>\n\u682a\u5f0f\u4f1a\u793e \u30d6\u30ec\u30a4\u30f3\u30d1\u30c3\u30c9<\/td>\n<\/tr>\n
39<\/td>\n2014<\/a><\/td>\n\u6771\u4eac\u30ac\u30b9 \u682a\u5f0f\u4f1a\u793e<\/td>\n<\/tr>\n
39<\/td>\n2014<\/a><\/td>\n\u682a\u5f0f\u4f1a\u793e \u4e09\u83f1UFJ\u30c8\u30e9\u30b9\u30c8\u6295\u8cc7\u5de5\u5b66\u7814\u7a76\u6240<\/td>\n<\/tr>\n
38<\/td>\n2013<\/a><\/td>\n\u30ad\u30e4\u30ce\u30f3\uff29\uff34\u30bd\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3\u30ba \u682a\u5f0f\u4f1a\u793e<\/td>\n<\/tr>\n
38<\/td>\n2013<\/a><\/td>\n\uff2a\uff26\uff25\u30b9\u30c1\u30fc\u30eb \u682a\u5f0f\u4f1a\u793e<\/td>\n<\/tr>\n
37<\/td>\n2012<\/a><\/td>\n\u5927\u962a\u30ac\u30b9 \u682a\u5f0f\u4f1a\u793e<\/td>\n<\/tr>\n
36<\/td>\n2011<\/a><\/td>\n\u682a\u5f0f\u4f1a\u793e \u69cb\u9020\u8a08\u753b\u7814\u7a76\u6240<\/td>\n<\/tr>\n
35<\/td>\n2010<\/a><\/td>\n\u8ca1\u56e3\u6cd5\u4eba \u96fb\u529b\u4e2d\u592e\u7814\u7a76\u6240<\/td>\n<\/tr>\n
34<\/td>\n2009<\/a><\/td>\n\u682a\u5f0f\u4f1a\u793e \u6771\u829d<\/td>\n<\/tr>\n
33<\/td>\n2008<\/a><\/td>\n\u682a\u5f0f\u4f1a\u793e \u65e5\u7acb\u88fd\u4f5c\u6240 \u751f\u7523\u6280\u8853\u7814\u7a76\u6240<\/td>\n<\/tr>\n
32<\/td>\n2007<\/a><\/td>\n\u682a\u5f0f\u4f1a\u793e \u91ce\u6751\u7dcf\u5408\u7814\u7a76\u6240<\/td>\n<\/tr>\n
31<\/td>\n2006<\/a><\/td>\n\u5357\u5c71\u5927\u5b66OR\u30c1\u30fc\u30e0\u300c\u30d7\u30ed\u30b8\u30a7\u30af\u30c8N\u300d<\/td>\n<\/tr>\n
30<\/td>\n2005<\/a><\/td>\n\u8ca1\u56e3\u6cd5\u4eba \u9244\u9053\u7dcf\u5408\u6280\u8853\u7814\u7a76\u6240<\/td>\n<\/tr>\n
29<\/td>\n2004<\/td>\n\u8a72\u5f53\u306a\u3057<\/td>\n<\/tr>\n
28<\/td>\n2003<\/a><\/td>\n\u682a\u5f0f\u4f1a\u793e NTT\u30c7\u30fc\u30bf<\/td>\n<\/tr>\n
27<\/td>\n2002<\/a><\/td>\n\u65e5\u672c\u30a2\u30a4\u30fb\u30d3\u30fc\u30fb\u30a8\u30e0\u682a\u5f0f\u4f1a\u793e \u6771\u4eac\u57fa\u790e\u7814\u7a76\u6240<\/td>\n<\/tr>\n
26<\/td>\n2001<\/td>\n\u4e09\u83f1\u91cd\u5de5\u696d\u682a\u5f0f\u4f1a\u793e \u9ad8\u7802\u7814\u7a76\u6240<\/td>\n<\/tr>\n
25<\/td>\n2000<\/td>\n\u65e5\u672c\u30ac\u30a4\u30b7\u682a\u5f0f\u4f1a\u793e \u958b\u767a\u30bb\u30f3\u30bf\u30fc<\/td>\n<\/tr>\n
24<\/td>\n1999<\/td>\n\u682a\u5f0f\u4f1a\u793e \u6570\u7406\u30b7\u30b9\u30c6\u30e0<\/td>\n<\/tr>\n
23<\/td>\n1998<\/td>\n\u682a\u5f0f\u4f1a\u793e \u30bc\u30af\u30bb\u30eb \u6280\u8853\u672c\u90e8<\/td>\n<\/tr>\n
22<\/td>\n1997<\/td>\n\u5bcc\u58eb\u901a\u682a\u5f0f\u4f1a\u793e \u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u4e8b\u696d\u672c\u90e8\u30df\u30c9\u30eb\u30a6\u30a7\u30a2\u4e8b\u696d\u90e8<\/td>\n<\/tr>\n
21<\/td>\n1996<\/td>\n\u79e9\u7236\u5c0f\u91ce\u7530 \u682a\u5f0f\u4f1a\u793e<\/td>\n<\/tr>\n
20<\/td>\n1995<\/td>\n\u65e5\u672c\u96fb\u4fe1\u96fb\u8a71\u682a\u5f0f\u4f1a\u793e \u7814\u7a76\u958b\u767a\u672c\u90e8<\/td>\n<\/tr>\n
19<\/td>\n1994<\/td>\n\u6771\u4eac\u30ac\u30b9 \u682a\u5f0f\u4f1a\u793e<\/td>\n<\/tr>\n
18<\/td>\n1993<\/td>\n\u682a\u5f0f\u4f1a\u793e \u5b89\u5ddd\u96fb\u6a5f<\/td>\n<\/tr>\n
17<\/td>\n1992<\/td>\n\u677e\u4e0b\u96fb\u5de5 \u682a\u5f0f\u4f1a\u793e<\/td>\n<\/tr>\n
16<\/td>\n1991<\/td>\n\u682a\u5f0f\u4f1a\u793e \u69cb\u9020\u8a08\u753b\u7814\u7a76\u6240<\/td>\n<\/tr>\n
15<\/td>\n1990<\/td>\n\u682a\u5f0f\u4f1a\u793e \u65e5\u901a\u7dcf\u5408\u7814\u7a76\u6240<\/td>\n<\/tr>\n
14<\/td>\n1989<\/td>\n\u7530\u8fba\u88fd\u85ac \u682a\u5f0f\u4f1a\u793e<\/td>\n<\/tr>\n
13<\/td>\n1988<\/td>\n\u8a72\u5f53\u306a\u3057<\/td>\n<\/tr>\n
12<\/td>\n1987<\/td>\n\u8a72\u5f53\u306a\u3057<\/td>\n<\/tr>\n
11<\/td>\n1986<\/td>\n\u65e5\u672c\u96fb\u6c17 \u682a\u5f0f\u4f1a\u793e<\/td>\n<\/tr>\n
10<\/td>\n1985<\/td>\n\u8a72\u5f53\u306a\u3057<\/td>\n<\/tr>\n
9<\/td>\n1984<\/td>\n\u682a\u5f0f\u4f1a\u793e \u6771\u829d<\/td>\n<\/tr>\n
8<\/td>\n1983<\/td>\n\u5ddd\u5d0e\u88fd\u9244 \u682a\u5f0f\u4f1a\u793e<\/td>\n<\/tr>\n
7<\/td>\n1982<\/td>\n\u4f4f\u53cb\u91d1\u5c5e\u5de5\u696d \u682a\u5f0f\u4f1a\u793e<\/td>\n<\/tr>\n
7<\/td>\n1982<\/td>\n\u4e09\u83f1\u77f3\u6cb9\u682a \u5f0f\u4f1a\u793e<\/td>\n<\/tr>\n
6<\/td>\n1981<\/td>\n\u8ca1\u56e3\u6cd5\u4eba \u96fb\u529b\u4e2d\u592e\u7814\u7a76\u6240<\/td>\n<\/tr>\n
5<\/td>\n1980<\/td>\n\u4e2d\u56fd\u96fb\u529b \u682a\u5f0f\u4f1a\u793e<\/td>\n<\/tr>\n
4<\/td>\n1979<\/td>\n\u5175\u5eab\u770c<\/td>\n<\/tr>\n
3<\/td>\n1978<\/td>\n\u4e2d\u90e8\u96fb\u529b \u682a\u5f0f\u4f1a\u793e<\/td>\n<\/tr>\n
2<\/td>\n1977<\/td>\n\u682a\u5f0f\u4f1a\u793e \u65e5\u7acb\u88fd\u4f5c\u6240<\/td>\n<\/tr>\n
1<\/td>\n1976<\/td>\n\u6771\u4e9c\u71c3\u6599 \u682a\u5f0f\u4f1a\u793e<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
\n
\u4e8b\u4f8b\u7814\u7a76\u8cde Case Study Award<\/h5>\n

\u203b\u7b2c20\u56de\u307e\u3067\u306f\u4e8b\u4f8b\u7814\u7a76\u5968\u52b1\u8cde<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u56de<\/th>\n\u5e74\u5ea6<\/th>\n\u53d7\u8cde\u8005<\/th>\n\u5bfe\u8c61\u7814\u7a76<\/th>\n\u63b2\u8f09\u8a8c<\/th>\n<\/tr>\n<\/thead>\n
45<\/td>\n2025<\/td>\n\u5c0f\u718a\u7950\u53f8\uff0c\u677e\u672c\u62d3\u54c9\uff08\u682a\u5f0f\u4f1a\u793eIHI\uff09\uff0c\u5009\u7530\u512a\u5fd7\uff08\u682a\u5f0f\u4f1a\u793eIHI\u7269\u6d41\u7523\u696d\u30b7\u30b9\u30c6\u30e0\uff09<\/td>\n\u7269\u6d41\u5009\u5eab\u30d1\u30ec\u30bf\u30a4\u30ba\u30b7\u30b9\u30c6\u30e0\u5411\u3051\u6df7\u8f09\u7a4d\u307f\u4ed8\u3051\u8a08\u753b\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u958b\u767a<\/td>\n\u65e5\u672c\u30aa\u30da\u30ec\u30fc\u30b7\u30e7\u30f3\u30ba\u30fb\u30ea\u30b5\u30fc\u30c1\u5b66\u4f1a2024\u5e74\u6625\u5b63\u7814\u7a76\u767a\u8868\u4f1a\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8\u96c6, 2-E-1 (\u4f01\u696d\u4e8b\u4f8b\u4ea4\u6d41\u4f1a)<\/td>\n<\/tr>\n
45<\/td>\n2025<\/td>\n\u7530\u53e3\u6771\uff0c\u67ff\u5d0e\u967d\uff08\u682a\u5f0f\u4f1a\u793e\u30d9\u30af\u30c8\u30eb\u7dcf\u7814\uff09<\/td>\n\u6771\u4eacBRT(Bus Rapid Transit)\u3068\u5730\u4e0b\u92448\u53f7\u7dda\u5efa\u8a2d\u306b\u3088\u308b\u4ea4\u901a\u5229\u4fbf\u6027\u5411\u4e0a\u306e\u8a66\u7b97<\/td>\n\u65e5\u672c\u30aa\u30da\u30ec\u30fc\u30b7\u30e7\u30f3\u30ba\u30fb\u30ea\u30b5\u30fc\u30c1\u5b66\u4f1a2024\u5e74\u6625\u5b63\u7814\u7a76\u767a\u8868\u4f1a\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8\u96c6\uff0cpp.164-165, 2024<\/td>\n<\/tr>\n
45<\/td>\n2025<\/td>\n\u682a\u5f0f\u4f1a\u793e\u65e5\u7acb\u88fd\u4f5c\u6240\uff08\u7814\u7a76\u4ee3\u8868: \u702c\u6238\u660e\u5dba\uff0c\u7d30\u7530\u9806\u5b50\uff09<\/td>\nOR\u306b\u3088\u308b\u8f38\u914d\u9001\u696d\u52d9\u306e\u8ab2\u984c\u89e3\u6c7a<\/td>\nOR Vol.69, No.11, pp.606-611, 2024<\/a><\/td>\n<\/tr>\n
45<\/td>\n2025<\/td>\n\u682a\u5f0f\u4f1a\u793e\u535a\u5831\u5802\u30c6\u30af\u30ce\u30ed\u30b8\u30fc\u30ba\uff08\u7814\u7a76\u4ee3\u8868: \u5ddd\u4e0a\u5b5d\u4ecb\uff0c\u77f3\u585a\u6e56\u592a\uff09\uff0c\u99ac\u5d8b\u6d77\u6597\uff0c\u4e2d\u7530\u548c\u79c0\uff08\u6771\u4eac\u79d1\u5b66\u5927\u5b66\uff09<\/td>\nKeyword-Level Bayesian Online Bid Optimization for Sponsored Search Advertising<\/td>\nOperations Research Forum, Vol.5, No.2, pp.1-32. Springer International Publishing, 2024<\/td>\n<\/tr>\n
44<\/td>\n2024<\/a><\/td>\nFracta Leap \u682a\u5f0f\u4f1a\u793e\uff08\u7814\u7a76\u4ee3\u8868: \u8acb\u5ddd\u514b\u4e4b, \u6751\u4e95\u771f\u4e5f\uff09<\/td>\n\u6c34\u51e6\u7406\u30d7\u30e9\u30f3\u30c8\u5185\u306e\u88c5\u7f6e\u306b\u5bfe\u3059\u308b\u914d\u7f6e\u8a2d\u8a08\u306b\u95a2\u3059\u308b\u53d6\u308a\u7d44\u307f<\/td>\nOR Vol. 68, No. 8\uff0cpp. 415\u2013420, 2023<\/a><\/td>\n<\/tr>\n
44<\/td>\n2024<\/a><\/td>\n\u5c0f\u6cc9\u5321\u79c0 \u6c0f\uff08\u6771\u6d77\u65c5\u5ba2\u9244\u9053\u682a\u5f0f\u4f1a\u793e\uff09, \u592a\u7530\u6709\u4eba \u6c0f\uff08\u65e5\u9244\u30bd\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3\u30ba\u682a\u5f0f\u4f1a\u793e\uff09<\/td>\n\u6570\u7406\u6700\u9069\u5316\u8a08\u7b97\u306b\u3088\u308b\u6771\u6d77\u9053\u65b0\u5e79\u7dda\u306e\u8eca\u4e21\u904b\u7528\u81ea\u52d5\u4f5c\u6210\u6280\u8853\u306e\u78ba\u7acb<\/td>\n\u65e5\u672c\u30aa\u30da\u30ec\u30fc\u30b7\u30e7\u30f3\u30ba\u30fb\u30ea\u30b5\u30fc\u30c1\u5b66\u4f1a2023\u5e74\u79cb\u5b63\u7814\u7a76\u767a\u8868\u4f1a\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8\u96c6, 1-F-6 (\u4f01\u696d\u4e8b\u4f8b\u4ea4\u6d41\u4f1a)<\/td>\n<\/tr>\n
44<\/td>\n2024<\/a><\/td>\n\u690e\u540d\u840c, \u9ad8\u91ce\u7950\u4e00\uff08\u7b51\u6ce2\u5927\u5b66\uff09, \u5b87\u4f50\u7f8e\u670b\u9999, \u5c71\u897f\u5eb7\u5b5d, \u85e4\u5dfb\u7c73\u9686\uff08\u682a\u5f0f\u4f1a\u793e\u30eb\u30ea\u30a2\u30f3\uff09<\/td>\n\u6df7\u5408\u6574\u6570\u6700\u9069\u5316\u306b\u3088\u308b\u76f8\u7d9a\u5de5\u7a0b\u306e\u9577\u671f\u5316\u30ea\u30b9\u30af\u63a1\u70b9\u30b7\u30b9\u30c6\u30e0<\/td>\nOR Vol. 69, No. 4\uff0cpp. 219\u2013224, 2024<\/a><\/td>\n<\/tr>\n
43<\/td>\n2023<\/a><\/td>\n\u5ddd\u672c\u5353\u6a39\uff0c\u84ee\u6c60\u9686 (\u65e9\u7a32\u7530\u5927\u5b66)<\/td>\n\u30dc\u30ed\u30ce\u30a4\u5206\u5272\u3092\u7528\u3044\u305f\u52a0\u76df\u5e97\u8217\u3078\u306e\u52d5\u7684\u306a\u6ce8\u6587\u5272\u308a\u5f53\u3066\u624b\u6cd5<\/td>\nOR Vol. 67, No. 11\uff0cpp. 619\u2013630, 2022<\/a><\/td>\n<\/tr>\n
43<\/td>\n2023<\/a><\/td>\n\u5409\u826f\u77e5\u6587 (\u7fa4\u99ac\u5927\u5b66)\uff0c\u5bfa\u5cf6\u4f38\u7537\uff0c\u6e21\u9089\u5b89\u5f66 (\u65e5\u672c\u30d1\u30ec\u30c3\u30c8\u30ec\u30f3\u30bf\u30eb\u682a\u5f0f\u4f1a\u793e)\uff0c\u5c71\u672c\u5e83\u9ad8 (\u682a\u5f0f\u4f1a\u793eTHINCESS)<\/td>\nOR\u3067\u652f\u63f4\uff1a\u7269\u6d41\u696d\u754c\u304c\u76f4\u9762\u3059\u308b\u7af6\u4e89\u304b\u3089\u5171\u5275\u3078\u306e\u8ee2\u63db<\/td>\n\u7b2c34\u56deRAMP\u6570\u7406\u6700\u9069\u5316\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u4e88\u7a3f\u96c6, pp. 137-152, 2022.<\/td>\n<\/tr>\n
43<\/td>\n2023<\/a><\/td>\n\u4e09\u83f1\u96fb\u6a5f\u682a\u5f0f\u4f1a\u793e\u96fb\u5b50\u901a\u4fe1\u30b7\u30b9\u30c6\u30e0\u88fd\u4f5c\u6240 (\u7814\u7a76\u4ee3\u8868\uff1a\u677e\u7530\u77e5\u4e5f)<\/td>\n\u5bfe\u6d41\u570f\u30a6\u30a3\u30f3\u30c9\u30d7\u30ed\u30d5\u30a1\u30a4\u30e9\u306e\u958b\u767a\u3068\u6c17\u8c61\u89b3\u6e2c\u3067\u306e\u5b9f\u7528\u5316<\/td>\n\u65e5\u672c\u30aa\u30da\u30ec\u30fc\u30b7\u30e7\u30f3\u30ba\u30fb\u30ea\u30b5\u30fc\u30c1\u5b66\u4f1a2022\u5e74\u79cb\u5b63\u7814\u7a76\u767a\u8868\u4f1a\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8\u96c6, 1-F-4 (\u4f01\u696d\u4e8b\u4f8b\u4ea4\u6d41\u4f1a)<\/td>\n<\/tr>\n
42<\/td>\n2022<\/a><\/td>\n\u5343\u4ee3\u7adc\u4f51\u6c0f\uff08ZOZO\u7814\u7a76\u6240\uff09<\/td>\n\u7269\u6d41\u5009\u5eab\u62e1\u5f35\u5f8c\u306e\u62e0\u70b9\u9593\u8f38\u9001\u3092\u6700\u5c0f\u5316\u3059\u308b\u5728\u5eab\u914d\u7f6e<\/td>\n\u65e5\u672c\u30aa\u30da\u30ec\u30fc\u30b7\u30e7\u30f3\u30ba\u30fb\u30ea\u30b5\u30fc\u30c1\u5b66\u4f1a\uff0c 2021\u5e74\u79cb\u5b63\u7814\u7a76\u767a\u8868\u4f1a\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8\u96c6\uff0c 1-E-7<\/td>\n<\/tr>\n
41<\/td>\n2021<\/td>\n\u8a72\u5f53\u306a\u3057<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
40<\/td>\n2020<\/td>\n\u8a72\u5f53\u306a\u3057<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
39<\/td>\n2019<\/a><\/td>\n\u4e95\u51fa\u967d\u5b50\uff0c\u592a\u7530\u88d5\u6a39\uff0c\u8302\u4e2d\u4fca\u660e\uff0c\u77f3\u4e95\u4f38\u4e5f(\u4e09\u83f1\u91cd\u5de5\u696d\u682a\u5f0f\u4f1a\u793e)\uff0c\u7267\u91ce\u548c\u4e45(\u4eac\u90fd\u5927\u5b66)<\/td>\n\u30ab\u30fc\u30b7\u30a7\u30a2\u30ea\u30f3\u30b0\u306e\u8eca\u4e21\u914d\u9001\u8a08\u753b\u306e\u6700\u9069\u5316<\/td>\n\u65e5\u672c\u30aa\u30da\u30ec\u30fc\u30b7\u30e7\u30f3\u30ba\u30fb\u30ea\u30b5\u30fc\u30c1\u5b66\u4f1a\uff0c 2017\u5e74\u6625\u5b63\u7814\u7a76\u767a\u8868\u4f1a\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8\u96c6\uff0c 29-30<\/td>\n<\/tr>\n
38<\/td>\n2018<\/a><\/td>\n\u8d64\u5800\u5cfb((\u682a)\u65e5\u7acb\u88fd\u4f5c\u6240)\uff0c\u95a2\u53e3\u967d\u4ecb(\u30b7\u30b9\u30e1\u30c3\u30af\u30b9(\u682a))\uff0c\u7530\u6751\u660e\u4e45(\u6176\u61c9\u7fa9\u587e\u5927\u5b66)<\/td>\n\u5b66\u751f\u306b\u30b0\u30eb\u30fc\u30d7\u5206\u3051\u306e\u3042\u308b\u5b66\u79d1\u914d\u5c5e\u554f\u984c\u2015\u96e2\u6563\u51f8\u89e3\u6790\u306e\u9069\u7528\u4f8b<\/td>\nTORSJ Vol. 60\uff0c pp. 50\u201373\uff0c 2017<\/td>\n<\/tr>\n
37<\/td>\n2017<\/a><\/td>\n\u5df3\u6ce2\u5f18\u4f73(\u95a2\u897f\u5b66\u9662\u5927\u5b66)<\/td>\n\u6642\u8a08\u53f0\u30d7\u30ed\u30b8\u30a7\u30af\u30b7\u30e7\u30f3\u30de\u30c3\u30d4\u30f3\u30b0\u30d7\u30ed\u30b8\u30a7\u30af\u30c8<\/td>\nOR Vol.61\uff0cNo.11\uff0cpp.782\u2013786\uff0c2016<\/a><\/td>\n<\/tr>\n
36<\/td>\n2016<\/a><\/td>\n\u5c71\u6839\u667a\u4e4b\uff0c\u83c5\u539f\u5149\u592a\u90ce\uff0c\u897f\u6751\u76f4\u6a39\uff0c\u5c0f\u6797\u5065\uff0c\u5409\u7530\u4f51\u8f14(\u6771\u4eac\u5de5\u696d\u5927\u5b66)\uff0c\u9ad8\u91ce\u7950\u4e00(\u5c02\u4fee\u5927\u5b66)\uff0c\u4e2d\u7530\u548c\u79c0(\u6771\u4eac\u5de5\u696d\u5927\u5b66)<\/td>\n\u6642\u7cfb\u5217\u30e2\u30c7\u30eb\u306b\u3088\u308b\u5546\u54c1\u8ca9\u4fc3\u52b9\u679c\u306e\u5206\u6790<\/td>\n\u5e73\u6210 26 \u5e74\u5ea6\u30c7\u30fc\u30bf\u89e3\u6790\u30b3\u30f3\u307a\u30c6\u30a3\u30b7\u30e7\u30f32015\u5e743\u6708\uff0cOR Vol.61\uff0cNo.2\uff0cpp.65-70\uff0c2016<\/a><\/td>\n<\/tr>\n
35<\/td>\n2015<\/a><\/td>\n\u4e2d\u539f\u5b5d\u4fe1(\u5c02\u4fee\u5927\u5b66)\uff0c\u7fbd\u5ba4\u884c\u4fe1(\u95a2\u897f\u5b66\u9662\u5927\u5b66)\uff0c\u5b87\u91ce\u6bc5\u660e(\u56fd\u7acb\u60c5\u5831\u5b66\u7814\u7a76\u6240\u4f1a)\uff0c\u5317\u5cf6\u8061((\u682a)KSK\u30a2\u30ca\u30ea\u30c6\u30a3\u30af\u30b9)<\/td>\n\u6d88\u8cbb\u8005\u30de\u30a4\u30f3\u30c9\u306e\u6982\u5ff5\u5316\u3068\u5206\u985e\u30e2\u30c7\u30eb\u751f\u6210<\/td>\n\u5e73\u6210 25 \u5e74\u5ea6\u30c7\u30fc\u30bf\u89e3\u6790\u30b3\u30f3\u307a\u30c6\u30a3\u30b7\u30e7\u30f32014\u5e743\u6708<\/td>\n<\/tr>\n
35<\/td>\n2015<\/a><\/td>\n\u5c71\ufa11\u8aed\uff0c\u5c0f\u5e02\u4fca\u609f\uff0c\u9234\u6728\u6566\u592b(\u5357\u5c71\u5927\u5b66)<\/td>\n\u707d\u5bb3\u6642\u306e\u4ee3\u66ff\u7d4c\u8def\u306e\u78ba\u4fdd\u3092\u8003\u616e\u3057\u305f\u9053\u8def\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u69cb\u7bc9\u6cd5<\/td>\nTORSJ\uff0cVol. 56\uff0c pp. 31-52\uff0c 2013<\/td>\n<\/tr>\n
34<\/td>\n2014<\/a><\/td>\n\u5ca9\u6c38\u4e8c\u90ce\uff0c\u934b\u8c37\u6634\u4e00\uff0c\u68b6\u539f\u60a0\uff0c\u4e94\u5341\u5d50\u5065\u592a((\u682a)NTT\u30c7\u30fc\u30bf\u6570\u7406\u30b7\u30b9\u30c6\u30e0)<\/td>\n\u95a2\u5fc3\u5ea6\u3068\u5fd8\u5374\u5ea6\u306b\u57fa\u3065\u304f\u30ec\u30b3\u30e1\u30f3\u30c9\u624b\u6cd5\uff0d\u5358\u8abf\u6027\u5236\u7d04\u4ed8\u304d\u30ec\u30b3\u30e1\u30f3\u30c9\u30e2\u30c7\u30eb\u306e\u69cb\u7bc9\uff0d<\/td>\n\u5e73\u6210 24 \u5e74\u5ea6\u30c7\u30fc\u30bf\u89e3\u6790\u30b3\u30f3\u307a\u30c6\u30a3\u30b7\u30e7\u30f32013\u5e743\u6708<\/td>\n<\/tr>\n
34<\/td>\n2014<\/a><\/td>\n\u5df3\u6ce2\u5f18\u4f73(\u95a2\u897f\u5b66\u9662\u5927\u5b66)\uff0c\u53e4\u5c4b\u664b\u4e00(\u4e0a\u667a\u5927\u5b66)\uff0c\u9577\u7530\u5178\u5b50(\u95a2\u897f\u5b66\u9662\u5927\u5b66)<\/td>\n\u30d4\u30a2\u30ce\u6f14\u594f\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30b0\u30e9\u30d5\u30a3\u30af\u30b9\u5236\u4f5c\u6280\u8853<\/td>\nOR Vol. 58\uff0c No.3 pp. 149-155\uff0c 2013<\/a><\/td>\n<\/tr>\n
33<\/td>\n2013<\/a><\/td>\n\u9b8f\u5ddd\u77e9\u7fa9(\u6771\u4eac\u5de5\u696d\u5927\u5b66)\uff0c\u6b63\u6728\u4fca\u884c\uff0c\u4f0a\u8c46\u6c38\u6d0b\u4e00\uff0c\u4f50\u85e4\u4fca\u6a39\uff0c\u77f3\u6ff1\u53cb\u88d5\uff0c\u7530\u4e2d\u5f70\u6d69\uff0c\u4e2d\u5cf6\u96c4\u57fa\uff0c\u821f\u6a4b\u53f2\u660e(\u7b51\u6ce2\u5927\u5b66)<\/td>\n\u30a2\u30af\u30bb\u30b9\u30ed\u30b0\u30c7\u30fc\u30bf\u53ef\u8996\u5316\u306e\u8a66\u307f<\/td>\n\u5e73\u621023\u5e74\u5ea6\u30c7\u30fc\u30bf\u89e3\u6790\u30b3\u30f3\u30da\u30c6\u30a3\u30b7\u30e7\u30f32012\u5e743\u6708<\/td>\n<\/tr>\n
