{"id":5587,"date":"2023-03-16T15:51:54","date_gmt":"2023-03-16T06:51:54","guid":{"rendered":"https:\/\/orsj.org\/?page_id=5587"},"modified":"2023-03-16T15:51:54","modified_gmt":"2023-03-16T06:51:54","slug":"2023%e5%b9%b4%e6%98%a5%e5%ad%a3%e7%a0%94%e7%a9%b6%e7%99%ba%e8%a1%a8%e4%bc%9a%e3%82%a2%e3%83%96%e3%82%b9%e3%83%88%e3%83%a9%e3%82%af%e3%83%88","status":"publish","type":"page","link":"https:\/\/orsj.org\/nc2023s-abstract","title":{"rendered":"2023\u5e74\u6625\u5b63\u7814\u7a76\u767a\u8868\u4f1a\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8"},"content":{"rendered":"

\u516c\u958b\u306b\u540c\u610f\u3044\u305f\u3060\u3044\u305f\u65b9\u306e\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8\u3092\u63b2\u8f09\u3057\u3066\u304a\u308a\u307e\u3059\u3002<\/p>\n

\u203b\u7b2c1\u8457\u8005\u306e\u307f\u63b2\u8f09\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n

\uff11\u65e5\u76ee (2023\/3\/7)<\/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
1-A-1<\/td>\n\u30d8\u30c3\u30bb\u884c\u5217\u306e\u5bfe\u89d2\u6210\u5206\u3092\u7528\u3044\u305f\u8fd1\u63a5\u52fe\u914d\u6cd5<\/a>
\u67f3\u4e0b\u7fd4\u592a\u90ce (\u4e2d\u592e\u5927\u5b66) <\/td>\n<\/tr>\n
1-A-2<\/td>\n\u591a\u76ee\u7684\u6700\u9069\u5316\u554f\u984c\u306b\u5bfe\u3059\u308b\u52a0\u901f\u4ed8\u304d\u8fd1\u63a5\u52fe\u914d\u6cd5\u306e\u6539\u826f<\/a>
\u897f\u6751\u7d50\u5e0c (\u4eac\u90fd\u5927\u5b66) <\/td>\n<\/tr>\n
1-A-3<\/td>\n\u30d1\u30e9\u30e1\u30fc\u30bf\u8abf\u6574\u4e0d\u8981\u306a\u975e\u51f8\u52a0\u901f\u52fe\u914d\u6cd5<\/a>
\u4e38\u8302\u76f4\u8cb4 (\u6771\u4eac\u5927\u5b66) <\/td>\n<\/tr>\n
1-A-4<\/td>\nRiemannian Interior Point Methods for Constrained Optimization on Manifolds<\/a>
\u983c\u5fd7\u5805 (\u7b51\u6ce2\u5927\u5b66) <\/td>\n<\/tr>\n
1-A-5<\/td>\n\u5c04\u5f71 – \u518d\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u6cd5\u3092\u7528\u3044\u305f\u5bfe\u79f0\u9310\u8a08\u753b\u554f\u984c\u306b\u5bfe\u3059\u308b\u5f8c\u51e6\u7406\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0<\/a>
\u52a0\u7d0d\u4f38\u4e00 (\u7b51\u6ce2\u5927\u5b66) <\/td>\n<\/tr>\n
1-A-6<\/td>\n\u5bfe\u79f0\u9310\u4e0a\u306e\u5171\u6b63\u5024\u9310\u306b\u5bfe\u3059\u308b\u8fd1\u4f3c\u968e\u5c64\u3068\u305d\u306e\u6bd4\u8f03<\/a>
\u897f\u5cf6\u5149\u6d0b (\u6771\u4eac\u5de5\u696d\u5927\u5b66) <\/td>\n<\/tr>\n
1-A-7<\/td>\n\u5927\u898f\u6a21\u69cb\u9020\u7269\u306e\u5efa\u8a2d\u6642\u306e\u88dc\u5f37\u5ea6\u5408\u6c7a\u5b9a\u554f\u984c\u306b\u5bfe\u3059\u308b\u81ea\u7136\u72b6\u6cc1\u3092\u8003\u616e\u3057\u305f\u591a\u30af\u30e9\u30b9\u30b5\u30dd\u30fc\u30c8\u30d9\u30af\u30c8\u30eb\u30de\u30b7\u30f3<\/a>
\u