32<\/td>\n2012<\/a><\/td>\n\u7530\u4e2d\u5065\u4e00(\u96fb\u6c17\u901a\u4fe1\u5927\u5b66)\uff0c\u53e4\u7530\u58ee\u5b8f(\u6771\u4eac\u7406\u79d1\u5927\u5b66)<\/td>\n\u8907\u6570\u56de\u306e\u6355\u6349\u3092\u8003\u616e\u3057\u305f\u30d5\u30ed\u30fc\u6355\u6349\u578b\u914d\u7f6e\u554f\u984c\u3068\u9244\u9053\u6d41\u52d5\u30c7\u30fc\u30bf\u3078\u306e\u9069\u7528\u2015\u4eac\u738b\u7dda\u3068\u5c71\u624b\u7dda\u3092\u4e8b\u4f8b \u3068\u3057\u3066\u2015<\/td>\nOR\u3000Vol.56\uff0c No.3\uff0cpp. 166-174\uff0c2011<\/a><\/td>\n<\/tr>\n
32<\/td>\n2012<\/a><\/td>\n\u52dd\u53c8\u58ee\u592a\u90ce(\u9577\u5d0e\u5927\u5b66)\uff0c\u963f\u90e8\u8aa0(\u6771\u4eac\u5927\u5b66)<\/td>\n\u5b66\u7fd2\u306b\u3088\u308b\u5185\u90e8\u5316\u306e\u30e2\u30c7\u30eb\u2015\u9078\u629e\u80a2\u306e\u7d5e\u308a\u8fbc\u307f\u904e\u7a0b\u3092\u3069\u3046\u7d44\u307f\u8fbc\u3080\u304b\u2015<\/td>\n\u5e73\u621022\u5e74\u5ea6\u30c7\u30fc\u30bf\u89e3\u6790\u30b3\u30f3\u30da\u30c6\u30a3\u30b7\u30e7\u30f3\u4e00\u822c\u90e8\u9580\u6700\u512a\u79c0\u8cde<\/td>\n<\/tr>\n
31<\/td>\n2011<\/td>\n\u539f\u53e3\u548c\u4e5f(\u77f3\u5dfb\u5c02\u4fee\u5927\u5b66)\uff0c\u4f50\u85e4\u7950\u4e00(\u6ecb\u8cc0\u770c\u7435\u7436\u6e56\u74b0\u5883\u79d1\u5b66\u7814\u7a76\uff7e\uff9d\uff80\uff70)<\/td>\n\u7435\u7436\u6e56\u6e56\u6c34\u89b3\u6e2c\u5730\u70b9\u306e\u914d\u7f6e\u554f\u984c\u306e\u7814\u7a76<\/td>\nJORSJ Vol. 53\uff0c No. 4<\/td>\n<\/tr>\n
31<\/td>\n2011<\/a><\/td>\n\u5317\u53e4\u8cc0\u572d\u7950(\u65e9\u7a32\u7530\u5927\u5b66)\uff0c\u4eca\u6cc9\u6df3(\u6771\u6d0b\u5927\u5b66)\uff0c\u91cd\u7530\u82f1\u8cb4(\u65e5\u672c\u8ca8\u7269\u9244\u9053(\u682a))\uff0c\u68ee\u6238\u664b(\u65e9\u7a32\u7530\u5927\u5b66)<\/td>\n\u6a5f\u95a2\u8eca\u306e\u57fa\u5730\u5185\u7559\u7f6e\u8a08\u753b\u306b\u5bfe\u3059\u308b\u6574\u6570\u8a08\u753b\u30a2\u30d7\u30ed\u30fc\u30c1<\/td>\nOR\u3000Vol.55\uff0c No.2\uff0cpp.121-127\uff0c2010<\/a><\/td>\n<\/tr>\n
31<\/td>\n2011<\/a><\/td>\n\u9ce5\u6d77\u91cd\u559c(\u4e2d\u592e\u5927\u5b66)<\/td>\n\u6d77\u4e0a\u822a\u8def\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u7528\u3044\u305f\u30b3\u30f3\u30c6\u30ca\u8239\u306e\u904b\u822a\u30d1\u30bf\u30fc\u30f3\u5206\u6790<\/td>\nOR\u3000Vol.55\uff0c No.6\uff0cpp.359-367\uff0c2010<\/a><\/td>\n<\/tr>\n
30<\/td>\n2010<\/a><\/td>\n\u5c71\u672c\u4f73\u5948\uff0c\u9234\u6728\u6566\u592b(\u5357\u5c71\u5927\u5b66)<\/td>\n\u5357\u5c71\u5927\u5b66\u306b\u304a\u3051\u308b\u5165\u8a66\u76e3\u7763\u8005\u81ea\u52d5\u5272\u5f53\u30b7\u30b9\u30c6\u30e0\u306e\u4f5c\u6210<\/td>\nOR\u3000Vol.54\uff0c No.6\uff0cpp.335-341\uff0c2009<\/a><\/td>\n<\/tr>\n
30<\/td>\n2010<\/a><\/td>\n\u4e09\u6d66\u82f1\u4fca(\u660e\u6d77\u5927\u5b66)<\/td>\n\u30ed\u30b8\u30c3\u30c8\u30e2\u30c7\u30eb\u3092\u7528\u3044\u305f\u30ea\u30cb\u30a2\u4e2d\u592e\u65b0\u5e79\u7dda\u306e\u9700\u8981\u4e88\u6e2c<\/td>\nOR\u3000Vol.54\uff0c No.7\uff0cpp.419-428\uff0c2009<\/a><\/td>\n<\/tr>\n
29<\/td>\n2009<\/a><\/td>\n\u6fa4\u4e95\u8ce2\u4e00(\u6771\u4eac\u5927\u5b66)\uff0c\u9ed2\u6728\u88d5\u4ecb(\u3231\u6771\u829d)\uff0c\u677e\u4e95\u77e5\u5df1(\u4e2d\u592e\u5927\u5b66)<\/td>\n\u30d5\u30eb\u30fc\u30c8\u306e\u904b\u6307\u6700\u9069\u5316\u3068\u9006\u6700\u9069\u5316\u3092\u7528\u3044\u305f\u30d1\u30e9\u30e1\u30fc\u30bf\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0<\/td>\nOR\u3000vol.53\uff0cNo.1\uff0cpp.39-46\uff0c2008<\/a><\/td>\n<\/tr>\n
29<\/td>\n2009<\/a><\/td>\n\u9ce5\u6d77\u91cd\u559c(\u4e2d\u592e\u5927\u5b66)\uff0c\u5ddd\u53e3\u771f\u7531(\u5168\u65e5\u7a7a\uff7c\uff7d\uff83\uff91\u4f01\u753b\u3231)\uff0c\u7530\u53e3\u6771(\u4e2d\u592e\u5927\u5b66)<\/td>\n\u9996\u90fd\u76f4\u4e0b\u5730\u9707\u306b\u3088\u308b\u9244\u9053\u5229\u7528\u901a\u52e4\u30fb\u901a\u5b66\u5ba2\u306e\u88ab\u5bb3\u60f3\u5b9a<\/td>\nOR\u3000Vol.53\uff0cNo.2\uff0cpp.111-118\uff0c2008<\/a><\/td>\n<\/tr>\n
29<\/td>\n2009<\/a><\/td>\n\u95a2\u5eb8\u4e00\uff0c\u9577\u4e95\u6b69\uff0c\u963f\u5de6\u7f8e\u5c1a\u5fd7(\u7fa4\u99ac\u5927\u5b66)<\/td>\n\u6975\u5024\u56de\u5e30\u30e2\u30c7\u30eb\u306b\u3088\u308b\u81ea\u52d5\u8eca\u30aa\u30fc\u30af\u30b7\u30e7\u30f3\u306b\u304a\u3051\u308b\u843d\u672d\u4fa1\u683c\u5206\u5e03\u306e\u5206\u6790<\/td>\n\u5e73\u621019\u5e74\u5ea6\u30c7\u30fc\u30bf\u89e3\u6790\u30b3\u30f3\u30da\u30c6\u30a3\u30b7\u30e7\u30f3<\/td>\n<\/tr>\n
28<\/td>\n2008<\/a><\/td>\n\u677e\u7530\u8d70\u4e00\u90ce\uff0c\u5c0f\u6fa4\u6b63\u5178\uff0c\u68ee\u96c5\u592b(\u6176\u61c9\u7fa9\u587e\u5927\u5b66)<\/td>\n\u88c1\u5224\u54e1\u5236\u5ea6\u306b\u304a\u3051\u308b\u5224\u6c7a\u306e\u4fe1\u983c\u6027<\/td>\nOR\u3000Vol.52, No.12\uff0cpp.780-784\uff0c2007<\/a><\/td>\n<\/tr>\n
28<\/td>\n2008<\/a><\/td>\n\u7fbd\u5ba4\u884c\u4fe1\uff0c\u5c71\u672c\u662d\u4e8c\uff0c\u4e2d\u897f\u6b63\u96c4(\u95a2\u897f\u5b66\u9662\u5927\u5b66)<\/td>\nEmerging Sequence Pattern\u306b\u57fa\u3065\u304fWeb\u30a2\u30af\u30bb\u30b9\u30ed\u30b0\u30c7\u30fc\u30bf\u304b\u3089\u306e\u77e5\u8b58\u767a\u898b<\/td>\n\u5e73\u621018\u5e74\u5ea6\u30c7\u30fc\u30bf\u89e3\u6790\u30b3\u30f3\u30da\u30c6\u30a3\u30b7\u30e7\u30f3<\/td>\n<\/tr>\n
27<\/td>\n2007<\/a><\/td>\n\u4f4f\u7530\u6f6e\uff0c\u68ee\u4fca\u6a39\uff0c\u6589\u85e4\u6643\u4e00\uff0c\u9ad8\u6a4b\u4e00\u6a39\uff0c\u83c5\u8c37\u5065\u4eba\uff0c\u5c0f\u6c60\u96c4\u5e73\uff0c\u5e73\u91ce\u667a\u7ae0(\u7b51\u6ce2\u5927\u5b66)<\/td>\n\u6d88\u8cbb\u8005\u884c\u52d5\u306b\u57fa\u3065\u304f\u5546\u54c1\u30d6\u30e9\u30f3\u30c9\u306e\u69cb\u9020\u5206\u6790<\/td>\n\u5e73\u621017\u5e74\u5ea6\u30c7\u30fc\u30bf\u89e3\u6790\u30b3\u30f3\u30da\u30c6\u30a3\u30b7\u30e7\u30f3<\/td>\n<\/tr>\n
27<\/td>\n2007<\/a><\/td>\n\u9ad8\u6851\u5b97\u53f3\u30f1\u9580\uff0c\u4e09\u8f2a\u51a0\u5948(\u540d\u53e4\u5c4b\u5927\u5b66)<\/td>\n\u30bb\u30eb\u751f\u7523\u30fb\u6c34\u3059\u307e\u3057\u30fb\u304b\u3093\u3070\u3093\u65b9\u5f0f\u63f4\u7528\u751f\u7523\u30b7\u30b9\u30c6\u30e0\u306b\u304a\u3051\u308b\u90e8\u54c1\u5728\u5eab\u7ba1\u7406\u306e\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u6700\u9069\u5316<\/td>\nOR\u3000Vol.51\uff0cNo.7\uff0cpp.445-453\uff0c2006<\/a><\/td>\n<\/tr>\n
27<\/td>\n2007<\/a><\/td>\n\u5ee3\u6d25\u4fe1\u7fa9(\u9806\u5929\u5802\u5927\u5b66)\uff0c\u79cb\u5c71\u5927\u8f14\uff0c\u4e0a\u7530\u5fb9(\u6210\u8e4a\u5927\u5b66)<\/td>\n\u30b5\u30c3\u30ab\u30fc\u9078\u624b\u306eDEA\u306e\u8996\u70b9\u304b\u3089\u306e\u8a55\u4fa1<\/td>\nOR\u3000Vol.51\uff0cNo.10\uff0cpp.655-661\uff0c2006<\/a><\/td>\n<\/tr>\n
26<\/td>\n2006<\/a><\/td>\n\u30aa\u30a6\u30ed(\u6771\u4eac\u6d77\u4e0a\u65e5\u52d5\u706b\u707d\u4fdd\u967a)\uff0c\u5409\u539f\u4e9c\u5f25\uff0c\u77e2\u5cf6\u5b89\u654f(\u6771\u4eac\u5de5\u696d\u5927\u5b66)<\/td>\n\u767e\u8ca8\u5e97\u306b\u304a\u3051\u308b\u96a0\u308c\u305f\u89aa\u8fd1\u6027\u306e\u767a\u6398<\/td>\nOR\u3000Vol.50\uff0cNo.2<\/td>\n<\/tr>\n
26<\/td>\n2006<\/a><\/td>\n\u4e95\u968e\u7f8e\u6b69\uff0c\u9ad8\u6a4b\u5f70\u5b50\uff0c\u4e2d\u5ddd\u6176\u4e00\u90ce\uff0c\u77e2\u91ce\u9806\u5b50\uff0c\u5c71\u4e2d\u5553\u4e4b(NTT\u30c7\u30fc\u30bf)\uff0c\u751f\u7530\u76ee\u5d07(\u5c02\u4fee\u5927\u5b66)<\/td>\n\u96fb\u529b\u6d88\u8cbb\u306e\u30e2\u30cb\u30bf\u30ea\u30f3\u30b0\u30fb\u30c7\u30fc\u30bf\u3092\u7528\u3044\u305f\u7701\u30a8\u30cd\u30fb\u30a2\u30c9\u30d0\u30a4\u30b9\u65b9\u6cd5\u306e\u63d0\u6848<\/td>\nOR\u3000Vol.50\uff0cNo.2<\/td>\n<\/tr>\n
26<\/td>\n2006<\/a><\/td>\n\u9ce5\u6d77\u91cd\u559c(\u4e2d\u592e\u5927\u5b66)\uff0c\u4e2d\u6751\u5e78\u53f2(\u30d1\u30a4\u30aa\u30cb\u30a2)\uff0c\u7530\u53e3\u6771(\u4e2d\u592e\u5927\u5b66)<\/td>\n\u901a\u52e4\u96fb\u8eca\u306e\u9045\u5ef6\u8a08\u7b97\u30e2\u30c7\u30eb<\/td>\nOR\u3000Vol.50\uff0cNo.6<\/td>\n<\/tr>\n
25<\/td>\n2005<\/a><\/td>\n\u5ee3\u6d25\u4fe1\u7fa9\uff0c\u5bae\u5730\u529b(\u56fd\u7acb\u30b9\u30dd\u30fc\u30c4\u79d1\u5b66\u30bb\u30f3\u30bf-)<\/td>\n\u91ce\u7403\u30c1\u30fc\u30e0\u306e\u30e9\u30a4\u30f3\u30ca\u30c3\u30d7\u9078\u5b9a\u306e\u305f\u3081\u306e\u6570\u7406\u7684\u4e00\u624b\u6cd5 \uff0d\u65e5\u672c\u4ee3\u8868\u30c1\u30fc\u30e0\u306e\u9078\u5b9a\u3092\u4f8b\u3068\u3057\u3066\uff0d<\/td>\nOR\u3000Vol.49, No.6<\/td>\n<\/tr>\n
25<\/td>\n2005<\/a><\/td>\n\u7fbd\u5ba4\u884c\u4fe1(\u5927\u962a\u7523\u696d\u5927\u5b66)\uff0c\u52a0\u85e4\u76f4\u6a39(\u4eac\u90fd\u5927\u5b66)<\/td>\nUSASHI(Mining Utilities and System Architecture for Scalable processing of Historical data)<\/td>\n\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2<\/td>\n<\/tr>\n
24<\/td>\n2004<\/a><\/td>\n\u5927\u897f\u6d69\u5fd7\uff0c\u77f3\u7530\u5065\u4ec1\uff0c\u9752\u5c71\u6d69\u4e4b(\u30d3\u30c7\u30aa\u30ea\u30b5\u30fc\u30c1)\uff0c\u733f\u6e21\u5eb7\u6587(\u7b51\u6ce2\u5927\u5b66)\uff0c\u732a\u98fc\u7f8e\u7fbd(\u6771\u4eac\u5de5\u696d\u5927\u5b66)<\/td>\n\u30c6\u30ec\u30d3\u756a\u7d44CM\u306e\u5272\u4ed8\u306b\u5bfe\u3059\u308b\u6570\u7406\u7684\u30a2\u30d7\u30ed\u30fc\u30c1\u3000\u30c6\u30ec\u30d3\u756a\u7d44CM\u306e\u5272\u4ed8\u306b\u5bfe\u3059\u308b\u89e3\u6cd5<\/td>\n\u65e5\u672cOR\u5b66\u4f1a\u5e73\u621015\u5e74\u79cb\u5b63\u7814\u7a76\u767a\u8868\u4f1a\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8\u96c6<\/td>\n<\/tr>\n
24<\/td>\n2004<\/a><\/td>\n\u6c60\u4e0a\u6566\u5b50(\u6210\u8e4a\u5927\u5b66)<\/td>\nA Subproblem-centric Model and Approach to the Nurse Scheduling Problem<\/td>\nMathematical Programming Vol.97, No.3<\/td>\n<\/tr>\n
23<\/td>\n2003<\/a><\/td>\n\u67f3\u7530\u4fca\u6a39(\u65e5\u672c\u90f5\u8239)\uff0c\u6589\u85e4\u52aa(\u69cb\u9020\u8a08\u753b\u7814\u7a76\u6240)<\/td>\n\u81ea\u52d5\u8eca\u8239\u7a4d\u4ed8\u304d\u652f\u63f4\u30b7\u30b9\u30c6\u30e0\u306e\u81ea\u52d5\u5e2d\u5272\u306b\u3064\u3044\u3066<\/td>\n\u5e73\u621014\u5e74\u6625\u5b63\u7814\u7a76\u767a\u8868\u4f1a\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8\u96c6<\/td>\n<\/tr>\n
23<\/td>\n2003<\/a><\/td>\n\u897f\u7530\u5927(\u30ad\u30e4\u30ce\u30f3\u30b7\u30b9\u30c6\u30e0\u30bd\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3\u30ba)\uff0c\u4e2d\u5ddd\u8cc0\u6d25\u4e5f(\u30b5\u30f3\u30c8\u30ea\u30fc)\uff0c\u76f8\u7530\u525b(\u5343\u4ee3\u7530\u8208\u696d)\uff0c\u718a\u672c\u548c\u6d69\uff0c\u5c0f\u897f\u4f38\u4e4b(\u30ad\u30e4\u30ce\u30f3\u30b7\u30b9\u30c6\u30e0\u30bd\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3\u30ba)<\/td>\n\u6700\u9069\u8f38\u914d\u9001\u8a08\u753b\u554f\u984c\u3078\u306e\u6570\u7406\u8a08\u753b\u6cd5\u306e\u9069\u7528<\/td>\nOR\u3000Vol.47\uff0cNo.1<\/td>\n<\/tr>\n
22<\/td>\n2002<\/td>\n\u5cf6\u5ddd\u967d\u4e00\uff0c\u6797\u7f8e\u6c99\uff0c\u7530\u53e3\u6771(\u4e2d\u592e\u5927\u5b66)<\/td>\n\u9996\u90fd\u9ad8\u901f\u9053\u8def\u306e\u74b0\u72b6\u7dda\u5efa\u8a2d\u306b\u3088\u308b\u4ea4\u901a\u6df7\u96d1\u306e\u7de9\u548c\u4e88\u6e2c<\/td>\nOR\u3000Vol.46\uff0cNo.3<\/td>\n<\/tr>\n
22<\/td>\n2002<\/td>\n\u5200\u6839\u85ab(\u653f\u7b56\u7814\u7a76\u5927\u5b66\u9662\u5927\u5b66)\uff0c\u9ad8\u6751\u7fa9\u6674(\u5e83\u5cf6\u5e02)<\/td>\n\u9996\u90fd\u6a5f\u80fd\u79fb\u8ee2\u8a08\u753b\u306e\u305f\u3081\u306e\u7dcf\u5408\u8a55\u4fa1\u624b\u6cd5\u306e\u958b\u767a\u3068\u305d\u306e\u624b\u6cd5<\/td>\nOR\u3000Vol.46\uff0cNo.6<\/td>\n<\/tr>\n
21<\/td>\n2001<\/td>\n\u670d\u90e8\u6b63\u592a\uff0c\u6728\u6751\u9999\u4ee3\u5b50\u897f\u5c71\u76f4\u6a39(\u69cb\u9020\u8a08\u753b\u7814\u7a76\u6240)<\/td>\nABS(Agent Based Simulator)\u30b7\u30b9\u30c6\u30e0<\/td>\n\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2<\/td>\n<\/tr>\n
21<\/td>\n2001<\/td>\n\u897f\u5ca1\u9756\u4e4b(\u6cd5\u653f\u5927\u5b66)<\/td>\n\u6559\u80b2\u30fb\u7814\u7a76\u7528\u3000\u751f\u7523\u30b9\u30b1\u30b8\u30e5\u30fc\u30e9\u300cAPSTMIZER\u300d<\/td>\n\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2<\/td>\n<\/tr>\n
20<\/td>\n2000<\/td>\n\u7560\u4e2d\u653f\u56fd(\u4e2d\u592e\u9451\u5b9a\u6240)\uff0c\u85e4\u6c5f\u5bff\u7d00\uff0c\u571f\u80a5\u6b63\uff0c\u5c3e\u5d0e\u4fca\u6cbb(\u5e83\u5cf6\u5927\u5b66)<\/td>\n\u56fa\u5b9a\u8cc7\u7523\u5b85\u5730\u3078\u306e\u30d5\u30a1\u30b8\u30a3\u6570\u91cf\u5316\u7406\u8ad6\u306e\u9069\u7528<\/td>\nOR\u3000Vol.44\uff0cNo.6<\/td>\n<\/tr>\n
19<\/td>\n1999<\/td>\n\u4eca\u6cc9\u6df3(\u6771\u6d0b\u5927\u5b66)\uff0c\u5c71\u8d8a\u5eb7\u88d5(NTT)\uff0c\u6751\u4e0a\u5143\u4e00(\u6771\u90a6\u30ac\u30b9\u60c5\u5831\u30b7\u30b9\u30c6\u30e0)\uff0c\u68ee\u6238\u664b(\u65e9\u7a32\u7530\u5927\u5b66)<\/td>\n\u30b8\u30e7\u30d6\u306e\u5206\u5c90\u3092\u4f34\u30462\u5de5\u7a0b\u4e26\u5217\u6a5f\u68b0\u30d5\u30ed\u30fc\u30b7\u30e7\u30c3\u30d7\u30b9\u30b1\u30b8\u30e5\u30fc\u30ea\u30f3\u30b0\u3078\u306e\u5206\u5272\u30a2\u30d7\u30ed\u30fc\u30c1<\/td>\nOR\u3000Vol.43\uff0cNo.11<\/td>\n<\/tr>\n
18<\/td>\n1998<\/td>\n\u6749\u91ce\u9686(\u30b7\u30ea\u30a6\u30b9)<\/td>\n\u4f01\u696d\u901a\u4fe1\u7db2\u306b\u304a\u3051\u308bHybrid\u7db2\u69cb\u6210\u6c7a\u5b9a\u306e\u305f\u3081\u306e\u6570\u7406\u30e2\u30c7\u30eb<\/td>\nOR\u3000Vol.42\uff0cNo.4<\/td>\n<\/tr>\n
17<\/td>\n1997<\/td>\n\u76f8\u6ca2\u5065\u5b9f\u30fb\u6cb3\u91ce\u9ad8\u6d0b(\u79e9\u7236\u5c0f\u91ce\u7530)\uff0c\u68ee\u96c5\u592b(\u6771\u4eac\u5de5\u696d\u5927\u5b66)\uff0c<\/td>\n\u751f\u7523\u30fb\u8f38\u9001\u8a08\u753b\u30e2\u30c7\u30eb\u3068\u305d\u306e\u611f\u5ea6\u5206\u6790\u60c5\u5831\u306e\u6226\u7565\u7684\u5229\u7528<\/td>\nOR\u3000Vol.41\uff0cNo.8<\/td>\n<\/tr>\n
17<\/td>\n1997<\/td>\n\u6c60\u4e0a\u6566\u5b50\uff0c\u4e39\u7fbd\u660e\uff0c\u5927\u5009\u5143\u5b8f(\u6210\u8e4a\u5927\u5b66)<\/td>\n\u6211\u304c\u56fd\u306b\u304a\u3051\u308b\u30ca\u30fc\u30b9\u30fb\u30b9\u30b1\u30b8\u30e5\u30fc\u30ea\u30f3\u30b0\u554f\u984c<\/td>\nOR\u3000Vol.41\uff0cNo.8<\/td>\n<\/tr>\n
16<\/td>\n1996<\/td>\n\u6728\u4e0b\u6804\u8535(\u540d\u57ce\u5927\u5b66)\uff0c\u5bae\u5742\u623f\u5343\u52a0(\u5c71\u6b66\u30cf\u30cd\u30a6\u30a8\u30eb)\uff0c\u77f3\u5ddd\u826f\u5149\uff0c\u6771\u5e78\u5f66(\u5c71\u6b66\u8a08\u88c5)<\/td>\n\u62e1\u5f35AHP\u624b\u6cd5\u3092\u5229\u7528\u3057\u305f\u30ea\u30cb\u30e5\u30fc\u30a2\u30eb\u306e\u30b3\u30b9\u30c8\u30d9\u30cd\u30d5\u30a3\u30c3\u30c8\u5206\u6790<\/td>\nOR\u3000Vol.40\uff0cNo.8<\/td>\n<\/tr>\n
16<\/td>\n1996<\/td>\n\u672b\u5409\u4fca\u5e78(\u6771\u4eac\u7406\u79d1\u5927\u5b66)<\/td>\nDEA\u306b\u57fa\u3065\u304f\u9650\u754c\u8cbb\u7528\u4fa1\u683c\u5f62\u6210\uff1aNTT\u96fb\u8a71\u57fa\u672c\u6599\u91d1\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf<\/td>\nOR\u3000Vol.40\uff0cNo.12<\/td>\n<\/tr>\n
16<\/td>\n1996<\/td>\n\u77e2\u7530\u5065\uff0c\u4e2d\u5c71\u7adc\u8d77\uff0c\u4e95\u4e0a\u6b63\u4e4b(\u65e5\u672c\u96fb\u4fe1\u96fb\u8a71)<\/td>\n\u901a\u4fe1\u4e8b\u696d\u306b\u304a\u3051\u308bDEA\u6cd5\u306e\u9069\u7528\u4e8b\u4f8b<\/td>\nOR\u3000Vol.40\uff0cNo.12<\/td>\n<\/tr>\n
15<\/td>\n1995<\/td>\n\u5c71\u7530\u6cf0\u5f18(\u9577\u5ca1\u6280\u8853\u79d1\u5b66\u5927\u5b66)\uff0c\u53e4\u6797\u9686(\u6cd5\u653f\u5927\u5b66)<\/td>\n\u9078\u629e\u7d44\u7acb\u306b\u304a\u3051\u308b\u7d44\u307f\u5408\u308f\u305b\u6700\u9069\u5316\uff0d\u81ea\u52d5\u8eca\u30a8\u30f3\u30b8\u30f3\u306e\u4e8b\u4f8b\uff0d<\/td>\nOR\u3000Vol.39\uff0cNo.10<\/td>\n<\/tr>\n
15<\/td>\n1995<\/td>\n\u9ad8\u685c\u6d0b(\u5ddd\u5d0e\u5e02)\uff0c\u5927\u5c71\u9054\u96c4(\u57fc\u7389\u5927\u5b66)<\/td>\n\u30cd\u30c3\u30c8\u30a6\u30ef\u30fc\u30af\u30e2\u30c7\u30eb\u306b\u3088\u308b\u90fd\u5e02\u3054\u307f\u53ce\u96c6\u8f38\u9001\u30b7\u30b9\u30c6\u30e0\u306e\u6700\u9069\u5316<\/td>\nOR\u3000Vol.39\uff0cNo.10<\/td>\n<\/tr>\n
14<\/td>\n1994<\/td>\n\u6a4b\u672c\u662d\u6d0b(\u7b51\u6ce2\u5927\u5b66)<\/td>\nDEA\u306b\u3088\u308b\u91ce\u7403\u6253\u8005\u306e\u8a55\u4fa1<\/td>\nOR\u3000Vol.38\uff0cNo.3<\/td>\n<\/tr>\n
13<\/td>\n1993<\/td>\n\u6d6a\u5e73\u535a\u4eba(\u7523\u80fd\u77ed\u671f\u5927\u5b66)<\/td>\n\u7af6\u5408\u54c1\u4e88\u6e2c\u30e2\u30c7\u30eb\u306e\u4e00\u8003\u5bdf<\/td>\nOR\u3000Vol.37\uff0cNo.5<\/td>\n<\/tr>\n
13<\/td>\n1993<\/td>\n\u5c71\u5730\u54f2\u4e5f(\u7dcf\u7406\u5e9c)<\/td>\n\u72ed\u6c34\u9053\u306b\u304a\u3051\u308b\u822a\u884c\u74b0\u5883\u7dcf\u5408\u8a55\u4fa1\u306e\u305f\u3081\u306e\u30a8\u30ad\u30b9\u30d1\u30fc\u30c8\u30b7\u30b9\u30c6\u30e0\u306e\u7814\u7a76<\/td>\nOR\u3000Vol.37\uff0cNo.5<\/td>\n<\/tr>\n
13<\/td>\n1993<\/td>\n\u5409\u5ca1\u8302(\u6771\u4eac\u90fd)<\/td>\n1\u5186\u5165\u672d\u306e\u640d\u76ca<\/td>\nOR\u3000Vol.37\uff0cNo.5<\/td>\n<\/tr>\n
12<\/td>\n1992<\/td>\n\u4eca\u91ce\u6d69\uff0c\u6731\u5586(\u6771\u4eac\u5de5\u696d\u5927\u5b66)<\/td>\n\u6700\u9069\u30af\u30e9\u30b9\u7de8\u6210\u554f\u984c\uff0d\u6771\u4eac\u5de5\u696d\u5927\u5b66\u306b\u304a\u3051\u308b\u30b1\u30fc\u30b9\u30fb\u30b9\u30bf\u30c7\u30a3\uff0d<\/td>\nOR\u3000Vol.36\uff0cNo.2<\/td>\n<\/tr>\n
11<\/td>\n1991<\/td>\n\u7530\u53e3\u6771\uff0c\u9ad8\u6a4b\u4fee\u4e00\uff0c\u4e2d\u6751\u5b66(\u5c71\u68a8\u5927\u5b66)<\/td>\n\u5fd7\u671b\u6821\u4f75\u9858\u30c7\u30fc\u30bf\u304b\u3089\u5c0e\u304b\u308c\u308b\u53d7\u9a13\u751f\u306e\u5927\u5b66\u30fb\u5b66\u90e8\u306b\u5bfe\u3059\u308b\u9078\u597d<\/td>\nOR\u3000Vol.35\uff0cNo.4<\/td>\n<\/tr>\n
11<\/td>\n1991<\/td>\n\u5ddd\u4e2d\u5b50\u656c\u81f3(\u8db3\u5229\u5de5\u696d\u5927\u5b66)\uff0c\u77e2\u90e8\u771e(\u5de5\u5b66\u9662\u5927\u5b66)<\/td>\n\u5c0f\u5b66\u6821\u4e8b\u4f8b\u3088\u308a\u898b\u305f\u65bd\u8a2d\u914d\u7f6e\u3068\u570f\u57df\u914d\u5206<\/td>\nOR\u3000Vol.35\uff0cNo.5<\/td>\n<\/tr>\n
11<\/td>\n1991<\/td>\n\u4e0a\u91ce\u4fe1\u884c\uff0c\u4e2d\u5ddd\u7fa9\u4e4b\uff0c\u5fb3\u5c71\u535a\u4e8e(\u4f4f\u53cb\u91d1\u5c5e\u5de5\u696d)\uff0c\u4e2d\u5c71\u5f18\u9686(\u7532\u5357\u5927\u5b66)\uff0c\u7530\u6751\u5766\u4e4b(\u5927\u962a\u5927\u5b66)<\/td>\n\u9244\u92fc\u88fd\u9020\u30d7\u30ed\u30bb\u30b9\u306b\u304a\u3051\u308b\u30c8\u30e9\u30a4\u9078\u629e\u554f\u984c\u3078\u306e\u591a\u76ee\u7684\u8a08\u753b\u6cd5\u306e\u5fdc\u7528<\/td>\nOR\u3000Vol.35\uff0cNo.12<\/td>\n<\/tr>\n
10<\/td>\n1990<\/td>\n\u5185\u85e4\u6b63\u660e\uff0c\u68ee\u7530\u6052\u5e78\uff0c\u9752\u67f3\u307f\u3069\u308a(\u56fd\u7acb\u516c\u5bb3\u7814\u7a76\u6240)<\/td>\n\u30a2\u30e1\u30cb\u30c6\u30a3\u3092\u3044\u304b\u306b\u8a08\u91cf\u5316\u3059\u308b\u304b<\/td>\nOR\u3000Vol.34\uff0cNo.8<\/td>\n<\/tr>\n
10<\/td>\n1990<\/td>\n\u5317\u6751\u771e\u4e00(\u5c71\u68a8\u5927\u5b66)<\/td>\n\u90fd\u5e02\u3068\u6cb3\u5ddd\u306e\u30a4\u30e1\u30fc\u30b8\u3068\u30a2\u30e1\u30cb\u30c6\u30a3\u30bf\u30a6\u30f3\u8a08\u753b<\/td>\nOR\u3000Vol.34\uff0cNo.8<\/td>\n<\/tr>\n
10<\/td>\n1990<\/td>\n\u9593\u6e15\u91cd\u662d(\u795e\u6238\u5546\u8239\u5927\u5b66)<\/td>\n\u91ce\u7403\u306e\u6253\u8005\u30fb\u6295\u624b\u306e\u8ca2\u732e\u5ea6\u8a55\u4fa1\u306e\u305f\u3081\u306e\u65b0\u3057\u3044\u6307\u6a19<\/td>\nOR\u3000Vol.34\uff0cNo.2<\/td>\n<\/tr>\n
9<\/td>\n1989<\/td>\n\u99d2\u4e95\u7814\u4e8c\u30fb\u5742\u53e3\u654f\u660e(\u4e09\u83f1\u96fb\u6a5f)<\/td>\n\u7cfb\u7d71\u5fa9\u65e7\u554f\u984c\u306e\u5206\u679d\u9650\u5b9a\u6cd5\u306b\u3088\u308b\u89e3\u6cd5\u3068\u5fa9\u65e7\u64cd\u4f5c\u306b\u95a2\u3059\u308b\u77e5\u8b58\u306eOR\u7684\u5206\u6790\u3068\u8a55\u4fa1<\/td>\nOR\u3000Vol.33\uff0cNo.1<\/td>\n<\/tr>\n
9<\/td>\n1989<\/td>\n\u798f\u6751\u8061\uff0c\u4f50\u80fd\u514b\u660e\uff0c\u5c71\u5ddd\u6804\u6a39(\u5ddd\u5d0e\u88fd\u9244)<\/td>\n\u92fc\u6750\u51fa\u8377\u8a08\u753b\u30a8\u30ad\u30b9\u30d1\u30fc\u30c8\u30b7\u30b9\u30c6\u30e0\u3068\u5206\u679d\u9650\u5b9a\u6cd5<\/td>\nOR\u3000Vol.33\uff0cNo.1<\/td>\n<\/tr>\n
8<\/td>\n1988<\/td>\n\u6fa4\u7530\u6643\u4e8c(\u65e5\u7523\u81ea\u52d5\u8eca)<\/td>\n\u6700\u9069\u677f\u53d6\u308a\u81ea\u52d5\u8a08\u7b97\u30b7\u30b9\u30c6\u30e0<\/td>\nOR\u3000Vol.32\uff0cNo.4<\/td>\n<\/tr>\n
8<\/td>\n1988<\/td>\n\u4e2d\u91ce\u4e00\u592b\uff0c\u76f8\u6ca2\u308a\u3048\u5b50\uff0c\u4f1a\u7530\u667a\u5b50(\u69cb\u9020\u8a08\u753b\u7814\u7a76\u6240)\uff0c\u77e2\u5d0e\u7fa9\u884c\uff0c\u68ee\u6238\u664b(\u65e9\u7a32\u7530\u5927\u5b66)<\/td>\n\u30d1\u30bd\u30b3\u30f3\u7248\u30c0\u30f3\u30d7\u30c8\u30e9\u30c3\u30af\u904b\u884c\u30b7\u30df\u30e5\u30ec\u30fc\u30bf\u306e\u958b\u767a\u3068\u8d70\u8def\u533a\u9593\u306e\u30e2\u30c7\u30eb\u5316<\/td>\nOR\u3000Vol.32\uff0cNo.5<\/td>\n<\/tr>\n
7<\/td>\n1987<\/td>\n\u9e7f\u5009\u5c1a\u592b(\u6771\u30ec\u30b7\u30b9\u30c6\u30e0\u30bb\u30f3\u30bf\u30fc)<\/td>\n\u591a\u54c1\u7a2e\u751f\u7523\u30d7\u30ed\u30bb\u30b9\u306e\u305f\u3081\u306e\u751f\u7523\u8a08\u753b\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2<\/td>\nOR\u3000Vol.31\uff0cNo.3<\/td>\n<\/tr>\n
7<\/td>\n1987<\/td>\n\u5ddd\u5317\u7c73\u826f(\u65e5\u672c\u5927\u5b66)<\/td>\n\u6d77\u9762\u57cb\u7acb\u5730\u306e\u6700\u9069\u76e4\u9ad8<\/td>\nOR\u3000Vol.31\uff0cNo.9<\/td>\n<\/tr>\n
7<\/td>\n1987<\/td>\n\u4e00\u68ee\u54f2\u7537(\u5927\u962a\u5de5\u696d\u5927\u5b66)\uff0c\u8d8a\u5c71\u5eb7(\u8d8a\u5c71\u6cd5\u5f8b\u4e8b\u52d9\u6240)<\/td>\n\u8846\u8b70\u9662\u8b70\u54e1\u306e\u6c7a\u5b9a\u914d\u5206\u306e\u662f\u6b63\u306b\u95a2\u3059\u308b\u7dca\u6025\u63aa\u7f6e<\/td>\n1986\u3000\u6625\u3000\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8\u96c6<\/td>\n<\/tr>\n
6<\/td>\n1986<\/td>\n\u91ce\u6751\u6df3\u4e8c\uff0c\u5409\u7530\u5e78\u7537\uff0c\u6817\u5c3e\u5b5d\uff0c\u7af9\u4e2d\u6e05\u4ecb(\u677e\u4e0b\u96fb\u5de5)\uff0c\u897f\u5ddd\u7995\u4e00(\u4eac\u90fd\u5927\u5b66)<\/td>\n\u5bfe\u8a71\u578b\u591a\u76ee\u7684\u5728\u5eab\u6700\u9069\u5316\u30b7\u30b9\u30c6\u30e0\u306e\u958b\u767a<\/td>\nOR\u3000Vol.30\uff0cNo.2<\/td>\n<\/tr>\n
6<\/td>\n1986<\/td>\n\u52dd\u6751\u6b63\u9df9\uff0c\u7530\u4e2d\u5feb\u5409\uff0c\u68ee\u6587\u5f66\uff0c\u4f50\u85e4\u656c(\u65e5\u7acb\u88fd\u4f5c\u6240)<\/td>\n\u5730\u57df\u5225\u8ca9\u58f2\u529b\u8a55\u4fa1\u30b7\u30b9\u30c6\u30e0\u306e\u4f5c\u6210\u3068\u91cf\u7523\u54c1\u8ca9\u58f2\u65bd\u7b56\u3078\u306e\u9069\u7528<\/td>\nJORSJ\u3000Vol.28\uff0cNo.2<\/td>\n<\/tr>\n
5<\/td>\n1985<\/td>\n\u5cf6\u7530\u4fca\u90ce(\u660e\u6cbb\u5927\u5b66)\uff0c\u798f\u5cf6\u61b2\u6cbb(\u6b6f\u79d1\u533b\u5e2b\u4f1a)<\/td>\n\u6b6f\u79d1\u75be\u60a3SD\u30e2\u30c7\u30eb<\/td>\nOR\u3000Vol.29\uff0cNo.4<\/td>\n<\/tr>\n
5<\/td>\n1985<\/td>\n\u4e95\u585a\u6ecb\u592b\uff0c\u9ad8\u7530\u4fca\u592b(\u5ddd\u5d0e\u88fd\u9244\u30fb\u6c34\u5cf6)<\/td>\n\u96fb\u529b\u30c7\u30de\u30f3\u30c9\u5951\u7d04\u30b7\u30b9\u30c6\u30e0\u306e\u78ba\u7387<\/td>\nOR\u3000Vol.29\uff0cNo.5<\/td>\n<\/tr>\n
5<\/td>\n1985<\/td>\n\u5e73\u6797\u9686\u4e00(\u6771\u4eac\u7406\u79d1\u5927\u5b66)\uff0c\u9234\u6728\u4e45\u654f(\u6771\u4eac\u5de5\u696d\u5927\u5b66)\uff0c\u571f\u5c4b\u6607(\u65e5\u7acb\u88fd\u4f5c\u6240)<\/td>\nTool Module Design Problem for NC Machine Tools<\/td>\nJORSJ\u3000Vol.27\uff0cNo.3<\/td>\n<\/tr>\n
4<\/td>\n1984<\/td>\n\u77f3\u4e95\u535a\u53f8\uff0c\u4e2d\u91ce\u4e00\u592b(\u69cb\u9020\u8a08\u753b\u7814\u7a76\u6240)\uff0c\u98db\u5ca1\u5229\u660e(\u65e5\u672c\u539f\u5b50\u529b\u7814\u7a76\u6240)<\/td>\n\u4fe1\u983c\u6027\u4e88\u6e2c\u306e\u305f\u3081\u306e\u30d5\u30a9\u30fc\u30eb\u30c9\u30fb\u30c4\u30ea\u30fc\u624b\u6cd5\u306e\u6709\u52b9\u6027<\/td>\nOR\u3000Vol.28\uff0cNo.1<\/td>\n<\/tr>\n
4<\/td>\n1984<\/td>\n\u677e\u672c\u4fe1\u4e8c\uff0c\u4e09\u6839\u76f4\u4eba(\u6e05\u6c34\u5efa\u8a2d)<\/td>\n\u5efa\u7bc9\u65bd\u5de5\u306e\u4f5c\u696d\u8a08\u753b\u306b\u304a\u3051\u308b\u6700\u9069\u5316<\/td>\nOR\u3000Vol.28\uff0cNo.5<\/td>\n<\/tr>\n
4<\/td>\n1984<\/td>\n\u5c0f\u897f\u6d0b\u4e09\uff0c\u4e09\u7559\u548c\u5e78\uff0c\u5c0f\u6797\u9756(\u65e5\u7acb\u88fd\u4f5c\u6240)<\/td>\n3\u6b21\u5143Cuting\u3000Stock\u554f\u984c\u306b\u5bfe\u3059\u308b\u30d1\u30bf\u30fc\u30f3\u89e3\u6cd5\u306e\u8a55\u4fa1<\/td>\n1983\u3000\u79cb\u3000\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8\u96c6<\/td>\n<\/tr>\n
4<\/td>\n1984<\/td>\n\u8170\u585a\u6b66\u5fd7(\u7b51\u6ce2\u5927\u5b66)<\/td>\n\u4efb\u610f\u306b\u4e0e\u3048\u3089\u308c\u305f\u9818\u57df\u306e\u4eba\u53e3\u63a8\u5b9a<\/td>\n1983\u3000\u79cb\u3000\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8\u96c6<\/td>\n<\/tr>\n
3<\/td>\n1983<\/td>\n\u5b89\u6c38\u901a\u6674\u30fb\u5c0f\u6797\u667a\u5b50(\u65e5\u672c\u60c5\u5831\u30b5\u30fc\u30d3\u30b9)\uff0c\u4e2d\u5143\u4e09\u90ce(\u5b89\u4e95\u5efa\u7bc9\u8a2d\u8a08)<\/td>\n\u9006\u65e5\u5f71\u554f\u984c\uff0d\u65e5\u5f71\u898f\u5236\u3092\u8003\u616e\u3057\u305f\u6700\u9069\u5efa\u8a2d\u53ef\u80fd\u9818\u57df\u306e\u6c7a\u5b9a\uff0d<\/td>\nOR\u3000Vol.27\uff0cNo.6<\/td>\n<\/tr>\n
3<\/td>\n1983<\/td>\n\u67f3\u4e95\u6d69(\u6176\u61c9\u7fa9\u587e\u5927\u5b66)<\/td>\n\u30d0\u30eb\u30af\u30e9\u30a4\u30f3\u65b9\u5f0f\u4e0b\u306b\u304a\u3051\u308b\u4fa1\u683c\u7af6\u4e89\u3068\u85ac\u4fa1\u57fa\u6e96\u306e\u63a8\u79fb<\/td>\nJORSJ\u3000Vol.25\uff0cNo.3<\/td>\n<\/tr>\n
3<\/td>\n1983<\/td>\n\u77f3\u5802\u4e00\u6210(\u4e09\u83f1\u91cd\u5de5\u696d)<\/td>\n\u300c\u76ee\u3067\u898b\u308b\u8a08\u753b\u300d\u306e\u624b\u6cd5\uff0dGERT\u306e\u5b9f\u7528\u5316<\/td>\n1982\u3000\u79cb\u3000\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8\u96c6<\/td>\n<\/tr>\n
2<\/td>\n1982<\/td>\n\u77e2\u91ce\u5075\u4e00\uff0c\u5929\u6d77\u6e05\u5fd7(\u7af9\u4e2d\u5de5\u52d9\u5e97)<\/td>\n\u7acb\u4f53\u81ea\u52d5\u5009\u5eab\u306e\u8a08\u753b\u30fb\u8a2d\u8a08\u624b\u6cd5\u306b\u3064\u3044\u3066<\/td>\nOR\u3000Vol.26\uff0cNo.3<\/td>\n<\/tr>\n
1<\/td>\n1981<\/td>\n\u897f\u7530\u4fca\u592b\u30b0\u30eb\u30fc\u30d7(\u5927\u962a\u5927\u5b66)<\/td>\n\u8ca1\u52d9\u8af8\u8868\u3092\u7528\u3044\u305f\u9577\u671f\u7d4c\u55b6\u8a08\u753b\u306e\u305f\u3081\u306e\u30b7\u30b9\u30c6\u30e0<\/td>\nOR\u3000Vol.22\uff0cNo.3<\/td>\n<\/tr>\n
1<\/td>\n1981<\/td>\n\u6edd\u53e3\u5e78\u5f18\uff0c\u91d1\u5b50\u6e96\u30cb(\u5b87\u90e8\u8208\u7523)<\/td>\n\u5de5\u5834\u7fa4\u306b\u304a\u3051\u308b\u96fb\u529b\u30fb\u84b8\u6c17\u306e\u6700\u9069\u4f9b\u7d66\u30b7\u30b9\u30c6\u30e0<\/td>\nOR\u3000Vol.23\uff0cNo.4<\/td>\n<\/tr>\n
1<\/td>\n1981<\/td>\n\u6728\u4e0b\u77e5\u5df1(\u4e09\u83f1\u7dcf\u5408\u7814\u7a76\u6240)<\/td>\n\u5c0f\u58f2\u5e97\u7acb\u5730\u8a08\u753b\u306e\u65b0\u3057\u3044\u8003\u3048\u65b9<\/td>\nOR\u3000Vol.24\uff0cNo.5<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\u4e8b\u4f8b\u7814\u7a76\u5968\u52b1\u8cde\uff08\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u90e8\u9580\uff09<\/h5>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u56de<\/th>\n\u5e74\u5ea6<\/th>\n\u53d7\u8cde\u8005<\/th>\n\u5bfe\u8c61\u7814\u7a76<\/th>\n<\/tr>\n<\/thead>\n
15<\/td>\n2000<\/td>\n\u5409\u91ce\u79c0\u660e\uff0c\u5c71\u672c\u5c1a\u751f(NTT)<\/td>\n\u30c8\u30e9\u30d2\u30c3\u30af\u8a55\u4fa1\u30fb\u8a2d\u8a08\u652f\u63f4\u30b7\u30b9\u30c6\u30e0\uff1aTEDAS<\/td>\n<\/tr>\n
14<\/td>\n1999<\/td>\n\u8a72\u5f53\u306a\u3057<\/td>\n<\/td>\n<\/tr>\n
13<\/td>\n1998<\/td>\n\u8a72\u5f53\u306a\u3057<\/td>\n<\/td>\n<\/tr>\n
12<\/td>\n1997<\/td>\n\u8a72\u5f53\u306a\u3057<\/td>\n<\/td>\n<\/tr>\n
11<\/td>\n1996<\/td>\n\u8a72\u5f53\u306a\u3057<\/td>\n<\/td>\n<\/tr>\n
10<\/td>\n1995<\/td>\n\u5c0f\u6797\u9f8d\u4e00(\u6843\u5c71\u5b66\u9662\u5927\u5b66)<\/td>\n\u591a\u5909\u91cf\u89e3\u6790\u30d7\u30ed\u30b0\u30e9\u30e0\u3000\u307e\u308b\u3070<\/td>\n<\/tr>\n
9<\/td>\n1994<\/td>\n\u8a72\u5f53\u306a\u3057<\/td>\n<\/td>\n<\/tr>\n
8<\/td>\n1993<\/td>\n\u4e2d\u5c71\u5f18\u9686(\u7532\u5357\u5927\u5b66)\uff0c\u4e09\u8c37\u514b\u4e4b\u52a9(\u5e83\u5cf6\u5927\u5b66)\uff0c\u5409\u7530\u592a(\u677e\u4e0b\u96fb\u5de5)<\/td>\n\u591a\u76ee\u7684\u8a08\u753b\u6cd5\u306b\u3088\u308b\u98fc\u6599\u914d\u5408\u652f\u63f4\u30b7\u30b9\u30c6\u30e0<\/td>\n<\/tr>\n
7<\/td>\n1992<\/td>\n\u95a2\u53e3\u606d\u6bc5(\u5317\u6d77\u9053\u5927\u5b66)<\/td>\nCAMP\uff1a\u9806\u5e8f\u3065\u3051\u5206\u679d\u9650\u5b9a\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u8a2d\u8a08\u652f\u63f4\u30b7\u30b9\u30c6\u30e0<\/td>\n<\/tr>\n
7<\/td>\n1992<\/td>\n\u5185\u7530\u667a\u53f2(\u795e\u5948\u5ddd\u5927\u5b66)\uff0c\u672c\u90f7\u8302(\u5c02\u4fee\u5927\u5b66)\uff0c(\u682a)\u30b7\u30b9\u30c6\u30e0\u8a08\u753b\u7814\u7a76\u6240<\/td>\n\u884c\u5217\u6f14\u7b97\u7528\u8a00\u8a9e\u3000LAMAX-S<\/td>\n<\/tr>\n
6<\/td>\n1991<\/td>\n\u8a72\u5f53\u306a\u3057<\/td>\n<\/td>\n<\/tr>\n
5<\/td>\n1990<\/td>\n\u5357\u77f3\u6643\u660e(\u8fb2\u6797\u6c34\u7523\u7701)<\/td>\n\u30d1\u30bd\u30b3\u30f3\u7528\u6570\u7406\u8a08\u753b\u30b7\u30b9\u30c6\u30e0\u300cMicro-NAPS\u300d<\/td>\n<\/tr>\n
4<\/td>\n1989<\/td>\n\u690e\u585a\u4e45\u96c4(\u5de5\u5b66\u9662\u5927\u5b66)<\/td>\n\u30da\u30c8\u30ea\u30cd\u30c3\u30c8\u30e2\u30c7\u30eb\u30fb\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u30fb\u30b7\u30b9\u30c6\u30e0<\/td>\n<\/tr>\n
3<\/td>\n1988<\/td>\n\u9ad8\u6a4b\u609f\uff0c\u77e2\u90e8\u535a(\u6771\u4eac\u7406\u79d1\u5927\u5b66)\uff0c\u5bae\u7530\u96c5\u667a(\u9752\u5c71\u5b66\u9662\u5973\u5b50\u77ed\u5927)\uff0c\u672c\u90f7\u8302(\u5c02\u4fee\u5927\u5b66)\uff0c\u516b\u5dfb\u76f4\u4e00(\u30b7\u30b9\u30c6\u30e0\u8a08\u753b\u7814\u7a76\u6240)<\/td>\nASNOP(Application System for Nonlinear Optimization Problems)<\/td>\n<\/tr>\n
2<\/td>\n1987<\/td>\n\u5927\u67f3\u4fca\u592b(\u5317\u6d77\u9053\u5927\u5b66)<\/td>\nLP Calculator<\/td>\n<\/tr>\n
2<\/td>\n1987<\/td>\n\u8fbb\u65b0\u516d\uff0c\u4e95\u5185\u5584\u81e3\uff0c\u6709\u99ac\u660c\u5b8f\uff0c\u591a\u4e95\u525b(\u795e\u6238\u5546\u79d1\u5927\u5b66)<\/td>\n\u30d1\u30bd\u30b3\u30f3\u306b\u3088\u308b\u30a2\u30f3\u30b1\u30fc\u30c8\u8abf\u67fb\u652f\u63f4\u30b7\u30b9\u30c6\u30e0\uff0dQUEST PACK Ver. 2.0\uff0d<\/td>\n<\/tr>\n
1<\/td>\n1986<\/td>\n\u53e4\u6797\u9686(\u57fc\u7389\u5927\u5b66)<\/td>\n\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u8a08\u753b\u6cd5<\/td>\n<\/tr>\n
1<\/td>\n1986<\/td>\n\u6797\u4e9c\u592b(\u7b51\u6ce2\u5927\u5b66)<\/td>\n\u6559\u80b2\u7528BASIC\u30fbDYNAMO(\u7b2c3\u7248)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
\n
\u8ad6\u6587\u8cde Best JORSJ\/TORSJ Paper of the Year<\/h5>\n

\u203b\u7b2c8\u56de\u306f\u300c60\u5468\u5e74\u8a18\u5ff5\u8ad6\u6587\u8cde\u300d<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u56de<\/th>\n\u5e74\u5ea6<\/th>\n\u53d7\u8cde\u8005<\/th>\n\u5bfe\u8c61\u8ad6\u6587<\/th>\n\u63b2\u8f09\u8a8c<\/th>\n<\/tr>\n<\/thead>\n
15<\/td>\n2025<\/td>\n\u897f\u6751\u7d50\u5e0c\uff08\u4eac\u90fd\u5927\u5b66\uff09\uff0c\u798f\u7530\u30a8\u30ec\u30f3\u79c0\u7f8e\uff08\u4eac\u90fd\u5927\u5b66\uff09\uff0c\u5c71\u4e0b\u4fe1\u96c4\uff08\u4eac\u90fd\u5927\u5b66\uff09<\/td>\nMonotonicity for Multiobjective Accelerated Proximal Gradient Methods<\/td>\nJORSJ Vol.67, No.1, pp.1-17\u00a0(2024)<\/td>\n<\/tr>\n
14<\/td>\n2024<\/a><\/td>\n\u6cb3\u702c\u5eb7\u5fd7\uff08\u6771\u4eac\u5927\u5b66\uff09<\/td>\nStochastic input models for online computing<\/td>\nJORSJ Vol. 66, No. 2, pp. 95-111\u00a0(2023)<\/td>\n<\/tr>\n
13<\/td>\n2023<\/a><\/td>\n\u5bd2\u91ce\u5584\u535a\uff08\u6771\u4eac\u5927\u5b66\uff09<\/td>\nPrimal-dual algorithm for quasi-static contact problem with Coulomb\u2019s friction<\/td>\nJORSJ Vol. 65, No. 1, pp. 1-22\u00a0(2022)<\/td>\n<\/tr>\n
12<\/td>\n2022<\/a><\/td>\n\u6728\u6751\u96c5\u4fca\uff08\u682a\u5f0f\u4f1a\u793e\u30b7\u30de\u30ce\uff09\uff0c\u6edd\u6839\u54f2\u54c9\uff08\u5927\u962a\u5927\u5b66\uff09<\/td>\nNumerical implementation of the augmented truncation approximation to single-server queues with level-dependent arrivals and disasters<\/td>\nJORSJ Vol.64, No.2, pp.61-86\u00a0(2021)<\/td>\n<\/tr>\n
11<\/td>\n2021<\/a><\/td>\n\u85e4\u539f\u6d0b\u5fd7\uff08\u4fe1\u5dde\u5927\u5b66\uff09\uff0c\u8352\u6728\u76f4\u6d69\uff08\u682a\u5f0f\u4f1a\u793e\u30de\u30a4\u30af\u30ed\u30c6\u30c3\u30af\uff09\uff0c\u5c71\u672c\u535a\u7ae0\uff08\u4fe1\u5dde\u5927\u5b66\uff09<\/td>\nOne-way trading problems via linear optimization<\/td>\nJORSJ Vol.63, No.1, pp.1-30\u00a0(2020)<\/td>\n<\/tr>\n
10<\/td>\n2020<\/a><\/td>\n\u9727\u751f\u62d3\u4e5f\uff08\u4e09\u83f1UFJ\u30c8\u30e9\u30b9\u30c8\u6295\u8cc7\u5de5\u5b66\u7814\u7a76\u6240\uff09\uff0c\u6787\u3005\u6728\u898f\u96c4\uff08\u6176\u61c9\u7fa9\u587e\u5927\u5b66\uff09<\/td>\nEstimating forward looking distribution with the ross recovery theorem<\/td>\nJORSJ Vol.62, No.2, pp.83-107\u00a0(2019)<\/td>\n<\/tr>\n
9<\/td>\n2019<\/a><\/td>\n\u80e1\u8276\u6960\uff08\u540d\u53e4\u5c4b\u5927\u5b66\uff09\uff0c\u6df1\u6d25\u7fd4\uff08\u540d\u53e4\u5c4b\u5927\u5b66\uff09\uff0c\u6a4b\u672c\u82f1\u6a39\uff08\u6771\u4eac\u6d77\u6d0b\u5927\u5b66\uff09\uff0c\u4eca\u5800\u614e\u6cbb\uff08\u4e2d\u592e\u5927\u5b66\uff09\uff0c\u67f3\u6d66\u7766\u61b2\uff08\u540d\u53e4\u5c4b\u5927\u5b66\uff09<\/td>\nEfficient overlap detection and construction algorithms for the bitmap shape packing problem<\/td>\nJORSJ Vol.61, No.1, pp.132-150\u00a0(2018)<\/td>\n<\/tr>\n
8<\/td>\n2018<\/a><\/td>\n\u5e73\u4e95\u5e83\u5fd7\uff08\u6771\u4eac\u5927\u5b66\uff09<\/td>\nL-convexity on graph structures<\/td>\nJORSJ Vol.61, No.1, pp.71\u2013109 (2018)<\/td>\n<\/tr>\n
8<\/td>\n2018<\/a><\/td>\n\u5869\u6d66\u662d\u7fa9\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\uff09<\/td>\nAlgorithms for L-convex function minimization: connection between discrete convex analysis and other research fields<\/td>\nJORSJ Vol.60, No.3, pp.216\u2013243 (2017)<\/td>\n<\/tr>\n
7<\/td>\n2017<\/a><\/td>\n\u30d6\u30eb\u30ce\u30fbF\u30fb\u30ed\u30a6\u30ec\u30f3\u30bd\uff08\u6210\u8e4a\u5927\u5b66\uff09\uff0c\u6751\u677e\u6b63\u548c\uff08\u96fb\u6c17\u901a\u4fe1\u5927\u5b66\uff09\uff0c\u571f\u8c37\u9686\uff08\u653f\u7b56\u7814\u7a76\u5927\u5b66\u9662\u5927\u5b66\uff09<\/td>\nA Structural Geometrical Analysis of Weakly Infeasible SDPs<\/td>\nJORSJ Vol.59, No.3, pp.241\u2013257\u00a0(2016)<\/td>\n<\/tr>\n
6<\/td>\n2016<\/a><\/td>\n\u5869\u6d66\u662d\u7fa9\uff08\u6771\u5317\u5927\u5b66\uff09\uff0c\u7530\u6751\u660e\u4e45\uff08\u6176\u61c9\u7fa9\u587e\u5927\u5b66\uff09<\/td>\nGROSS SUBSTITUTES CONDITION AND DISCRETE CONCAVITY FOR MULTI-UNIT VALUATIONS: A SURVEY<\/td>\nJORSJ Vol.58, No.1, pp.61-103\u00a0(2015)<\/td>\n<\/tr>\n
5<\/td>\n2015<\/a><\/td>\n\u4eca\u5800\u614e\u6cbb, \u7c21\u4e8e\u8000\uff08\u540d\u53e4\u5c4b\u5927\u5b66\uff09\uff0c\u7530\u4e2d\u52c7\u771f\uff08\u6210\u8e4a\u5927\u5b66\uff09\uff0c\u67f3\u6d66\u7766\u61b2\uff08\u540d\u53e4\u5c4b\u5927\u5b66\uff09<\/td>\nENUMERATING BOTTOM-LEFT STABLE POSITIONS FOR RECTANGLE PLACEMENTS WITH OVERLAP<\/td>\nJORSJ Vol.57, No.1, pp.45-61\u00a0(2014)<\/td>\n<\/tr>\n
4<\/td>\n2014<\/a><\/td>\n\u5897\u5c71\u535a\u4e4b\uff08\u4eac\u90fd\u5927\u5b66\uff09<\/td>\nTAIL ASYMPTOTICS FOR CUMULATIVE PROCESSES SAMPLED AT HEAVY-TAILED RANDOM TIMES WITH APPLICATIONS TO QUEUEING MODELS IN MARKOVIAN ENVIRONMENTS<\/td>\nJORSJ Vol.56, No.4, pp.257-308\u00a0(2013)<\/td>\n<\/tr>\n
3<\/td>\n2013<\/a><\/td>\n\u5b89\u85e4\u548c\u654f\uff0c\u7532\u6590\u5145\u5f66\uff0c\u524d\u7530\u606d\u4f38\uff0c\u95a2\u8c37\u548c\u4e4b\uff08\u9759\u5ca1\u5927\u5b66\uff09<\/td>\nLEAST DISTANCE BASED INEFFICIENCY MEASURES ON THE PARETO-EFFICIENT FRONTIER IN DEA<\/td>\nJORSJ Vol.55, No.1, pp.73-91\u00a0(2012)<\/td>\n<\/tr>\n
2<\/td>\n2012<\/a><\/td>\n\u7530\u4e2d\u5065\u4e00\uff08\u96fb\u6c17\u901a\u4fe1\u5927\u5b66\uff09<\/td>\nMAXIMUM FLOW-COVERING LOCATION AND SERVICE START TIME PROBLEM AND ITS APPLICATION TO TOKYO METROPOLITAN RAILWAY NETWORK<\/td>\nJORSJ Vol.54, No.4, pp.237-258\u00a0(2011)<\/td>\n<\/tr>\n
1<\/td>\n2011<\/a><\/td>\n\u539f\u53e3\u548c\u4e5f(\u77f3\u5dfb\u5c02\u4fee\u5927\u5b66)\uff0c\u4f50\u85e4\u7950\u4e00(\u6ecb\u8cc0\u770c\u7435\u7436\u6e56\u74b0\u5883\u79d1\u5b66\u7814\u7a76\u30bb\u30f3\u30bf\u30fc)<\/td>\nSAMPLING SITE LOCATION PROBLEM IN LAKE MONITORING HAVING MULTIPLE PURPOSES AND CONSTRAINTS<\/td>\nJORSJ Vol.53, No.4, pp.289\u2013304\u00a0(2010)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
\n
\u5b66\u751f\u8ad6\u6587\u8cde Student Thesis Award<\/h5>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u56de<\/th>\n\u5e74\u5ea6<\/th>\n\u53d7\u8cde\u8005<\/th>\n\u8ad6\u6587\u540d<\/th>\n\u6307\u5c0e\u6559\u54e1<\/th>\n<\/tr>\n<\/thead>\n
43<\/td>\n2025<\/td>\n\u91ce\u53e3\u8cb4\u5fd7
\n(\u4eac\u90fd\u5927\u5b66 \u4fee\u8ad6)<\/td>\n		A Polynomial-Time Approximation Scheme for Weighted Triangle-Free 2-Matching<\/td>\n\u5c0f\u6797\u4f51\u8f14<\/td>\n<\/tr>\n
43<\/td>\n2025<\/td>\n\u4f50\u85e4\u5c1a\u6a39
\n(\u660e\u6cbb\u5927\u5b66 \u4fee\u8ad6)<\/td>\n		Implicit Graduated Optimization with Noise in Stochastic Gradient Descent<\/td>\n\u98ef\u585a\u79c0\u660e<\/td>\n<\/tr>\n
43<\/td>\n2025<\/td>\n\u6a2a\u5c71\u5065
\n(\u4e5d\u5dde\u5927\u5b66 \u4fee\u8ad6)<\/td>\n		\u30aa\u30f3\u30e9\u30a4\u30f3L\u266e\u51f8\u6700\u5c0f\u5316<\/td>\n\u6728\u6751\u6167<\/td>\n<\/tr>\n
43<\/td>\n2025<\/td>\n\u4e0a\u5cf6\u667a\u54c9
\n(\u6771\u4eac\u5927\u5b66 \u4fee\u8ad6)<\/td>\n		A Simple yet Highly Accurate Prediction-Correction Algorithm for Time-Varying Optimization<\/td>\n\u6b66\u7530\u6717\u5b50<\/td>\n<\/tr>\n
43<\/td>\n2025<\/td>\n\u5009\u4e0b\u967d
\n(\u6771\u4eac\u79d1\u5b66\u5927\u5b66 \u4fee\u8ad6)<\/td>\n		Egalitarian-equivalent and Strategy-proof Mechanisms in Homogeneous Multi-object Allocation Problems<\/td>\n\u5927\u548c\u6bc5\u5f66<\/td>\n<\/tr>\n
42<\/td>\n2024<\/a><\/td>\n\u5bfa\u5c3e\u6a39\u54c9
\n(\u4eac\u90fd\u5927\u5b66 \u4fee\u8ad6)<\/td>\n		Faster Matroid Partition Algorithms (\u30de\u30c8\u30ed\u30a4\u30c9\u5206\u5272\u554f\u984c\u306b\u5bfe\u3059\u308b\u9ad8\u901f\u306a\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0)<\/td>\n\u5c0f\u6797\u4f51\u8f14<\/td>\n<\/tr>\n
42<\/td>\n2024<\/a><\/td>\n\u6c38\u4e95\u7409\u751f
\n(\u4eac\u90fd\u5927\u5b66 \u4fee\u8ad6)<\/td>\n		A Twin Hyper-ellipsoidal Model with a Single Quadratic Constraint for Multiclass Classification<\/td>\n\u5c71\u5ddd\u96c4\u4e5f, \u5c71\u4e0b\u4fe1\u96c4<\/td>\n<\/tr>\n
42<\/td>\n2024<\/a><\/td>\n\u91ce\u5742\u6842\u60a0
\n(\u7b51\u6ce2\u5927\u5b66 \u4fee\u8ad6)<\/td>\n		Data Collaboration Analysis Over Matrix Manifolds<\/td>\n\u5409\u702c\u7ae0\u5b50<\/td>\n<\/tr>\n
42<\/td>\n2024<\/a><\/td>\n\u91ce\u6ca2\u8ad2\u592a
\n(\u6771\u4eac\u5927\u5b66 \u4fee\u8ad6)<\/td>\n		Random Subspace Methods for Two Difficult Classes of Optimization Problems (\u6271\u3044\u306b\u304f\u3044\u7279\u5fb4\u3092\u3082\u3063\u305f\u4e8c\u3064\u306e\u6700\u9069\u5316\u554f\u984c\u306b\u5bfe\u3059\u308b\u30e9\u30f3\u30c0\u30e0\u90e8\u5206\u7a7a\u9593\u6cd5)<\/td>\n\u6b66\u7530\u6717\u5b50<\/td>\n<\/tr>\n
42<\/td>\n2024<\/a><\/td>\n\u4e38\u6a4b\u594f\u97f3
\n(\u4e2d\u592e\u5927\u5b66 \u4fee\u8ad6)<\/td>\n		\u4f4e\u983b\u5ea6\u516c\u5171\u4ea4\u901a\u5730\u57df\u306b\u304a\u3051\u308b\u66dc\u65e5\u904b\u884c\u30d0\u30b9\u306e\u6642\u523b\u8868\u8a2d\u8a08<\/td>\n\u9ad8\u677e\u745e\u4ee3<\/td>\n<\/tr>\n
41<\/td>\n2023<\/a><\/td>\n\u5ca1\u90e8\u516c\u4eae
\n(\u4eac\u90fd\u5927\u5b66 \u4fee\u8ad6)<\/td>\n		A Second-Order Sequential Optimality Condition for Nonlinear Second-Order Cone Programming Problems<\/td>\n\u798f\u7530\u30a8\u30ec\u30f3\u79c0\u7f8e\uff0c\u5c71\u5ddd\u96c4\u4e5f<\/td>\n<\/tr>\n
41<\/td>\n2023<\/a><\/td>\n\u6069\u7530\u525b
\n(\u6771\u4eac\u5de5\u696d\u5927\u5b66 \u4fee\u8ad6)<\/td>\n		\u30bb\u30ab\u30f3\u30c9\u30d7\u30e9\u30a4\u30b9\u30aa\u30fc\u30af\u30b7\u30e7\u30f3\u306b\u304a\u3051\u308b\u60c5\u5831\u53d6\u5f97\u884c\u52d5\u306b\u95a2\u3059\u308b\u7814\u7a76<\/td>\n\u5927\u548c\u6bc5\u5f66<\/td>\n<\/tr>\n
41<\/td>\n2023<\/a><\/td>\n\u5ddd\u4e0a\u96c4\u5927
\n(\u7b51\u6ce2\u5927\u5b66 \u4fee\u8ad6)<\/td>\n		\u30c7\u30fc\u30bf\u30b3\u30e9\u30dc\u30ec\u30fc\u30b7\u30e7\u30f3\u89e3\u6790\u306b\u304a\u3051\u308b\u7d71\u5408\u95a2\u6570\u6700\u9069\u5316\u554f\u984c\u306e\u5b9a\u5f0f\u5316\u3068\u52b9\u7387\u7684\u89e3\u6cd5<\/td>\n\u9ad8\u91ce\u7950\u4e00<\/td>\n<\/tr>\n
41<\/td>\n2023<\/a><\/td>\n\u9577\u8c37\u5ddd\u548c\u6a39
\n(\u9759\u5ca1\u5927\u5b66 \u5352\u8ad6)<\/td>\n		\u30ed\u30d0\u30b9\u30c8\u5de1\u56de\u30bb\u30fc\u30eb\u30b9\u30de\u30f3\u554f\u984c\u306b\u5bfe\u3059\u308b\u767a\u898b\u7684\u89e3\u6cd5<\/td>\n\u5449\u5049\uff0c\u67f3\u6d66\u7766\u61b2<\/td>\n<\/tr>\n
41<\/td>\n2023<\/a><\/td>\n\u99ac\u5d8b\u6d77\u6597
\n(\u6771\u4eac\u5de5\u696d\u5927\u5b66 \u4fee\u8ad6)<\/td>\n		\u30d9\u30a4\u30ba\u63a8\u5b9a\u306b\u3088\u308b\u30b9\u30dd\u30f3\u30b5\u30fc\u30c9\u30b5\u30fc\u30c1\u5e83\u544a\u306e\u30ad\u30fc\u30ef\u30fc\u30c9\u5358\u4f4d\u3067\u306e\u30aa\u30f3\u30e9\u30a4\u30f3\u5165\u672d\u984d\u6700\u9069\u5316<\/td>\n\u4e2d\u7530\u548c\u79c0<\/td>\n<\/tr>\n
41<\/td>\n2023<\/a><\/td>\n\u4e09\u6d66\u958b\u767b
\n(\u6771\u4eac\u5de5\u696d\u5927\u5b66 \u4fee\u8ad6)<\/td>\n		\u4e8c\u76ee\u7684\u5168\u57df\u6728\u8a2d\u8a08\u30b9\u30b1\u30b8\u30e5\u30fc\u30ea\u30f3\u30b0\u554f\u984c\u306e\u30d1\u30ec\u30fc\u30c8\u6700\u9069\u89e3\u306e\u8a08\u7b97\u306b\u95a2\u3059\u308b\u7814\u7a76<\/td>\n\u5869\u6d66\u662d\u7fa9<\/td>\n<\/tr>\n
40<\/td>\n2022<\/td>\n\u5009\u53c8\u8fea\u54c9
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u91cf\u5b50\u30a2\u30cb\u30fc\u30ea\u30f3\u30b0\u3092\u7528\u3044\u305f2\u6b21\u5272\u5f53\u554f\u984c\u3068\u30d6\u30ec\u30fc\u30af\u6570\u6700\u5c0f\u5316\u554f\u984c\u306e\u6c42\u89e3<\/td>\n\u4e2d\u7530\u548c\u79c0\u6559\u6388<\/td>\n<\/tr>\n
40<\/td>\n2022<\/td>\n\u9ad8\u6a4b\u77e5\u5e0c
\n\uff08\u6771\u4eac\u7406\u79d1\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u6539\u672d\u5916\u4e57\u63db\u99c5\u306e\u8a2d\u5b9a\u306b\u3088\u308b\u8eca\u5185\u6df7\u96d1\u7387\u3078\u306e\u5f71\u97ff<\/td>\n\u6c60\u8fba\u6dd1\u5b50\u51c6\u6559\u6388\uff0c\u9b8f\u5ddd\u77e9\u7fa9\u52a9\u6559<\/td>\n<\/tr>\n
40<\/td>\n2022<\/td>\n\u4e2d\u6751\u5f69\u97f3
\n\uff08\u7b51\u6ce2\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Analysis of Infinite Server Batch Service Queues with Fixed and Random Batch Sizes<\/td>\nPhung-Duc Tuan \u51c6\u6559\u6388<\/td>\n<\/tr>\n
40<\/td>\n2022<\/td>\n\u897f\u5cf6\u5149\u6d0b
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u30bb\u30f3\u30b5\u30fc\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u4f4d\u7f6e\u63a8\u5b9a\u554f\u984c\u306b\u5bfe\u3059\u308b\u30d6\u30ed\u30c3\u30af\u5ea7\u6a19\u964d\u4e0b\u6cd5<\/td>\n\u4e2d\u7530\u548c\u79c0\u6559\u6388<\/td>\n<\/tr>\n
40<\/td>\n2022<\/td>\n\u5bcc\u58eb\u6643\u6210
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Non-Convex Quadratic Optimization with Random Projection<\/td>\n\u6b66\u7530\u6717\u5b50\u6559\u6388<\/td>\n<\/tr>\n
39<\/td>\n2021<\/td>\n\u5c0f\u539f\u5149\u6681
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Sequential Quadratic Optimization for Nonlinear Optimization Problems on Riemannian\u3000Manifolds<\/td>\n\u6b66\u7530\u6717\u5b50\u6559\u6388
\n\u5965\u91ce\u8cb4\u4e4b\u7814\u7a76\u54e1<\/td>\n<\/tr>\n
39<\/td>\n2021<\/td>\n\u52a0\u7d0d\u4f38\u4e00
\n\uff08\u7b51\u6ce2\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u6539\u826fChubanov\u6cd5\u306e\u5bfe\u79f0\u9310\u6700\u9069\u5316\u3078\u306e\u62e1\u5f35<\/td>\n\u5409\u702c\u7ae0\u5b50\u6559\u6388<\/td>\n<\/tr>\n
39<\/td>\n2021<\/td>\n\u6749\u6d66\u77e5\u6a39
\n\uff08\u540d\u53e4\u5c4b\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u6700\u9045\u5230\u7740\u6642\u523b\u5236\u7d04\u4ed8\u304d\u901a\u52e4\u30d0\u30b9\u30eb\u30fc\u30c6\u30a3\u30f3\u30b0\u554f\u984c\u306b\u5bfe\u3059\u308b\u53cd\u5fa9\u5c40\u6240\u63a2\u7d22\u6cd5<\/td>\n\u67f3\u6d66\u7766\u61b2\u6559\u6388
\n\u80e1\u8276\u6960\u8b1b\u5e2b<\/td>\n<\/tr>\n
39<\/td>\n2021<\/td>\n\u7530\u4e2d\u96c5\u4eba
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u30b5\u30fc\u30af\u30eb\u30b0\u30e9\u30d5\u306e\u9802\u70b9\u5f69\u8272<\/td>\n\u677e\u4e95\u77e5\u5df1\u6559\u6388<\/td>\n<\/tr>\n
39<\/td>\n2021<\/td>\n\u7fbd\u4f50\u7530\u7d18\u4e4b
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u7d4c\u8def\u60c5\u5831\u30c7\u30fc\u30bf\u3092\u6d3b\u7528\u3057\u305f\u7a7a\u9593\u79fb\u52d5\u55dc\u597d\u306e\u9006\u63a8\u5b9a<\/td>\n\u672c\u9593\u88d5\u5927\u51c6\u6559\u6388<\/td>\n<\/tr>\n
39<\/td>\n2021<\/td>\n\u6787\u3005\u6728\u88d5\u592a
\n\uff08\u6176\u61c9\u7fa9\u587e\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u5e73\u5747\u30fb\u5206\u6563\u30a2\u30d7\u30ed\u30fc\u30c1\u306b\u3088\u308b\u30a4\u30f3\u30d7\u30e9\u30a4\u30c9\u5206\u5e03\u3092\u7528\u3044\u305f\u6700\u9069\u901a\u8ca8\u30dd\u30fc\u30c8\u30d5\u30a9\u30ea\u30aa\u306b\u95a2\u3059\u308b\u7814\u7a76<\/td>\n\u6787\u3005\u6728\u898f\u96c4\u6559\u6388<\/td>\n<\/tr>\n
39<\/td>\n2021<\/td>\n\u6e21\u9089 \u71c3
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Hepatorenal Organ Exchange<\/td>\n\u5927\u548c\u6bc5\u5f66\u6559\u6388<\/td>\n<\/tr>\n
38<\/td>\n2020<\/td>\n\u6771 \u609f\u5927
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u4e09\u91cd\u5bfe\u89d2\u306a\u4e8c\u6b21\u5236\u7d04\u4ed8\u304d\u4e8c\u6b21\u8a08\u753b\u554f\u984c\u306b\u5bfe\u3059\u308b\u534a\u6b63\u5b9a\u5024\u8a08\u753b\u7de9\u548c\u306e\u72ed\u5c0f\u6027\u306b\u3064\u3044\u3066<\/td>\n\u798f\u7530\u5149\u6d69\u51c6\u6559\u6388<\/td>\n<\/tr>\n
38<\/td>\n2020<\/td>\n\u6c60\u7530\u57fa\u6a39
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u30b0\u30e9\u30d5\u69cb\u9020\u4e0a\u306e\u96e2\u6563\u51f8\u6027\u306b\u57fa\u3065\u304f\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u6700\u9069\u5316\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0<\/td>\n\u5e73\u4e95\u5e83\u5fd7\u51c6\u6559\u6388<\/td>\n<\/tr>\n
38<\/td>\n2020<\/td>\n\u5927\u5185\u514b\u4e45
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Subgeometric convergence formulas for the level-increment truncation of M\/G\/1-type Markov chain<\/td>\n\u5897\u5c71\u535a\u4e4b\u51c6\u6559\u6388<\/td>\n<\/tr>\n
38<\/td>\n2020<\/td>\n\u7530\u8fba\u5e83\u6a39
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Merit functions for multiobjective optimization and convergence rates analysis of multiobjective proximal gradient methods<\/td>\n\u798f\u7530\u30a8\u30ec\u30f3\u79c0\u7f8e\u51c6\u6559\u6388
\n\u5c71\u4e0b\u4fe1\u96c4\u6559\u6388<\/td>\n<\/tr>\n
38<\/td>\n2020<\/td>\n\u5897\u6751\u512a\u54c9
\n\uff08\u5927\u962a\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Dynamic Programming Approach to the Generalized Manhattan Network Problem<\/td>\n\u5c71\u53e3\u52c7\u592a\u90ce\u51c6\u6559\u6388<\/td>\n<\/tr>\n
38<\/td>\n2020<\/td>\nZHONG DAI
\n\uff08\u7b51\u6ce2\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Analysis on Aggregation Bias of Travelling Distance on Network using Crofton\u2019s Formula<\/td>\n\u5927\u6fa4\u7fa9\u660e\u6559\u6388<\/td>\n<\/tr>\n
37<\/td>\n2019<\/td>\n\u5c3e\u5f62\u4e00\u7a42
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u4e00\u822c\u5316\u30d7\u30ed\u30af\u30e9\u30b9\u30c6\u30b9\u89e3\u6790\u306eSDP\u7de9\u548c\u89e3\u6cd5\u306b\u304a\u3051\u308b\u30e9\u30f3\u30af\u30ea\u30ab\u30d0\u30ea\u30fc\u73fe\u8c61\u306e\u89e3\u6790<\/td>\n\u5ca9\u7530 \u899a\u6559\u6388<\/td>\n<\/tr>\n
37<\/td>\n2019<\/td>\n\u795e\u8c37\u4fca\u4ecb
\n\uff08\u6771\u4eac\u8fb2\u5de5\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u591a\u9805\u30ed\u30b8\u30c3\u30c8\u30e2\u30c7\u30eb\u306e\u5909\u6570\u9078\u629e\u554f\u984c\u306b\u5bfe\u3059\u308b\u6574\u6570\u6700\u9069\u5316\u624b\u6cd5\u306e\u69cb\u7bc9<\/td>\n\u5bae\u4ee3\u9686\u5e73\u51c6\u6559\u6388<\/td>\n<\/tr>\n
37<\/td>\n2019<\/td>\n\u5f90\u5b89\u6d0b
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u5de1\u822a\u901f\u5ea6\u5236\u5fa1\u306b\u3088\u308b\u822a\u7a7a\u4ea4\u901a\u7ba1\u7406\u624b\u6cd5\u306e\u63d0\u6848<\/td>\n\u677e\u4e95\u77e5\u5df1\u6559\u6388<\/td>\n<\/tr>\n
37<\/td>\n2019<\/td>\n\u7530\u4e2d\u96c5\u4eba
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u91cd\u307f\u4ed8\u304d\u6295\u7968\u30b2\u30fc\u30e0\u306e\u6700\u5c0f\u30b3\u30a2<\/td>\n\u677e\u4e95\u77e5\u5df1\u6559\u6388<\/td>\n<\/tr>\n
37<\/td>\n2019<\/td>\n\u4e09\u7530\u4f73\u90a3\u5b50
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Nonmonotone Descent Methods for Multiobjective Optimization Problems<\/td>\n\u798f\u7530\u30a8\u30ec\u30f3\u79c0\u7f8e\u51c6\u6559\u6388
\n\u5c71\u4e0b\u4fe1\u96c4\u6559\u6388<\/td>\n<\/tr>\n
37<\/td>\n2019<\/td>\n\u5c71\u5185\u9054\u8cb4
\n\uff08\u4e2d\u592e\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u6771\u4eac\u9996\u90fd\u570f\u306e\u5217\u8eca\u904b\u884c\u8a08\u753b\u306b\u5bfe\u3059\u308b\u5b9f\u7528\u7684\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0<\/td>\n\u9ad8\u677e\u745e\u4ee3\u51c6\u6559\u6388
\n\u4eca\u5800\u614e\u6cbb\u6559\u6388<\/td>\n<\/tr>\n
36<\/td>\n2018<\/td>\n\u5927\u57ce\u6cf0\u5e73
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Index Reduction for Differential- Algebraic Equations by Combinatorial Relaxation<\/td>\n\u5ca9\u7530 \u899a\u6559\u6388<\/td>\n<\/tr>\n
36<\/td>\n2018<\/td>\n\u541b\u585a\u67fe\u8cb4
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u4e00\u822c\u5316\u512a\u5bfe\u89d2\u884c\u5217\u306b\u3088\u308b\u7de9\u548c\u3092\u7528\u3044\u305fPooling Problem\u306b\u5bfe\u3059\u308b\u89e3\u6cd5\u306e\u69cb\u7bc9<\/td>\n\u5c71\u4e0b \u771f\u51c6\u6559\u6388<\/td>\n<\/tr>\n
36<\/td>\n2018<\/td>\n\u9ed2\u6728\u7950\u5b50
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Approximation Algorithms for Hub-and-Spoke Network Design Problems and Metric Labeling Problems<\/td>\n\u677e\u4e95\u77e5\u5df1\u6559\u6388<\/td>\n<\/tr>\n
36<\/td>\n2018<\/td>\n\u4f50\u85e4\u826f\u4eae
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u53cc\u65b9\u5411\u5e02\u5834\u306b\u5bfe\u3059\u308b\u591a\u9762\u4f53\u7684\u30af\u30ea\u30f3\u30c1\u30f3\u30b0\u30aa\u30fc\u30af\u30b7\u30e7\u30f3<\/td>\n\u5e73\u4e95\u5e83\u5fd7\u51c6\u6559\u6388<\/td>\n<\/tr>\n
36<\/td>\n2018<\/td>\n\u6210\u5cf6\u5927\u609f
\n\uff08\u7b51\u6ce2\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u6b63\u5b9a\u5024\u57fa\u5e95\u3092\u7528\u3044\u305f\u9310\u6700\u9069\u5316\u554f\u984c\u306e\u8fd1\u4f3c\u306b\u3064\u3044\u3066<\/td>\n\u5409\u702c\u7ae0\u5b50\u6559\u6388<\/td>\n<\/tr>\n
36<\/td>\n2018<\/td>\n\u5800 \u7be4\u53f2
\n\uff08\u5357\u5c71\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Gauss\u2012Seidel method for multi- leader-follower games<\/td>\n\u798f\u5cf6\u96c5\u592b\u6559\u6388<\/td>\n<\/tr>\n
35<\/td>\n2017<\/td>\n\u4f0a\u6771\u771f\u7531
\n\uff08\u540d\u53e4\u5c4b\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u30aa\u30f3\u30e9\u30a4\u30f3\u578b\u5546\u54c1\u767a\u9001\u554f\u984c\u306b\u5bfe\u3059\u308b\u5217\u751f\u6210\u30a2\u30d7\u30ed\u30fc\u30c1<\/td>\n\u67f3\u6d66\u7766\u61b2\u6559\u6388
\n\u80e1\u8276\u6960\u52a9\u6559<\/td>\n<\/tr>\n
35<\/td>\n2017<\/td>\n\u9ad8\u6fa4\u967d\u592a\u6717
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Approximation algorithms for covering 0-1 integer programming problems<\/td>\n\u6c34\u91ce\u771e\u6cbb\u6559\u6388<\/td>\n<\/tr>\n
35<\/td>\n2017<\/td>\n\u7530\u6751\u9686\u592a
\n\uff08\u6771\u4eac\u8fb2\u5de5\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u591a\u91cd\u5171\u7dda\u6027\u3092\u8003\u616e\u3057\u305f\u5909\u6570\u9078\u629e\u624b\u6cd5\u306e\u63d0\u6848<\/td>\n\u5bae\u4ee3\u9686\u5e73\u51c6\u6559\u6388<\/td>\n<\/tr>\n
35<\/td>\n2017<\/td>\n\u4e2d\u5cf6 \u84bc
\n\uff08\u6771\u4eac\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		Birkhoff\u8868\u73fe\u5b9a\u7406\u306e\u534a\u675f\u3078\u306e\u62e1\u5f35\u3068\u305d\u306e\u5fdc\u7528<\/td>\n\u5e73\u4e95\u5e83\u5fd7\u51c6\u6559\u6388<\/td>\n<\/tr>\n
35<\/td>\n2017<\/td>\n\u524d\u7530\u8b19\u592a\u90ce
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u30d0\u30a4\u30af\u30b7\u30a7\u30a2\u30ea\u30f3\u30b0\u306b\u304a\u3051\u308b\u81ea\u8ee2\u8eca\u518d\u914d\u7f6e\u7d4c\u8def\u306e\u6c7a\u5b9a\u624b\u6cd5\u306b\u95a2\u3059\u308b\u7814\u7a76<\/td>\n\u798f\u7530\u5149\u6d69\u51c6\u6559\u6388<\/td>\n<\/tr>\n
35<\/td>\n2017<\/td>\n\u77e2\u5cf6\u840c\u5b50
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Binomial catastrophe\u306e\u767a\u751f\u3092\u4f34\u3046\u30de\u30eb\u30b3\u30d5\u5909\u8abfMX\/M\/\u221e\u5f85\u3061\u884c\u5217\u306b\u5bfe\u3059\u308b\u4e2d\u5fc3\u6975\u9650\u5b9a\u7406<\/td>\n\u4e09\u597d\u76f4\u4eba\u6559\u6388<\/td>\n<\/tr>\n
34<\/td>\n2016<\/td>\n\u5ca9\u653f\u52c7\u4ec1
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u96e2\u6563\u6700\u9069\u5316\u554f\u984c\u306b\u5bfe\u3059\u308b\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u8868\u73fe\u3068k-\u52a3\u30e2\u30b8\u30e5\u30e9\u7de9\u548c<\/td>\n\u5e73\u4e95\u5e83\u5fd7\u51c6\u6559\u6388<\/td>\n<\/tr>\n
34<\/td>\n2016<\/td>\n\u6728\u6751\u572d\u5150
\n\uff08\u4e5d\u5dde\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u6df7\u5408\u6574\u6570\u975e\u7dda\u5f62\u8a08\u753b\u554f\u984c\u3092\u7528\u3044\u305fAIC\u6700\u5c0f\u5316<\/td>\n\u8107\u96bc\u4eba\u51c6\u6559\u6388<\/td>\n<\/tr>\n
34<\/td>\n2016<\/td>\n\u9727\u751f\u62d3\u4e5f
\n\uff08\u6176\u61c9\u7fa9\u587e\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Recovery Theorem\u3092\u7528\u3044\u305fForward Looking\u306a\u53ce\u76ca\u7387\u5206\u5e03\u306e\u63a8\u5b9a<\/td>\n\u6787\u3005\u6728\u898f\u96c4\u6559\u6388<\/td>\n<\/tr>\n
34<\/td>\n2016<\/td>\n\u6fa4\u4e95\u4f51\u6a39
\n\uff08\u540d\u53e4\u5c4b\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u30d0\u30b9\u4e57\u52d9\u54e1\u30b9\u30b1\u30b8\u30e5\u30fc\u30ea\u30f3\u30b0\u554f\u984c\u306b\u5bfe\u3059\u308b\u5217\u751f\u6210\u30a2\u30d7\u30ed\u30fc\u30c1<\/td>\n\u67f3\u6d66\u7766\u61b2\u6559\u6388
\n\u6a4b\u672c\u82f1\u6a39\u51c6\u6559\u6388<\/td>\n<\/tr>\n
34<\/td>\n2016<\/td>\n\u6a2a\u5c3e\u77e5\u5b5d
\n\uff08\u7b51\u6ce2\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Nuclear\u30ce\u30eb\u30e0\u3092\u7528\u3044\u305f\u884c\u5217\u30e9\u30f3\u30af\u6700\u5c0f\u5316\u624b\u6cd5\u306e\u5354\u8abf\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0\u3078\u306e\u5fdc\u7528<\/td>\n\u5409\u702c\u7ae0\u5b50\u6559\u6388<\/td>\n<\/tr>\n
33<\/td>\n2015<\/td>\n\u6c60\u4e0b\u6797\u592a\u90ce
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Infinitesimal Rigidity of Symmetric Frameworks<\/td>\n\u5ba4\u7530\u4e00\u96c4\u6559\u6388<\/td>\n<\/tr>\n
33<\/td>\n2015<\/td>\n\u5ddd\u51fa \u9759
\n\uff08\u540d\u53e4\u5c4b\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u30a8\u30c3\u30b7\u30e3\u30fc\u98a8\u30bf\u30a4\u30ea\u30f3\u30b0\u81ea\u52d5\u751f\u6210\u6cd5\u306e\u6539\u826f\u3068\u5fdc\u7528<\/td>\n\u4eca\u5800\u614e\u6cbb\u51c6\u6559\u6388<\/td>\n<\/tr>\n
33<\/td>\n2015<\/td>\n\u5c0f\u6797 \u5065
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u591a\u91cd\u5171\u7dda\u6027\u3092\u8003\u616e\u3057\u305f\u56de\u5e30\u5f0f\u306e\u5909\u6570\u9078\u629e\u554f\u984c\u306b\u5bfe\u3059\u308b\u6df7\u5408\u6574\u6570\u8a08\u753b\u6cd5\u3092\u7528\u3044\u305f\u53b3\u5bc6\u89e3\u6cd5<\/td>\n\u4e2d\u7530\u548c\u79c0\u51c6\u6559\u6388<\/td>\n<\/tr>\n
33<\/td>\n2015<\/td>\n\u9ad8\u7530\u967d\u4ecb
\n\uff08\u540d\u53e4\u5c4b\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u88ab\u8986\u5236\u7d04\u4ed8\u304d\u914d\u9001\u8a08\u753b\u554f\u984c\u306b\u5bfe\u3059\u308b\u53cd\u5fa9\u5c40\u6240\u63a2\u7d22\u6cd5<\/td>\n\u67f3\u6d66\u7766\u61b2\u6559\u6388
\n\u6a4b\u672c\u82f1\u6a39\u52a9\u6559<\/td>\n<\/tr>\n
33<\/td>\n2015<\/td>\n\u6771\u91ce\u514b\u54c9
\n\uff08\u6771\u4eac\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u5171\u6b63\u5b9a\u5024\u8a08\u753b\u6cd5\u306b\u57fa\u3065\u304f\u6700\u5927\u5b89\u5b9a\u96c6\u5408\u554f\u984c\u306b\u5bfe\u3059\u308b\u30d2\u30e5\u30fc\u30ea\u30b9\u30c6\u30a3\u30af\u30b9<\/td>\n\u5e73\u4e95\u5e83\u5fd7\u51c6\u6559\u6388<\/td>\n<\/tr>\n
33<\/td>\n2015<\/td>\n\u85e4\u4e95\u6d77\u6597
\n\uff08\u6771\u4eac\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u5236\u7d04\u3064\u304d\u5358\u8abf\u52a3\u30e2\u30b8\u30e5\u30e9\u95a2\u6570\u6700\u5927\u5316\u3068\u305d\u306e\u5fdc\u7528<\/td>\n\u5ca9\u7530 \u899a\u6559\u6388<\/td>\n<\/tr>\n
32<\/td>\n2014<\/td>\n\u4e94\u5341\u5d50\u6b69\u7f8e
\n\uff08\u7b51\u6ce2\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Properties and Computation of a Solution for Cost Allocation Games on Intersecting Families<\/td>\n\u5c71\u672c\u82b3\u55e3\u6559\u6388<\/td>\n<\/tr>\n
32<\/td>\n2014<\/td>\nWu Wei (\u5449\u5049)
\n\uff08\u540d\u53e4\u5c4b\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Heuristic and Exact Algorithms for the Interval Min-Max Regret Generalized Assignment Problem<\/td>\n\u67f3\u6d66\u7766\u61b2\u6559\u6388<\/td>\n<\/tr>\n
32<\/td>\n2014<\/td>\n\u5bae\u5185\u6566\u53f2
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		A Study on Modularity Maximization<\/td>\n\u4e2d\u7530\u548c\u79c0\u51c6\u6559\u6388<\/td>\n<\/tr>\n
32<\/td>\n2014<\/td>\n\u5c71\u4e4b\u5185\u4eae\u4ecb
\n\uff08\u5357\u5c71\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u9023\u7d50\u5236\u7d04\u3068\u88ab\u8986\u5236\u7d04\u3092\u6301\u3064\u65bd\u8a2d\u914d\u7f6e\u554f\u984c\u306b\u5bfe\u3059\u308b\u767a\u898b\u7684\u89e3\u6cd5<\/td>\n\u4f50\u3005\u6728\u7f8e\u88d5\u51c6\u6559\u6388<\/td>\n<\/tr>\n
32<\/td>\n2014<\/td>\n\u6a2a\u4e95 \u512a
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Study on Stable Allocations in Two-Sided Discrete-Concave Market<\/td>\n\u5ba4\u7530\u4e00\u96c4\u6559\u6388<\/td>\n<\/tr>\n
31<\/td>\n2013<\/td>\n\u5927\u4e95\u4e00\u8f1d
\n\uff08\u4eac\u90fd\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u30de\u30eb\u30b3\u30d5\u9023\u9396\u3092\u7528\u3044\u305f\u91ce\u7403\u306b\u304a\u3051\u308b\u72b6\u6cc1\u5225\u52dd\u7387\u8a08\u7b97\u3068\u305d\u306e\u5fdc\u7528<\/td>\n\u5c71\u4e0b\u4fe1\u96c4\u51c6\u6559\u6388<\/td>\n<\/tr>\n
31<\/td>\n2013<\/td>\n\u5ddd\u51fa \u9759
\n\uff08\u540d\u53e4\u5c4b\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		2\u7a2e\u985e\u306e\u56f3\u5f62\u306b\u3088\u308b\u30bf\u30a4\u30ea\u30f3\u30b0\u751f\u6210\u30fc\u56f3\u5f62\u306e\u63a5\u5408\u3092\u7528\u3044\u305f\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u69cb\u7bc9\u30fc<\/td>\n\u4eca\u5800\u614e\u6cbb\u51c6\u6559\u6388<\/td>\n<\/tr>\n
31<\/td>\n2013<\/td>\n\u76f8\u99ac\u8f14
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Fast Deterministic Algorithms for Matrix Completion Problems<\/td>\n\u5ca9\u7530 \u899a\u6559\u6388<\/td>\n<\/tr>\n
31<\/td>\n2013<\/td>\n\u7530\u4e2d\u5f70\u6d69
\n\uff08\u7b51\u6ce2\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u884c\u5217\u306e\u5171\u6b63\u5024\u6027\u3092\u5224\u5b9a\u3059\u308b\u65b0\u3057\u3044\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u63d0\u6848<\/td>\n\u5409\u702c\u7ae0\u5b50\u6559\u6388<\/td>\n<\/tr>\n
31<\/td>\n2013<\/td>\n\u6a4b\u672c\u5927\u6a39
\n\uff08\u6771\u4eac\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u7bc0\u70b9\u4f4d\u7f6e\u306e\u4e0d\u78ba\u5b9f\u6027\u3092\u8003\u616e\u3057\u305f\u30c8\u30e9\u30b9\u69cb\u9020\u306e\u30ed\u30d0\u30b9\u30c8\u6700\u9069\u5316\u6cd5<\/td>\n\u5bd2\u91ce\u5584\u535a\u51c6\u6559\u6388<\/td>\n<\/tr>\n
31<\/td>\n2013<\/td>\n\u80e1 \u8276\u6960
\n\uff08\u540d\u53e4\u5c4b\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Heuristic Algorithms for the Rectilinear Block Packing Problem<\/td>\n\u67f3\u6d66\u7766\u61b2\u6559\u6388
\n\u4eca\u5800\u614e\u6cbb\u51c6\u6559\u6388
\n\u6a4b\u672c\u82f1\u6a39\u52a9\u6559<\/td>\n<\/tr>\n
30<\/td>\n2012<\/td>\n\u5ddd\u5cf6\u5927\u8cb4
\n\uff08\u540d\u53e4\u5c4b\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		3\u6b21\u5143\u7bb1\u8a70\u3081\u554f\u984c\u306b\u5bfe\u3059\u308b2\u3064\u306e\u69cb\u7bc9\u578b\u89e3\u6cd5\u306e\u52b9\u7387\u7684\u5b9f\u73fe\u6cd5<\/td>\n\u67f3\u6d66\u7766\u61b2\u6559\u6388
\n\u4eca\u5800\u614e\u6cbb\u51c6\u6559\u6388<\/td>\n<\/tr>\n
30<\/td>\n2012<\/td>\n\u9b8f\u5ddd\u77e9\u7fa9
\n\uff08\u7b51\u6ce2\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Lagrangian relaxation and pegging test for clique partitioning problems<\/td>\n\u5c71\u672c\u82b3\u55e3\u6559\u6388<\/td>\n<\/tr>\n
30<\/td>\n2012<\/td>\n\u65b0\u898b\u670b\u5e83
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		A multiplier method with variable augmented Lagrangian functions<\/td>\n\u5c71\u4e0b\u4fe1\u96c4\u51c6\u6559\u6388<\/td>\n<\/tr>\n
30<\/td>\n2012<\/td>\n\u5e73\u5c71\u525b\u53f2
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u52a3\u30e2\u30b8\u30e5\u30e9\u30b7\u30b9\u30c6\u30e0\u306e\u5206\u5272\u554f\u984c\u306b\u95a2\u3059\u308b\u7814\u7a76<\/td>\n\u7267\u91ce\u548c\u4e45\u51c6\u6559\u6388<\/td>\n<\/tr>\n
30<\/td>\n2012<\/td>\n\u5bae\u5185\u6566\u53f2
\n\uff08\u4e0a\u667a\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u30e2\u30b8\u30e5\u30e9\u30ea\u30c6\u30a3\u306e\u4e0a\u754c\u5024\u7b97\u51fa<\/td>\n\u5bae\u672c\u88d5\u4e00\u90ce\u51c6\u6559\u6388<\/td>\n<\/tr>\n
29<\/td>\n2011<\/td>\n\u7cf8\u67f3\u9806\u6148
\n\uff08\u540d\u53e4\u5c4b\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u91cd\u307f\u4ed8\u304d\u6700\u5927\u72ec\u7acb\u96c6\u5408\u554f\u984c\u306b\u5bfe\u3059\u308b\u5927\u898f\u6a21\u306a\u8fd1\u508d\u3092\u7528\u3044\u305f\u5c40\u6240\u63a2\u7d22\u6cd5<\/td>\n\u67f3\u6d66\u7766\u61b2\u51c6\u6559\u6388
\n\u6a4b\u672c\u82f1\u6a39\u52a9\u6559<\/td>\n<\/tr>\n
29<\/td>\n2011<\/td>\n\u5ca1\u7530\u4f73\u4e5f
\n\uff08\u6771\u4eac\u8fb2\u5de5\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u73fe\u5b9f\u7684\u306a\u5236\u7d04\u3092\u8003\u616e\u3057\u305f\u512a\u7b49\u5217\u8eca\u505c\u8eca\u99c5\u306e\u6c7a\u5b9a<\/td>\n\u5bae\u4ee3\u9686\u5e73\u51c6\u6559\u6388<\/td>\n<\/tr>\n
29<\/td>\n2011<\/td>\n\u7530\u4e2d\u672a\u6765
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		0-1\u6574\u6570\u5909\u6570\u3092\u542b\u3080\u975e\u51f82\u6b21\u6700\u9069\u5316\u554f\u984c\u306b\u5bfe\u3059\u308b\u9762\u7684\u7e2e\u5c0f\u3092\u7528\u3044\u305f\u975e\u8ca0\u534a\u6b63\u5b9a\u5024\u7de9\u548c<\/td>\n\u6c34\u91ce\u771e\u6cbb\u6559\u6388
\n\u4e2d\u7530\u548c\u79c0\u51c6\u6559\u6388<\/td>\n<\/tr>\n
29<\/td>\n2011<\/td>\n\u585a\u7530\u76f4\u6a39
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		FIFO\u30ad\u30e3\u30c3\u30b7\u30e5\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u6d41\u4f53\u89e3\u6790<\/td>\n\u4e09\u597d\u76f4\u4eba\u51c6\u6559\u6388<\/td>\n<\/tr>\n
29<\/td>\n2011<\/td>\n\u677e\u5ddd\u606d\u660e
\n\uff08\u7b51\u6ce2\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u975e\u8ca0\u534a\u6b63\u5b9a\u5024\u8a08\u753b\u554f\u984c\u306b\u5bfe\u3059\u308b\u4e3b\u30d0\u30ea\u30a2\u95a2\u6570\u6cd5<\/td>\n\u5409\u702c\u7ae0\u5b50\u6559\u6388<\/td>\n<\/tr>\n
28<\/td>\n2010<\/td>\n\u6cc9\u3000\u5948\u592e\u7f8e
\n\uff08\u6176\u61c9\u7fa9\u587e\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u30c7\u30dd\u9593\u8f38\u9001\u554f\u984c\u306b\u5bfe\u3059\u308b\u30b0\u30e9\u30d5\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u7814\u7a76<\/td>\n\u7530\u6751\u660e\u4e45\u6559\u6388<\/td>\n<\/tr>\n
28<\/td>\n2010<\/td>\n\u5965\u91ce\u8cb4\u4e4b
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		A Regularized Explicit Exchange Method for Semi-Infinite Programs with an Infinite Number of Second-Order Cone Constraints<\/td>\n\u6797\u3000\u4fca\u4ecb\u52a9\u6559
\n\u798f\u5cf6\u96c5\u592b\u6559\u6388<\/td>\n<\/tr>\n
28<\/td>\n2010<\/td>\n\u6728\u6751\u9054\u660e
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Tail Asymptotics of Markov Chains of GI\/G\/1 Type<\/td>\n\u9ad8\u6a4b\u3000\u8c4a\u6559\u6388<\/td>\n<\/tr>\n
28<\/td>\n2010<\/td>\n\u5c0f\u6cc9\u3000\u62d3
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u30a8\u30c3\u30b7\u30e3\u30fc\u98a8\u30bf\u30a4\u30ea\u30f3\u30b0\u306e\u81ea\u52d5\u751f\u6210<\/td>\n\u6749\u539f\u539a\u5409\u6559\u6388
\n\u5bd2\u91ce\u5584\u535a\u51c6\u6559\u6388<\/td>\n<\/tr>\n
28<\/td>\n2010<\/td>\n\u9b8f\u5ddd\u77e9\u7fa9
\n\uff08\u7b51\u6ce2\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		Gale-Shapley\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3067\u306e\u30d7\u30ed\u30dd\u30fc\u30ba\u3092\u6c7a\u5b9a\u3059\u308b\u5b8c\u5168\u9078\u597d\u30ea\u30b9\u30c8\u306e\u5b58\u5728\u3068\u305d\u306e\u5224\u5b9a\u65b9\u6cd5<\/td>\n\u5c71\u672c\u82b3\u55e3\u6559\u6388<\/td>\n<\/tr>\n
27<\/td>\n2009<\/td>\n\u4eca\u7530\u53cb\u6a39
\n\uff08\u4eac\u90fd\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u5916\u5e73\u9762\u7684\u5316\u5b66\u30b0\u30e9\u30d5\u306e\u7acb\u4f53\u7570\u6027\u4f53\u306b\u5bfe\u3059\u308b\u69cb\u9020\u8868\u73fe\u304a\u3088\u3073\u751f\u6210\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0<\/td>\n\u6c38\u6301\u3000\u4ec1\u6559\u6388<\/td>\n<\/tr>\n
27<\/td>\n2009<\/td>\n\u4e0a\u7530\u5065\u8a5e
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		A Regularized Newton Method without Line Search for Unconstrained Optimization<\/td>\n\u5c71\u4e0b\u4fe1\u96c4\u51c6\u6559\u6388<\/td>\n<\/tr>\n
27<\/td>\n2009<\/td>\n\u52dd\u898b\u4f51\u5e73
\n\uff08\u6771\u4eac\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u96e2\u6563\u65ad\u9762\u7a4d\u3092\u6301\u3064\u69cb\u9020\u7269\u306e\u6700\u9069\u8a2d\u8a08<\/td>\n\u5bd2\u91ce\u5584\u535a\u51c6\u6559\u6388<\/td>\n<\/tr>\n
27<\/td>\n2009<\/td>\n\u897f\u6751\u4eae\u4e00
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Semidefinite programming reformulation for a class of robust optimization problems and its application to robust Nash equilibrium problems<\/td>\n\u6797\u3000\u4fca\u4ecb\u52a9\u6559
\n\u798f\u5cf6\u96c5\u592b\u6559\u6388<\/td>\n<\/tr>\n
27<\/td>\n2009<\/td>\n\u524d\u539f\u8cb4\u61b2
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u4ee3\u6570\u7684\u5bfe\u79f0\u6027\u306b\u3088\u308b\u884c\u5217\u306e\u540c\u6642\u30d6\u30ed\u30c3\u30af\u5bfe\u89d2\u5316\u6cd5<\/td>\n\u5ba4\u7530\u4e00\u96c4\u6559\u6388<\/td>\n<\/tr>\n
27<\/td>\n2009<\/td>\n\u5c71\u53e3\u5927\u8f14
\n\uff08\u4e2d\u592e\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u5de1\u56de\u30c8\u30fc\u30ca\u30e1\u30f3\u30c8\u554f\u984c\u306e\u8fd1\u4f3c\u89e3\u6cd5<\/td>\n\u677e\u4e95\u77e5\u5df1\u6559\u6388<\/td>\n<\/tr>\n
26<\/td>\n2008<\/td>\n\u5927\u5d8b\u9054\u4e5f
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		A Landmark Algorithm for the Time-Dependent Shortest Path Problem<\/td>\n\u6c38\u6301\u3000\u4ec1\u6559\u6388<\/td>\n<\/tr>\n
26<\/td>\n2008<\/td>\n\u7247\u5ca1\u3000\u9054
\n\uff08\u95a2\u897f\u5b66\u9662\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u4e00\u822c\u5316\u5b89\u5b9a\u7d50\u5a5a\u554f\u984c\u306b\u57fa\u3065\u304f\u7814\u7a76\u5ba4\u914d\u5c5e\u554f\u984c\u306e\u6570\u7406\u7684\u8003\u5bdf<\/td>\n\u8328\u6728\u4fca\u79c0\u6559\u6388<\/td>\n<\/tr>\n
26<\/td>\n2008<\/td>\n\u5cb8\u672c\u3000\u4fe1
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Bargaining Outcomes of Patent Licensing in Oligopoly Markets<\/td>\n\u6b66\u85e4\u6ecb\u592b\u6559\u6388<\/td>\n<\/tr>\n
26<\/td>\n2008<\/td>\n\u884c\u7530\u4fee\u4e45
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u6b69\u884c\u53ef\u80fd\u9818\u57df\u306b\u5236\u7d04\u306e\u3042\u308b\u7a7a\u9593\u306b\u304a\u3051\u308b\u6b69\u884c\u30e2\u30c7\u30eb<\/td>\n\u9ad8\u6a4b\u5e78\u96c4\u6559\u6388<\/td>\n<\/tr>\n
26<\/td>\n2008<\/td>\n\u934b\u8c37\u6634\u4e00
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Variational Inequality Approaches to Generalized Nash Equilibrium Problems<\/td>\n\u798f\u5cf6\u96c5\u592b\u6559\u6388<\/td>\n<\/tr>\n
26<\/td>\n2008<\/td>\n\u59da\u3000\u5049\u70fd
\n\uff08\u7b51\u6ce2\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Exploring the relationship between the hedging strategies based on coherent risk measures and the martingale probabilities via optimization approach<\/td>\n\u5c71\u672c\u82b3\u55e3 \u6559\u6388<\/td>\n<\/tr>\n
25<\/td>\n2007<\/td>\n\u5ca9\u4f50\u3000\u5927
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u30cf\u30d6\u30fb\u30a2\u30f3\u30c9\u30fb\u30b9\u30dd\u30fc\u30af\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u8a2d\u8a08\u554f\u984c\u306e\u8fd1\u4f3c\u89e3\u6cd5<\/td>\n\u677e\u4e95\u77e5\u5df1\u6559\u6388
\n\u6749\u539f\u539a\u5409\u6559\u6388<\/td>\n<\/tr>\n
25<\/td>\n2007<\/td>\n\u83ca\u5730\u4e00\u54f2
\n\uff08\u5317\u6d77\u9053\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u6700\u9069\u505c\u6b62\u69cb\u9020\u3092\u3082\u3064\u7d4c\u8def\u4f9d\u5b58\u578b\u30aa\u30d7\u30b7\u30e7\u30f3\u306e\u4fa1\u683c\u8a55\u4fa1<\/td>\n\u6728\u6751\u4fca\u4e00\u6559\u6388<\/td>\n<\/tr>\n
25<\/td>\n2007<\/td>\n\u5c0f\u5bae\u3000\u5f6c
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		A scatter search algorithm for the multi-resource generalized quadratic assignment problem<\/td>\n\u6c38\u6301\u3000\u4ec1\u6559\u6388<\/td>\n<\/tr>\n
25<\/td>\n2007<\/td>\n\u5927\u9ed2\u5065\u592a\u6717
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Algorithmic Computation of the Transient Queue Length Distribution in the BMAP\/D\/c Queue<\/td>\n\u9ad8\u6a4b\u3000\u8c4a\u6559\u6388<\/td>\n<\/tr>\n
25<\/td>\n2007<\/td>\n\u9ad8\u6fa4\u517c\u4e8c\u90ce
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		A Unified Approach to Combinatorial Algorithms for Matchings and Matroids<\/td>\n\u5ba4\u7530\u4e00\u96c4\u6559\u6388
\n\u5ca9\u7530\u3000\u899a\u51c6\u6559\u6388<\/td>\n<\/tr>\n
25<\/td>\n2007<\/td>\n\u9ad8\u677e\u745e\u4ee3
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Discrete Optimization Approach to Index Reduction for Differential-Algebraic Equations<\/td>\n\u5ba4\u7530\u4e00\u96c4\u6559\u6388
\n\u5ca9\u7530\u3000\u899a\u51c6\u6559\u6388<\/td>\n<\/tr>\n
25<\/td>\n2007<\/td>\n\u6d41\u738b\u667a\u5b50
\n\uff08\u7b51\u6ce2\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u9806\u5e8f\u4ed8\u3051\u5c3a\u5ea6\u306e\u30b2\u30fc\u30e0\u8ad6\u7684\u89e3\u91c8\u3068\u6570\u5024\u8a08\u7b97\u306b\u3088\u308b\u691c\u8a3c<\/td>\n\u5c71\u672c\u82b3\u55e3\u6559\u6388<\/td>\n<\/tr>\n
24<\/td>\n2006<\/td>\n\u4eca\u9053\u8cb4\u53f8
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Packing Non-Convex Polygons by Iterated Local Search Based on Nonlinear Programming<\/td>\n\u6c38\u6301\u3000\u4ec1\u6559\u6388<\/td>\n<\/tr>\n
24<\/td>\n2006<\/td>\n\u5ddd\u53e3\u6643\u53f2
\n\uff08\u4eac\u90fd\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u9762\u7a4d\u4ed8\u304d\u5e73\u9762\u30b0\u30e9\u30d5\u306b\u5bfe\u3059\u308b\u5b9a\u6570\u89d2\u5f62\u76f4\u4ea4\u63cf\u753b<\/td>\n\u6c38\u6301\u3000\u4ec1\u6559\u6388<\/td>\n<\/tr>\n
24<\/td>\n2006<\/td>\n\u5317\u539f\u77e5\u5c31
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u5bfe\u79f0\u9310\u8a08\u753b\u6cd5\u3092\u7528\u3044\u305f\u5224\u5225\u554f\u984c\u306e\u89e3\u6cd5<\/td>\n\u6c34\u91ce\u771e\u6cbb\u6559\u6388<\/td>\n<\/tr>\n
24<\/td>\n2006<\/td>\n\u4e2d\u539f\u5b5d\u4fe1
\n\uff08\u5927\u962a\u5e9c\u7acb\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u6728\u69cb\u9020\u30c7\u30fc\u30bf\u304b\u3089\u6709\u52b9\u306a\u30d1\u30bf\u30fc\u30f3\u3092\u62bd\u51fa\u3059\u308b\u305f\u3081\u306e\u30b0\u30e9\u30d5\u30de\u30a4\u30cb\u30f3\u30b0\u306b\u95a2\u3059\u308b\u7814\u7a76<\/td>\n\u68ee\u7530\u88d5\u4e4b\u52a9\u6559\u6388<\/td>\n<\/tr>\n
24<\/td>\n2006<\/td>\n\u677e\u7530\u62d3\u90ce
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u5f37\u9023\u7d50\u6709\u5411\u30b0\u30e9\u30d5\u4e0a\u306e\u6574\u5408\u5186\u9806\u5217<\/td>\n\u5ca9\u7530\u3000\u899a\u52a9\u6559\u6388<\/td>\n<\/tr>\n
24<\/td>\n2006<\/td>\n\u677e\u672c\u4e00\u8f1d
\n\uff08\u95a2\u897f\u5b66\u9662\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u6bb5\u30dc\u30fc\u30eb\u88fd\u9020\u30b9\u30b1\u30b8\u30e5\u30fc\u30ea\u30f3\u30b0\u554f\u984c\u306e\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3068 \u8a08\u7b97\u306e\u8907\u96d1\u3055\u306b\u95a2\u3059\u308b\u7814\u7a76<\/td>\n\u8328\u6728\u4fca\u79c0\u6559\u6388<\/td>\n<\/tr>\n
23<\/td>\n2005<\/td>\n\u57a3\u6751\u5c1a\u5fb3
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Combinatorial Matrix Analysis by Sign Patterns<\/td>\n\u5ca9\u7530\u3000\u899a\u52a9\u6559\u6388<\/td>\n<\/tr>\n
23<\/td>\n2005<\/td>\n\u5271\u6301\u5149\u4fca
\n\uff08\u4eac\u90fd\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u77e9\u5f62\u30d1\u30c3\u30ad\u30f3\u30b0\u554f\u984c\u306b\u5bfe\u3059\u308b\u53b3\u5bc6\u89e3\u6cd5<\/td>\n\u6c38\u6301\u3000\u4ec1\u6559\u6388<\/td>\n<\/tr>\n
23<\/td>\n2005<\/td>\n\u5c0f\u6797\u4f51\u8f14
\n\uff08\u6771\u4eac\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u30b0\u30e9\u30d5\u306e\u5411\u304d\u4ed8\u3051\u306b\u95a2\u3059\u308b\u6700\u9069\u5316\u554f\u984c\u306e\u89e3\u6cd5<\/td>\n\u5ca9\u7530\u3000\u899a\u52a9\u6559\u6388<\/td>\n<\/tr>\n
23<\/td>\n2005<\/td>\n\u5742\u4e0b\u9ebb\u91cc\u5b50
\n\uff08\u5927\u962a\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Minimum Cost Source Location Problems with Flow Requirements<\/td>\n\u7267\u91ce\u548c\u4e45\u52a9\u6559\u6388
\n\u4e7e\u53e3\u96c5\u5f18\u6559\u6388<\/td>\n<\/tr>\n
23<\/td>\n2005<\/td>\n\u6c38\u91ce\u6e05\u4ec1
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u52a3\u30e2\u30b8\u30e5\u30e9\u591a\u9762\u4f53\u4e0a\u306e\u6700\u9069\u5316\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u7814\u7a76<\/td>\n\u677e\u4e95\u77e5\u5df1\u52a9\u6559\u6388<\/td>\n<\/tr>\n
23<\/td>\n2005<\/td>\n\u897f\u539f\u3000\u7406
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		The Relation between Option Pricing and Optimization Problems<\/td>\n\u6c38\u6301\u3000\u4ec1\u6559\u6388<\/td>\n<\/tr>\n
23<\/td>\n2005<\/td>\n\u677e\u672c\u7acb\u5b50
\n\uff08\u7b51\u6ce2\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u79fb\u52d5\u8ddd\u96e2\u306b\u7740\u76ee\u3057\u305f\u591c\u9593\u5c0f\u5150\u533b\u7642\u65bd\u8a2d\u914d\u7f6e<\/td>\n\u8170\u585a\u6b66\u5fd7\u526f\u5b66\u9577<\/td>\n<\/tr>\n
23<\/td>\n2005<\/td>\n\u5c71\u7530\u525b\u53f2
\n\uff08\u6771\u4eac\u8fb2\u5de5\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u96fb\u5b50\u90e8\u54c1\u88c5\u7740\u6a5f\u306b\u304a\u3051\u308b\u6700\u9069\u5316\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u7814\u7a76<\/td>\n\u4e2d\u68ee\u771e\u7406\u96c4\u6559\u6388<\/td>\n<\/tr>\n
22<\/td>\n2004<\/td>\n\u6c93\u540d\u62d3\u90ce
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Optimal Design of PAC-Companion Structure for Mortgage Backed Securities Using Cash Reserve<\/td>\n\u798f\u5cf6\u96c5\u592b\u6559\u6388<\/td>\n<\/tr>\n
22<\/td>\n2004<\/td>\n\u7956\u7236\u6c5f\u8b19\u4ecb
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		An Iterated Local Search Algorithm for Vehicle Routing and Scheduling Problems with Convex Time Penalty Functions<\/td>\n\u8328\u6728\u4fca\u79c0\u6559\u6388<\/td>\n<\/tr>\n
22<\/td>\n2004<\/td>\n\u9ad8\u6a4b\u6210\u6643
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Accuracies of Decomposition-type Approximate Models for Large-Scale Mobile Communication Networks<\/td>\n\u9ad8\u6a4b\u5e78\u96c4\u6559\u6388<\/td>\n<\/tr>\n
22<\/td>\n2004<\/td>\n\u91dd\u8c37\u5c1a\u5e78
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u96e2\u6563\u6700\u9069\u5316\u624b\u6cd5\u306b\u3088\u308b\u5909\u91cf\u306e\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0<\/td>\n\u5ca9\u7530\u3000\u899a\u52a9\u6559\u6388<\/td>\n<\/tr>\n
22<\/td>\n2004<\/td>\n\u5e73\u4e95\u5e83\u5fd7
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u6709\u9650\u8ddd\u96e2\u7a7a\u9593\u306e\u96e2\u6563\u51f8\u6027<\/td>\n\u5ba4\u7530\u4e00\u96c4\u6559\u6388<\/td>\n<\/tr>\n
22<\/td>\n2004<\/td>\n\u85e4\u7530\u5b66\u6d0b
\n\uff08\u7b51\u6ce2\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u653e\u5c04\u74b0\u72b6\u578b\u4ea4\u901a\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u9069\u6b63\u914d\u7f6e\u3068\u305d\u306e\u6574\u5099\u52b9\u679c\u306b\u95a2\u3059\u308b\u6570\u7406\u7684\u7814\u7a76<\/td>\n\u9234\u6728\u3000\u52c9\u52a9\u6559\u6388<\/td>\n<\/tr>\n
22<\/td>\n2004<\/td>\n\u677e\u5ca1\u7950\u6cbb
\n\uff08\u6771\u4eac\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u6700\u5927\u96a3\u63a5\u9806\u5e8f\u3092\u7528\u3044\u305f\u6700\u5927\u6d41\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u6539\u826f\u3068\u5b9f\u88c5<\/td>\n\u5ca9\u7530\u3000\u899a\u52a9\u6559\u6388<\/td>\n<\/tr>\n
21<\/td>\n2003<\/td>\n\u4e0a\u5712\u667a\u5927
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u30de\u30eb\u30c1\u30f3\u30b2\u30fc\u30eb\u5909\u63db\u3092\u7528\u3044\u305f\u30a2\u30e1\u30ea\u30ab\u30f3\u30aa\u30d7\u30b7\u30e7\u30f3\u4fa1\u683c\u306e\u4e0a\u9650\u8a55\u4fa1<\/td>\n\u9ad8\u6a4b\u5e78\u96c4\u6559\u6388<\/td>\n<\/tr>\n
21<\/td>\n2003<\/td>\n\u4f50\u85e4\u572d\u4ecb
\n\uff08\u7b51\u6ce2\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		A Two-Phase Optimization Method for Virtual Topology Design and Routing of Multi-Hop WDM Networks<\/td>\n\u5c71\u672c\u82b3\u55e3\u6559\u6388<\/td>\n<\/tr>\n
21<\/td>\n2003<\/td>\n\u6a4b\u672c\u82f1\u6a39
\n\uff08\u4eac\u90fd\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u79fb\u52d5\u6642\u9593\u30b3\u30b9\u30c8\u95a2\u6570\u3092\u8003\u616e\u3057\u305f\u6642\u9593\u67a0\u3064\u304d\u914d\u9001\u8a08\u753b\u554f\u984c\u306b\u5bfe\u3059\u308b\u5c40\u6240\u63a2\u7d22\u6cd5<\/td>\n\u8328\u6728\u4fca\u79c0\u6559\u6388<\/td>\n<\/tr>\n
20<\/td>\n2002<\/td>\n\u6a58\u3000\u62d3\u81f3
\n\uff08\u5948\u826f\u5148\u7aef\u79d1\u5b66\u6280\u8853\u5927\u4fee\u8ad6\uff09<\/td>\n		Dynamic Light-path Configuration with GMPLS for WDM Networks<\/td>\n\u7b20\u539f\u6b63\u6cbb\u52a9\u6559\u6388<\/td>\n<\/tr>\n
20<\/td>\n2002<\/td>\n\u4e2d\u6850\u88d5\u5b50
\n\uff08\u6176\u61c9\u7fa9\u587e\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u4f1d\u67d3\u75c5\u306e\u6d41\u884c\u306b\u4f34\u3046\u500b\u4f53\u6570\u5909\u52d5\u306e\u5fae\u5206\u65b9\u7a0b\u5f0f\u30e2\u30c7\u30eb\uff0d\u72c2\u725b\u75c5\u767a\u75c7\u30c7\u30fc\u30bf\u3078\u306e\u30b3\u30f3\u30d1\u30fc\u30c8\u30e1\u30f3\u30c8\u30e2\u30c7\u30eb\u306e\u9069\u7528\uff0d<\/td>\n\u6817\u7530\u3000\u6cbb\u6559\u6388<\/td>\n<\/tr>\n
20<\/td>\n2002<\/td>\n\u4e2d\u6797\u3000\u5065
\n\uff08\u653f\u7b56\u7814\u7a76\u5927\u5b66\u9662\u5927\u4fee\u8ad6\uff09<\/td>\n		\u300c\u56fd\u529b\u306b\u5fdc\u3058\u305f\u8ecd\u4e8b\u529b\u300d\u306e\u56fd\u969b\u6bd4\u8f031984-1997<\/td>\n\u5200\u6839\u3000\u85ab\u6559\u6388<\/td>\n<\/tr>\n
20<\/td>\n2002<\/td>\n\u85e4\u7530\u967d\u5b50
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u96fb\u8eca\u5185\u306b\u304a\u3051\u308b\u4eba\u306e\u4e57\u964d\u7acb\u3061\u4f4d\u7f6e\u30e2\u30c7\u30eb<\/td>\n\u9ad8\u6a4b\u5e78\u96c4\u6559\u6388<\/td>\n<\/tr>\n
20<\/td>\n2002<\/td>\n\u677e\u6751\u9ad8\u5b8f
\n\uff08\u5343\u8449\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u6025\u884c\u7cfb\u96fb\u8eca\u306e\u8a2d\u5b9a\u65b9\u6cd5\u306b\u95a2\u3059\u308b\u7814\u7a76<\/td>\n\u9234\u6728\u8aa0\u9053\u6559\u6388<\/td>\n<\/tr>\n
20<\/td>\n2002<\/td>\n\u5bae\u5ddd\u96c5\u81f3
\n\uff08\u7b51\u6ce2\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u5229\u7528\u8005\u304b\u3089\u306e\u8ddd\u96e2\u306b\u7740\u76ee\u3057\u305f\u898f\u5247\u7684\u65bd\u8a2d\u914d\u7f6e\u306e\u9811\u5065\u6027<\/td>\n\u5927\u6fa4\u7fa9\u660e\u6559\u6388<\/td>\n<\/tr>\n
19<\/td>\n2001<\/td>\n\u4eca\u5800\u614e\u6cbb
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u914d\u7f6e\u30b3\u30b9\u30c8\u3092\u3082\u3064\u9577\u65b9\u5f62\u8a70\u8fbc\u307f\u554f\u984c\u306b\u5bfe\u3059\u308b\u5c40\u6240\u63a2\u7d22\u6cd5\u306b\u3064\u3044\u3066<\/td>\n\u8328\u6728\u4fca\u79c0\u6559\u6388<\/td>\n<\/tr>\n
19<\/td>\n2001<\/td>\n\u5c0f\u539f\u6731\u7406
\n\uff08\u7b51\u6ce2\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u76f8\u4e92\u8a55\u4fa1\u306e\u4e0b\u3067\u306e\u53ef\u80fd\u6027\u5b9a\u7406<\/td>\n\u5c71\u672c\u82b3\u55e3\u6559\u6388<\/td>\n<\/tr>\n
19<\/td>\n2001<\/td>\n\u9ad8\u6a4b\u5229\u81e3
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Bounds of Performance Measures in Large-Scale Mobile Communication Networks<\/td>\n\u9ad8\u6a4b\u5e78\u96c4\u6559\u6388<\/td>\n<\/tr>\n
19<\/td>\n2001<\/td>\n\u6b66\u85e4\u6b63\u7fa9
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u5229\u4ed6\u7684\u52b9\u7528\u95a2\u6570\u306b\u3088\u308b\u5354\u529b\u7684\u79e9\u5e8f\u5f62\u6210\u306e\u53ef\u80fd\u6027\uff0d\u9032\u5316\u30b2\u30fc\u30e0\u7406\u8ad6\u7684\u30a2\u30d7\u30ed\u30fc\u30c1\uff0d<\/td>\n\u6b66\u85e4\u6ecb\u592b\u6559\u6388<\/td>\n<\/tr>\n
19<\/td>\n2001<\/td>\n\u6e21\u90e8\u5927\u8f14
\n\uff08\u7b51\u6ce2\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u53ce\u96c6\u30fb\u914d\u9001\u8f38\u9001\u30b7\u30b9\u30c6\u30e0\u306b\u304a\u3051\u308b\u968e\u5c64\u69cb\u9020\u306e\u6700\u9069\u5316\u306b\u95a2\u3059\u308b\u7814\u7a76<\/td>\n\u9234\u6728\u3000\u52c9\u8b1b\u5e2b<\/td>\n<\/tr>\n
18<\/td>\n2000<\/td>\n\u4e95\u4e0a\u3000\u5927
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Sojourn Time in a Queue with Clustered Periodic Arrivals<\/td>\n\u6edd\u6839\u54f2\u54c9\u52a9\u6559\u6388<\/td>\n<\/tr>\n
18<\/td>\n2000<\/td>\n\u5ca1\u672c\u8cb4\u7ae0
\n\uff08\u6176\u61c9\u7fa9\u587e\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u6a4b\u306e\u9069\u6b63\u914d\u7f6e\u30e2\u30c7\u30eb\uff0d\u99c5\u69cb\u5185\u9023\u7d61\u901a\u8def\u306e\u8a2d\u8a08\u30fb\u8a55\u4fa1\u3078\u306e\u5fdc\u7528\uff0d<\/td>\n\u6817\u7530\u3000\u6cbb\u52a9\u6559\u6388<\/td>\n<\/tr>\n
18<\/td>\n2000<\/td>\n\u5cb8\u7530\u6b63\u535a
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u96c6\u5408\u88ab\u8986\u554f\u984c\u306b\u5bfe\u3059\u308b3\u53cd\u8ee2\u8fd1\u508d\u3092\u7528\u3044\u305f\u5c40\u6240\u63a2\u7d22\u6cd5<\/td>\n\u8328\u6728\u4fca\u79c0\u6559\u6388<\/td>\n<\/tr>\n
18<\/td>\n2000<\/td>\n\u4f50\u85e4\u5168\u5bdb
\n\uff08\u5927\u962a\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u591a\u54c1\u7a2e\u6700\u5927\u6d41\u554f\u984c\u306b\u5bfe\u3059\u308b\u52b9\u7387\u7684\u8fd1\u4f3c\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0<\/td>\n\u85e4\u91cd\u3000\u609f\u6559\u6388<\/td>\n<\/tr>\n
18<\/td>\n2000<\/td>\n\u4e2d\u6850\u88d5\u5b50
\n\uff08\u6176\u61c9\u7fa9\u587e\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u968e\u5c64\u69cb\u9020\u3092\u6709\u3059\u308b\u6210\u9577\u73fe\u8c61\u306e\u5fae\u5206\u65b9\u7a0b\u5f0f\u30e2\u30c7\u30eb\uff0d\u5bb6\u5ead\u7528\u30b2\u30fc\u30e0\u6a5f\u306e\u8ca9\u58f2\u5b9f\u7e3e\u306b\u57fa\u3065\u304f\u5206\u6790\u4f8b\uff0d<\/td>\n\u6817\u7530\u3000\u6cbb\u52a9\u6559\u6388<\/td>\n<\/tr>\n
17<\/td>\n1999<\/td>\n\u5150\u7389\u88d5\u4e00\u90ce
\n\uff08\u57fc\u7389\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u6570\u7406\u8a08\u753b\u30e2\u30c7\u30eb\u306e\u9069\u7528\u306b\u3088\u308b\u90fd\u5e02\u4ea4\u901a\u7ba1\u7406\u653f\u7b56\u306e\u8a55\u4fa1\u306b\u95a2\u3059\u308b\u7814\u7a76<\/td>\n\u5927\u5c71\u9054\u96c4\u6559\u6388<\/td>\n<\/tr>\n
17<\/td>\n1999<\/td>\n\u5742\u53e3\u3000\u9686
\n\uff08\u96fb\u6c17\u901a\u4fe1\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u99c5\u69cb\u5185\u5165\u308c\u63db\u3048\u8a08\u753b\u554f\u984c\u306b\u95a2\u3059\u308b\u7814\u7a76<\/td>\n\u7530\u6751\u660e\u4e45\u52a9\u6559\u6388<\/td>\n<\/tr>\n
17<\/td>\n1999<\/td>\n\u798f\u7530\u5149\u6d69
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Branch-and-cut algorithms for bilinear matrix inequality problems<\/td>\n\u5c0f\u5cf6\u653f\u548c\u6559\u6388<\/td>\n<\/tr>\n
17<\/td>\n1999<\/td>\n\u5897\u7530\u53cb\u6cf0
\n\uff08\u4eac\u90fd\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u6642\u9593\u67a0\u5236\u7d04\u4ed8\u304d\u914d\u9001\u8a08\u753b\u554f\u984c\u306b\u5bfe\u3059\u308b\u5c40\u6240\u63a2\u7d22\u6cd5\u306e\u9069\u7528\u306b\u3064\u3044\u3066<\/td>\n\u8328\u6728\u4fca\u79c0\u6559\u6388<\/td>\n<\/tr>\n
16<\/td>\n1998<\/td>\n\u52a0\u85e4\u61b2\u4e00
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Asymptotic Analysis of Tail Probabilities in Queueing Models with Markovian Arrival Processes<\/td>\n\u7267\u672c\u76f4\u6a39\u8b1b\u5e2b
\n\u9ad8\u6a4b\u5e78\u96c4\u6559\u6388<\/td>\n<\/tr>\n
16<\/td>\n1998<\/td>\n\u6d2a\u3000\u6642\u5b97
\n\uff08\u6771\u4eac\u7406\u79d1\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u4eba\u4e8b\u8a55\u4fa1\u306b\u9069\u7528\u3057\u305f\u30b0\u30eb\u30fc\u30d7AHP\u306e\u7814\u7a76\uff0d\u8a55\u4fa1\u57fa\u6e96\u306b\u95a2\u3059\u308b\u5408\u610f\u5f62\u6210\uff0d<\/td>\n\u5c71\u7530\u5584\u9756\u6559\u6388<\/td>\n<\/tr>\n
16<\/td>\n1998<\/td>\n\u67f4\u7530\u96c5\u535a
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		The Extended Semidefinite Linear Complementarity Problem: A Reformulation Approach<\/td>\n\u798f\u5cf6\u96c5\u592b\u6559\u6388<\/td>\n<\/tr>\n
16<\/td>\n1998<\/td>\n\u4e2d\u6751\u5927\u771f
\n\uff08\u96fb\u6c17\u901a\u4fe1\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		On the maximum weight stable set problem and its extension for claw-free graphs<\/td>\n\u7530\u6751\u660e\u4e45\u52a9\u6559\u6388<\/td>\n<\/tr>\n
16<\/td>\n1998<\/td>\n\u5bae\u672c\u88d5\u4e00\u90ce
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u30c1\u30e3\u30f3\u30cd\u30eb\u5272\u5f53\u554f\u984c\u306e\u89e3\u6cd5<\/td>\n\u677e\u4e95\u77e5\u5df1\u52a9\u6559\u6388<\/td>\n<\/tr>\n
15<\/td>\n1997<\/td>\n\u4e00\u4e0a\u3000\u97ff
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u30d5\u30a1\u30a4\u30ca\u30f3\u30b9\u306b\u304a\u3051\u308b\u53d6\u308a\u5f15\u304d\u30b3\u30b9\u30c8\u3092\u8003\u616e\u3057\u305f\u30ea\u30b9\u30af\u56de\u907f\u6226\u7565<\/td>\n\u4f0f\u898b\u6b63\u5247\u6559\u6388<\/td>\n<\/tr>\n
15<\/td>\n1997<\/td>\n\u9ad8\u7530\u5bdb\u4e4b
\n\uff08\u6771\u4eac\u7406\u79d1\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		A Method Identify Product Form in Queueing Networks<\/td>\n\u5bae\u6ca2\u653f\u6e05\u6559\u6388<\/td>\n<\/tr>\n
15<\/td>\n1997<\/td>\n\u91ce\u3005\u90e8\u5b8f\u53f8
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		General Purpose Heuristic Algorithms for Combinatorial Problems via CSP<\/td>\n\u8328\u6728\u4fca\u79c0\u6559\u6388<\/td>\n<\/tr>\n
15<\/td>\n1997<\/td>\n\u6e80\u6c5f\u6b63\u535a
\n\uff08\u57fc\u7389\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u90fd\u5e02\u306e\u30de\u30cd\u30b8\u30e1\u30f3\u30c8\u306b\u95a2\u3059\u308b\u7814\u7a76\uff0dOR\u3068\u306e\u5354\u6f14\u306b\u3088\u308b\u300c\u798f\u5ca1\u5275\u9020\u300d\u306e\u63a2\u7a76\uff0d<\/td>\n\u5200\u6839\u3000\u85ab\u6559\u6388<\/td>\n<\/tr>\n
14<\/td>\n1996<\/td>\n\u4e95\u95a2\u4e00\u9686
\n\uff08\u6176\u61c9\u7fa9\u587e\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u30d3\u30eb\u9593\u9ad8\u67b6\u9023\u7d61\u901a\u8def\u306e\u6700\u9069\u914d\u7f6e<\/td>\n\u6817\u7530\u3000\u6cbb\u52a9\u6559\u6388<\/td>\n<\/tr>\n
14<\/td>\n1996<\/td>\n\u8535\u6749\u4fca\u5eb7
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Data Analysis and Modeling of ATM Coded Video Traffic with Scene Changes<\/td>\n\u9ad8\u6a4b\u5e78\u96c4\u6559\u6388
\n\u7267\u672c\u76f4\u6a39\u8b1b\u5e2b<\/td>\n<\/tr>\n
14<\/td>\n1996<\/td>\n\u4f50\u3005\u6728\u3000\u6df3
\n\uff08\u8c4a\u6a4b\u6280\u79d1\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		AGV\u30b7\u30b9\u30c6\u30e0\u306e\u7406\u8ad6\u7684\u89e3\u6790<\/td>\n\u5897\u5c71\u3000\u7e41\u52a9\u6559\u6388<\/td>\n<\/tr>\n
14<\/td>\n1996<\/td>\n\u897f\u672c\u548c\u535a
\n\uff08\u57fc\u7389\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u6d77\u96e3\u4e8b\u6545\u4ef6\u6570\u306e\u7d71\u8a08\u30e2\u30c7\u30eb\u3068\u5de1\u8996\u8239\u306e\u914d\u5099\u904b\u7528\u306e\u6700\u9069\u5316\u306b\u95a2\u3059\u308b\u7814\u7a76<\/td>\n\u5927\u5c71\u9054\u96c4\u6559\u6388<\/td>\n<\/tr>\n
13<\/td>\n1995<\/td>\n\u671d\u5ca1\u614e\u6cbb
\n\uff08\u7b51\u6ce2\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u7dda\u5f62\u4e0d\u7b49\u5f0f\u3067\u5b9a\u7fa9\u3055\u308c\u305f\u591a\u9762\u4f53\u306e\u6700\u5c0f\u5305\u56f2\u7403\u554f\u984c\u3092\u89e3\u304f\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0<\/td>\n\u5c71\u672c\u82b3\u55e3\u6559\u6388<\/td>\n<\/tr>\n
13<\/td>\n1995<\/td>\n\u5ca9\u5d0e\u8aa0\u53f8
\n\uff08\u57fc\u7389\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u7a0e\u95a2\u4e8b\u5f8c\u8abf\u67fb\u90e8\u9580\u306b\u304a\u3051\u308b\u7acb\u5165\u8f38\u5165\u8005\u9078\u5b9a\u30e2\u30c7\u30eb\u306e\u69cb\u7bc9\u306b\u95a2\u3059\u308b\u7814\u7a76<\/td>\n\u5200\u6839\u3000\u85ab\u6559\u6388<\/td>\n<\/tr>\n
13<\/td>\n1995<\/td>\n\u5869\u6d66\u662d\u7fa9
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Efficient Algorithms for Location Problems on Tree Networks<\/td>\n\u5c0f\u5cf6\u653f\u548c\u6559\u6388<\/td>\n<\/tr>\n
13<\/td>\n1995<\/td>\n\u4e2d\u6751\u4f38\u4e5f
\n\uff08\u4eac\u90fd\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u6700\u7d42\u30c0\u30d6\u30eb\u30aa\u30d5\u30a1\u30fc\u4ef2\u88c1\u306e\u5747\u8861\u6226\u7565\u306b\u3064\u3044\u3066<\/td>\n\u8328\u6728\u4fca\u79c0\u6559\u6388<\/td>\n<\/tr>\n
13<\/td>\n1995<\/td>\n\u5c71\u4e0b\u4fe1\u96c4
\n\uff08\u5948\u826f\u5148\u7aef\u79d1\u5b66\u6280\u8853\u5927\u5b66\u9662\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Equivalent Differentiable Unconstrained Optimization for Complementarity Problems<\/td>\n\u798f\u5cf6\u96c5\u592b\u6559\u6388<\/td>\n<\/tr>\n
12<\/td>\n1994<\/td>\n\u5927\u7af9\u5fb3\u6210
\n\uff08\u6771\u4eac\u7406\u79d1\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		A Study on the Capacitated Traveling Salesmen Location Problem<\/td>\n\u5e73\u6797\u9686\u4e00\u6559\u6388<\/td>\n<\/tr>\n
12<\/td>\n1994<\/td>\n\u7530\u4e2d\u6b66\u5fd7
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Transient analysis of fluid approximation model for multi-entry queueing system in ATM statistical multiplexing<\/td>\n\u9ad8\u6a4b\u5e78\u96c4\u6559\u6388<\/td>\n<\/tr>\n
12<\/td>\n1994<\/td>\n\u85e4\u7530\u654f\u6cbb
\n\uff08\u4e5d\u5dde\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u30d5\u30a1\u30b8\u30a3\u7dda\u5f62\u8a08\u753b\u3068\u30d5\u30a1\u30b8\u30a3\u74b0\u5883\u4e0b\u306b\u304a\u3051\u308b\u78ba\u7387\u7684\u63a8\u79fb\u30b7\u30b9\u30c6\u30e0<\/td>\n\u5ddd\u5d0e\u82f1\u6587\u52a9\u6559\u6388<\/td>\n<\/tr>\n
12<\/td>\n1994<\/td>\n\u85e4\u539f\u7965\u9686
\n\uff08\u7b51\u6ce2\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u5730\u57df\u9593\u9053\u8def\u7db2\u89e3\u6790\u306b\u3088\u308b\u9053\u8def\u8a08\u753b<\/td>\n\u8170\u585a\u6b66\u5fd7\u6559\u6388<\/td>\n<\/tr>\n
12<\/td>\n1994<\/td>\n\u4e09\u6751\u5e84\u4e00
\n\uff08\u57fc\u7389\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u5e02\u55b6\u30d0\u30b9\u8def\u7dda\u6c7a\u5b9a\u554f\u984c\u306b\u5bfe\u3059\u308b\u6570\u7406\u8a08\u753b\u30e2\u30c7\u30eb\u306e\u9069\u7528<\/td>\n\u5927\u5c71\u9054\u96c4\u6559\u6388<\/td>\n<\/tr>\n
11<\/td>\n1993<\/td>\n\u9ad8\u91ce\u6b63\u6b21
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		On Eigenvalues of the Rate Matrix in a PH\/PH\/c Queue<\/td>\n\u9ad8\u6a4b\u5e78\u96c4\u6559\u6388<\/td>\n<\/tr>\n
11<\/td>\n1993<\/td>\n\u7530\u6751\u3000\u76f4
\n\uff08\u6771\u4eac\u7406\u79d1\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u5358\u8abf\u591a\u9762\u4f53\u306e\u6027\u8cea\u3068\u305d\u306e\u96c6\u5408\u5206\u5272\u554f\u984c\u3078\u306e\u9069\u7528<\/td>\n\u5c71\u53e3\u4fca\u548c\u6559\u6388<\/td>\n<\/tr>\n
11<\/td>\n1993<\/td>\nTAN AH CHOON
\n\uff08\u7b51\u6ce2\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		MV\u30e2\u30c7\u30eb\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u8a55\u4fa1\uff0d\u53ef\u80fd\u6027\u66f2\u7dda\u306e\u4e8b\u524d\u4e8b\u5f8c\u5206\u6790\uff0d<\/td>\n\u5c71\u672c\u82b3\u55e3\u6559\u6388<\/td>\n<\/tr>\n
11<\/td>\n1993<\/td>\n\u85e4\u6ca2\u514b\u6a39
\n\uff08\u65e9\u7a32\u7530\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		Tabu Search \u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u7d44\u5408\u305b\u6700\u9069\u5316\u554f\u984c\u3078\u306e\u9069\u7528<\/td>\n\u68ee\u6238\u3000\u664b\u6559\u6388<\/td>\n<\/tr>\n
11<\/td>\n1993<\/td>\n\u5409\u7fbd\u8981\u76f4
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Linear Time Algorithms for Convex Programming<\/td>\n\u4f0f\u898b\u6b63\u5247\u6559\u6388<\/td>\n<\/tr>\n
10<\/td>\n1992<\/td>\n\u4f0a\u85e4\u3000\u7a14
\n\uff08\u6771\u6d77\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u4e00\u822c\u5316\u30b0\u30eb\u30fc\u30d4\u30f3\u30b0\u554f\u984c<\/td>\n\u7fbd\u7530\u9686\u7537\u52a9\u6559\u6388<\/td>\n<\/tr>\n
10<\/td>\n1992<\/td>\n\u5927\u4e45\u4fdd\u7531\u7d00\u5b50
\n\uff08\u7b51\u6ce2\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u30aa\u30d7\u30b7\u30e7\u30f3\u7d44\u307f\u5165\u308c\u30dd\u30fc\u30c8\u30d5\u30a9\u30ea\u30aa\u306e\u53ce\u76ca\u7387\u5206\u5e03\u8a55\u4fa1\u30b7\u30b9\u30c6\u30e0\u306e\u69cb\u7bc9<\/td>\n\u6728\u5cf6\u6b63\u660e\u52a9\u6559\u6388<\/td>\n<\/tr>\n
10<\/td>\n1992<\/td>\n\u571f\u5c4b\u5229\u660e
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		On Discrete-Time Single-Server Queues with Markov Modulated Batch Bernoulli Input and Finite Capacity<\/td>\n\u9ad8\u6a4b\u5e78\u96c4\u6559\u6388<\/td>\n<\/tr>\n
9<\/td>\n1991<\/td>\n\u77f3\u9ed2\u3000\u52f2
\n\uff08\u6771\u4eac\u7406\u79d1\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u975e\u5bfe\u79f0\u5bb9\u91cf\u5236\u7d04\u4ed8\u304d\u914d\u9001\u8def\u6c7a\u5b9a\u554f\u984c\u306e\u89e3\u6cd5<\/td>\n\u897f\u7530\u76f4\u77e9\u6559\u6388<\/td>\n<\/tr>\n
9<\/td>\n1991<\/td>\n\u4fe1\u592a\u6b63\u4e4b
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Structure of Solution Set to Nonlinear Programs with 2 Parameters<\/td>\n\u5c0f\u5cf6\u653f\u548c\u6559\u6388<\/td>\n<\/tr>\n
9<\/td>\n1991<\/td>\n\u798f\u7530\u8def\u5b50
\n\uff08\u7b51\u6ce2\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		Binary Comparison \u306b\u304a\u3051\u308bAHP\u6cd5\u3068\u305d\u306e\u4ed6\u306e\u65b9\u6cd5\u3068\u306e\u6bd4\u8f03<\/td>\n\u9ad8\u6a4b\u78d0\u90ce\u6559\u6388<\/td>\n<\/tr>\n
9<\/td>\n1991<\/td>\n\u5bae\u5cb8\u5b8f\u660e
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u6771\u5de5\u5927\u306e\u5b66\u79d1\u6240\u5c5e\u65b9\u5f0f\u306b\u304a\u3051\u308b\u5b66\u751f\u306e\u7533\u544a\u306e\u5b89\u5b9a\u6027\u306b\u3064\u3044\u3066<\/td>\n\u68ee\u3000\u96c5\u592b\u6559\u6388<\/td>\n<\/tr>\n
9<\/td>\n1991<\/td>\n\u5ba4\u8c37\u6d0b\u4e00
\n\uff08\u57fc\u7389\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u3054\u307f\u51e6\u7406\u65bd\u8a2d\u306e\u6700\u9069\u914d\u7f6e\u306b\u95a2\u3059\u308b\u7814\u7a76\uff0d\u6642\u7cfb\u5217ARIMA\u30e2\u30c7\u30eb\u3068\u6df7\u5408\u578b\u6574\u6570\u8a08\u753b\u30e2\u30c7\u30eb\u306e\u9069\u7528\uff0d<\/td>\n\u5927\u5c71\u9054\u96c4\u6559\u6388<\/td>\n<\/tr>\n
8<\/td>\n1990<\/td>\n\u77f3\u4e95\u5b8f\u548c
\n\uff08\u6771\u4eac\u7406\u79d1\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u6574\u6570\u591a\u9762\u4f53\u7406\u8ad6\u306e\u4e57\u54e1\u30b9\u30b1\u30b8\u30e5\u30fc\u30ea\u30f3\u30b0\u554f\u984c\u3078\u306e\u5fdc\u7528<\/td>\n\u897f\u7530\u76f4\u77e9\u6559\u6388<\/td>\n<\/tr>\n
8<\/td>\n1990<\/td>\n\u8328\u6728\u3000\u667a
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Dual-Based Newton Method for Nonlinear Minimum Cost Network Flow Problems<\/td>\n\u8328\u6728\u4fca\u79c0\u6559\u6388<\/td>\n<\/tr>\n
8<\/td>\n1990<\/td>\n\u9326\u7e54\u7766\u5b50
\n\uff08\u57fc\u7389\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u7269\u8cc7\u60c5\u5831\u6d41\u52d5\u69cb\u9020\u306e\u30a8\u30f3\u30c8\u30ed\u30d4\u30fc\u30e2\u30c7\u30eb\u5206\u6790<\/td>\n\u5927\u5c71\u9054\u96c4\u6559\u6388<\/td>\n<\/tr>\n
8<\/td>\n1990<\/td>\n\u85e4\u4e95\u5149\u4e45
\n\uff08\u57fc\u7389\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u6c34\u8cc7\u6e90\u958b\u767a\u306b\u304a\u3051\u308b\u8cbb\u7528\u5206\u62c5\u5206\u6790\uff0d\u5354\u529b\u30b2\u30fc\u30e0\u7406\u8ad6\u53ca\u591a\u76ee\u7684\u52b9\u7528\u7406\u8ad6\u3092\u7528\u3044\u3066\uff0d<\/td>\n\u5ca1\u7530\u3000\u7ae0\u52a9\u6559\u6388<\/td>\n<\/tr>\n
8<\/td>\n1990<\/td>\n\u77e2\u90e8\u61b2\u4e00
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u679d\u88ab\u8986\u554f\u984c\u3092\u7528\u3044\u305f\u30b9\u30bf\u30a4\u30ca\u30fc\u554f\u984c\u306e\u4e0b\u754c\u5024<\/td>\n\u5c0f\u5cf6\u653f\u548c\u6559\u6388<\/td>\n<\/tr>\n
7<\/td>\n1989<\/td>\n\u5742\u5dfb\u6df3\u4e00
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u975e\u5b9a\u5e38\u306a\u9700\u8981\u95a2\u6570\u3092\u6301\u3064\u591a\u671f\u9593\u5be1\u5360\u5e02\u5834\u306e\u975e\u5354\u529b\u5747\u8861\u70b9<\/td>\n\u5c0f\u5cf6\u653f\u548c\u52a9\u6559\u6388<\/td>\n<\/tr>\n
7<\/td>\n1989<\/td>\n\u733f\u6e21\u5eb7\u6587
\n\uff08\u6771\u4eac\u7406\u79d1\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		A Study on the Matching Theory and the Arc Routing Problem<\/td>\n\u897f\u7530\u76f4\u77e9\u6559\u6388<\/td>\n<\/tr>\n
7<\/td>\n1989<\/td>\n\u8ee2\u99ac\u3000\u6f64
\n\uff08\u57fc\u7389\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u6df7\u5408\u578b\u6574\u6570\u8a08\u753b\u6cd5\u306b\u3088\u308b\u571f\u5730\u5229\u7528\u3068\u9053\u8def\u7db2\u306e\u540c\u6642\u6700\u9069\u5316\u30e2\u30c7\u30eb\u306b\u95a2\u3059\u308b\u7814\u7a76<\/td>\n\u5927\u5c71\u9054\u96c4\u6559\u6388<\/td>\n<\/tr>\n
7<\/td>\n1989<\/td>\n\u4f2f\u91ce\u5353\u5f66
\n\uff08\u6771\u4eac\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u30d3\u30eb\u306b\u304a\u3051\u308b\u907f\u96e3\u306e\u6570\u7406\u30e2\u30c7\u30eb<\/td>\n\u4f0f\u898b\u6b63\u5247\u6559\u6388<\/td>\n<\/tr>\n
7<\/td>\n1989<\/td>\n\u7267\u672c\u76f4\u6a39
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		On Job Schedulings in Stochastic Flow Shops and Related Results in Tandem Queues<\/td>\n\u68ee\u6751\u82f1\u5178\u6559\u6388<\/td>\n<\/tr>\n
6<\/td>\n1988<\/td>\n\u5927\u8c37\u3000\u6d69
\n\uff08\u4e0a\u667a\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u81ea\u52d5\u5009\u5eab\u306b\u304a\u3051\u308b\u5728\u5eab\u5206\u5e03\u306e\u63a8\u5b9a<\/td>\n\u9234\u6728\u8aa0\u9053\u6559\u6388<\/td>\n<\/tr>\n
6<\/td>\n1988<\/td>\n\u5b8b\u3000\u5b87
\n\uff08\u6771\u5317\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u76f4\u5217\u578b\u5f85\u3061\u884c\u5217\u30e2\u30c7\u30eb\u306e\u8fd1\u4f3c\u89e3\u6cd5<\/td>\n\u9ad8\u6a4b\u5e78\u96c4\u6559\u6388<\/td>\n<\/tr>\n
6<\/td>\n1988<\/td>\n\u7af9\u539f\u3000\u5747
\n\uff08\u7b51\u6ce2\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		The Stationary Ball Method for Linear Programming-A Primal Simplex Method with a New Column Selection Rule<\/td>\n\u85e4\u91cd\u3000\u609f\u52a9\u6559\u6388<\/td>\n<\/tr>\n
6<\/td>\n1988<\/td>\n\u91ce\u9593\u4fca\u4eba
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		A Continuation Method for Complementarity Problems<\/td>\n\u5c0f\u5cf6\u653f\u548c\u52a9\u6559\u6388<\/td>\n<\/tr>\n
5<\/td>\n1987<\/td>\n\u4f0a\u85e4\u6b66\u5bff
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u5909\u5206\u4e0d\u7b49\u5f0f\u306b\u5bfe\u3059\u308b\u89e3\u6cd5\u3068\u305d\u306e\u4ea4\u901a\u6d41\u5747\u8861\u554f\u984c\u3078\u306e\u9069\u7528<\/td>\n\u8328\u6728\u4fca\u79c0\u6559\u6388<\/td>\n<\/tr>\n
5<\/td>\n1987<\/td>\n\u7247\u5ca1\u9756\u8a5e
\n\uff08\u65e9\u7a32\u7530\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u5358\u4e00\u5236\u7d04\u4ed8\u6700\u5927\u96c6\u8377\u554f\u984c\u306e\u6700\u9069\u5316\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u958b\u767a<\/td>\n\u68ee\u6238\u3000\u664b\u6559\u6388<\/td>\n<\/tr>\n
5<\/td>\n1987<\/td>\n\u9234\u6728\u5eb7\u4ecb
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u5c0f\u4fee\u7406\uff08minimal repair\uff09\u3092\u4eee\u5b9a\u3057\u306a\u3044\u53d6\u308a\u66ff\u3048\u554f\u984c\u306e\u7814\u7a76<\/td>\n\u68ee\u6751\u82f1\u5178\u6559\u6388<\/td>\n<\/tr>\n
5<\/td>\n1987<\/td>\n\u9ad8\u6a4b\u3000\u5fb9
\n\uff08\u57fc\u7389\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u56fd\u5bb6\u9593\u95a2\u4fc2\u306e\u5206\u6790\u306b\u304a\u3051\u308b\u30b0\u30e9\u30d5\u7406\u8ad6\u304b\u3089\u306e\u63a5\u8fd1<\/td>\n\u5200\u6839\u3000\u85ab\u6559\u6388<\/td>\n<\/tr>\n
5<\/td>\n1987<\/td>\n\u5409\u702c\u7ae0\u5b50
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u7dda\u5f62\u8a08\u753b\u554f\u984c\u306b\u5bfe\u3059\u308b\u65b0\u89e3\u6cd5\u306b\u3064\u3044\u3066\uff0d\u5185\u70b9\u6cd5\u306e\u958b\u767a\u3068\u8a55\u4fa1<\/td>\n\u68ee\u3000\u96c5\u592b\u52a9\u6559\u6388<\/td>\n<\/tr>\n
4<\/td>\n1986<\/td>\n\u571f\u8c37\u3000\u9686
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u9ad8\u901f\u5fae\u5206\u6cd5\u304a\u3088\u3073\u4e38\u3081\u8aa4\u5dee\u63a8\u5b9a\u6cd5\u3068\u305d\u306e\u5fdc\u7528<\/td>\n\u4f0a\u7406\u6b63\u592b\u6559\u6388<\/td>\n<\/tr>\n
4<\/td>\n1986<\/td>\n\u897f\u5ca1\u8aa0\u6cbb
\n\uff08\u57fc\u7389\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u9053\u8def\u4ea4\u901a\u60c5\u5831\u30b7\u30b9\u30c6\u30e0\u306e\u691c\u8a0e<\/td>\n\u53e4\u6797\u3000\u9686\u6559\u6388<\/td>\n<\/tr>\n
4<\/td>\n1986<\/td>\n\u539f\u3000\u8070
\n\uff08\u65e9\u7a32\u7530\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		TSP\u306b\u304a\u3051\u308b\u8fd1\u4f3c\u89e3\u6cd5\u306e\u5b9f\u969b\u8a55\u4fa1\u306e\u307f\u306a\u304a\u3057<\/td>\n\u68ee\u6238\u3000\u664b\u6559\u6388<\/td>\n<\/tr>\n
4<\/td>\n1986<\/td>\n\u5c71\u5ddd\u6804\u6a39
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		An Efficient Trust Region Method for Minimizing Nondifferentiable Composite Functions<\/td>\n\u8328\u6728\u4fca\u79c0\u6559\u6388<\/td>\n<\/tr>\n
3<\/td>\n1985<\/td>\n\u679d\u5ee3\u6b63\u4eba
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u5e7e\u4f55\u5b66\u7684\u63a2\u7d22\u7b97\u6cd5\u306e\u7814\u7a76<\/td>\n\u4f0f\u898b\u6b63\u5247\u52a9\u6559\u6388<\/td>\n<\/tr>\n
3<\/td>\n1985<\/td>\n\u6b66\u7530\u3000\u664b
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u5730\u7406\u7684\u6700\u9069\u5316\u3068\u52d5\u7684\u65bd\u8a2d\u914d\u7f6e\u306e\u554f\u984c\u306e\u7814\u7a76<\/td>\n\u4f0a\u7406\u6b63\u592b\u6559\u6388<\/td>\n<\/tr>\n
3<\/td>\n1985<\/td>\n\u6f58\u3000\u7165\u65ed
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u30b5\uff0d\u30d3\u30b9\u30cd\u30c3\u30c8\u30ef\uff0d\u30af\u30b7\u30b9\u30c6\u30e0\u306b\u304a\u3051\u308b\u7aef\u672b\u6a5f\u5236\u5fa1<\/td>\n\u68ee\u6751\u82f1\u5178\u6559\u6388<\/td>\n<\/tr>\n
3<\/td>\n1985<\/td>\n\u5c71\u672c\u4eae\u4e09
\n\uff08\u57fc\u7389\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u5206\u6563\u578b\u30c7\uff0d\u30bf\u30d9\uff0d\u30b9\u30b7\u30b9\u30c6\u30e0\u306b\u304a\u3051\u308b\u6700\u9069\u30d5\u30a1\u30a4\u30eb\u914d\u7f6e\u554f\u984c\u306e\u7814\u7a76<\/td>\n\u5927\u5c71\u9054\u96c4\u52a9\u6559\u6388<\/td>\n<\/tr>\n
2<\/td>\n1984<\/td>\n\u5409\u5d0e\u3000\u53ce
\n\uff08\u57fc\u7389\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u9053\u8def\u6574\u5099\u512a\u5148\u9806\u4f4d\u6c7a\u5b9a\u624b\u6cd5\u306e\u691c\u8a0e<\/td>\n\u5200\u6839\u3000\u85ab\u6559\u6388<\/td>\n<\/tr>\n
2<\/td>\n1984<\/td>\n\u7530\u6751\u660e\u4e45
\n\uff08\u6771\u4eac\u5de5\u696d\u5927\u5b66\u5352\u8ad6\uff09<\/td>\n		\u6709\u9650\u70b9\u96c6\u5408\u306e\u51f8\u5305\u3092\u6c42\u3081\u308b\u52b9\u7387\u306e\u3088\u3044\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0<\/td>\n\u5c0f\u5cf6\u653f\u548c\u52a9\u6559\u6388<\/td>\n<\/tr>\n
2<\/td>\n1984<\/td>\n\u5927\u6ca2\u7fa9\u660e
\n\uff08\u7b51\u6ce2\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u8ddd\u96e2\u5206\u5e03\u306b\u3088\u308b\u90fd\u5e02\u65bd\u8a2d\u914d\u7f6e\u8a08\u753b\u306e\u7814\u7a76<\/td>\n\u8170\u585a\u6b66\u5fd7\u52a9\u6559\u6388<\/td>\n<\/tr>\n
1<\/td>\n1983<\/td>\n\u4eca\u4e95\u3000\u6d69
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u30cd\u30c3\u30c8\u30ef\uff0d\u30af\u7b97\u6cd5\u306b\u3088\u308b\u7d44\u5408\u305b\u6700\u9069\u5316\u554f\u984c\u306e\u52b9\u7387\u7684\u89e3\u6cd5<\/td>\n\u4f0a\u7406\u6b63\u592b\u6559\u6388<\/td>\n<\/tr>\n
1<\/td>\n1983<\/td>\n\u5c0f\u5ddd\u3000\u899a
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u914d\u6c34\u30cd\u30c3\u30c8\u30ef\uff0d\u30af\u306e\u5727\u529b\u5236\u5fa1\u8a08\u753b\u306b\u95a2\u3059\u308b\u57fa\u790e\u7684\u8003\u5bdf<\/td>\n\u897f\u5ddd\u7995\u4e00\u6559\u6388<\/td>\n<\/tr>\n
1<\/td>\n1983<\/td>\n\u5927\u5c4b\u9686\u751f
\n\uff08\u6771\u4eac\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		Voronoi \u7dda\u56f3\u306e\u52b9\u7387\u7684\u69cb\u6210\u6cd5\u306b\u95a2\u3059\u308b\u7814\u7a76<\/td>\n\u4f0f\u898b\u6b63\u5247\u52a9\u6559\u6388<\/td>\n<\/tr>\n
1<\/td>\n1983<\/td>\n\u5e84\u5883\u3000\u8aa0
\n\uff08\u4eac\u90fd\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u30d6\u30ed\u30c3\u30ad\u30f3\u30b0\u3092\u4f34\u3046\u5f85\u3061\u884c\u5217\u7db2\u306e\u5b89\u5b9a\u6761\u4ef6\u306b\u95a2\u3059\u308b\u7814\u7a76<\/td>\n\u4e09\u6839\u3000\u4e45\u6559\u6388<\/td>\n<\/tr>\n
1<\/td>\n1983<\/td>\n\u677e\u7530\u4e0d\u4e8c\u592b
\n\uff08\u57fc\u7389\u5927\u5b66\u4fee\u8ad6\uff09<\/td>\n		\u6d77\u4e0a\u4fdd\u5b89\u5e81\u306e\u8b66\u5099\u30fb\u6551\u96e3\u30b7\u30b9\u30c6\u30e0\u306b\u95a2\u3059\u308b\u8a55\u4fa1\u30e2\u30c7\u30eb\u306e\u4f5c\u6210<\/td>\n\u4f0a\u7406\u6b63\u592b\u6559\u6388<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
\n\n\n\n\n\n
\u5e74\u5ea6<\/th>\n\u53d7\u8cde\u8005<\/th>\n\u6240\u5c5e<\/th>\n\u6388\u8cde\u7406\u7531<\/th>\n<\/tr>\n<\/thead>\n
2015<\/a><\/td>\n\u5c71\u5143\u9806\u96c4<\/td>\n\u516c\u76ca\u8ca1\u56e3\u6cd5\u4eba\u65e5\u672c\u30b0\u30ed\u30fc\u30d0\u30eb\uff65\u30a4\u30f3\u30d5\u30e9\u30b9\u30c8\u30e9\u30af\u30c1\u30e3\u30fc\u7814\u7a76\u8ca1\u56e3\u7406\u4e8b\u9577<\/td>\n
\n\u5c71\u5143\u9806\u96c4\u6c0f\u306f\u65e5\u672c\u30b0\u30ed\u30fc\u30d0\u30eb\u30a4\u30f3\u30d5\u30e9\u30b9\u30c8\u30e9\u30af\u30c1\u30e3\u30fc\u7814\u7a76\u8ca1\u56e3\u306b\u5f53\u521d\u304b\u3089\u7406\u4e8b\u3068\u3057\u3066\u53c2\u753b\u3055\u308c\uff0c\u305d\u306e\u5f8c\u540c\u8ca1\u56e3\u306e\u7406\u4e8b\u9577\u3068\u3057\u3066\uff0c\u30a4\u30f3\u30d5\u30e9\u554f\u984c\u3092\u3081\u3050\u308b\u7cbe\u529b\u7684\u306a\u6d3b\u52d5\u3092\u56fd\u969b\u7684\u306b\u5c55\u958b\u3055\u308c\uff0c\u4eca\u65e5\u306b\u81f3\u3063\u3066\u3044\u308b\uff0e\u540c\u8ca1\u56e3\u306f\uff0c\u5f53\u6642\u4e09\u83f1\u7dcf\u5408\u7814\u7a76\u6240\u306e\u793e\u9577\u306e\u4e2d\u5cf6\u6b63\u6a39\u6c0f\u304c\uff0c\u9014\u4e0a\u56fd\u767a\u5c55\u306e\u305f\u3081\u306e\u5927\u898f\u6a21\u30a4\u30f3\u30d5\u30e9\u30b9\u30c8\u30e9\u30af\u30c1\u30e3\u30fc\u3078\u306e\u6295\u8cc7\u306e\u91cd\u8981\u6027\u3092\u63d0\u5531\u3057\u305f\u3053\u3068\u3092\u767a\u7aef\u3068\u3057\u30661990\u5e74\u306b\u8a2d\u7acb\u3055\u308c\u305f\u3082\u306e\u3067\u3042\u308b\uff0e
\n\u5c71\u5143\u6c0f\u306f\u68ee\u53e3\u7e41\u4e00\u5148\u751f\u304c1982\u5e74\u306b\u8d77\u3061\u4e0a\u3052\u305f\u7814\u7a76\u90e8\u4f1a\u300e\u7b2c\u4e09\u4e16\u754c\u3068\u30de\u30a4\u30b3\u30f3\u300f\u306b\uff0c\u68ee\u53e3\u5148\u751f\u304b\u3089\u7279\u306b\u62db\u304b\u308c\uff0c\u53c2\u753b\u3055\u308c\u305f\uff0e\u305d\u306e\u6642\u304b\u3089\u4eca\u65e5\u306b\u81f3\u308b\u307e\u3067\uff0c\u4e00\u8cab\u3057\u3066OR\u306b\u5bfe\u3059\u308b\u6df1\u3044\u7406\u89e3\u3068\u671f\u5f85\u3092\u793a\u3055\u308c\u3066\u3044\u308b\uff0e\u7279\u306b\u68ee\u53e3\u7814\u7a76\u90e8\u4f1a\u306e\u5f8c\u7d99\u7814\u7a76\u90e8\u4f1a\u3067\u3042\u308b\u300e\u5de8\u5927\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u306b\u95a2\u3059\u308bOR\u300f\uff08\u4e3b\u67fb\uff1a\u67f3\u4e95\u6d69\u5148\u751f\uff09\u306b\u59cb\u307e\u308a\uff0c\u4eca\u65e5\u306b\u81f3\u308b\u307e\u3067\u306e\u30a4\u30f3\u30d5\u30e9\u30b9\u30c8\u30e9\u30af\u30c1\u30e3\u30fc\u306b\u95a2\u3059\u308b\u7814\u7a76\u90e8\u4f1a\u306b\u7814\u7a76\u59d4\u8a17\u3092\u3055\u308c\u7d9a\u3051\u3066\u304d\u305f\uff0e\u4f1a\u5408\u5834\u6240\u306e\u63d0\u4f9b\uff0c\u4eca\u65e5\u7684\u30fb\u56fd\u969b\u7684\u306a\u7814\u7a76\u8ab2\u984c\u306e\u63d0\u6848\uff0c\u30c7\u30fc\u30bf\u3084\u7a2e\u3005\u306e\u7814\u7a76\u8cc7\u6599\u306e\u63d0\u4f9b\uff0c\u56fd\u5185\u5916\u306e\u4eba\u8108\u306e\u7d39\u4ecb\uff0c\u3068\u3044\u3063\u305f\u5b9f\u306b\u591a\u9762\u7684\u306a\u652f\u63f4\u3092\u5e38\u306b\u771f\u646f\u306b\u7d9a\u3051\u3089\u308c\u3066\u304d\u305f\uff0e\u5c71\u5143\u6c0f\u306e\u7269\u5fc3\u4e21\u9762\u306e\u652f\u63f4\u306e\u4e0b\u3067\uff0c\u591a\u304f\u306e\u82e5\u624b\u7814\u7a76\u8005\u304c\u80b2\u6210\u3055\u308c\u3066\u304d\u305f\uff0e\u3053\u3053\u306b\u611f\u8b1d\u306e\u610f\u3092\u8868\u3057\u300c\u65e5\u672c\u30aa\u30da\u30ec\u30fc\u30b7\u30e7\u30f3\u30ba\u30fb\u30ea\u30b5\u30fc\u30c1\u5b66\u4f1a\u7279\u5225\u8cde\u300d\u3092\u6388\u4e0e\u3059\u308b\u3053\u3068\u306b\u6c7a\u5b9a\u3057\u305f\uff0e
\n<\/div>
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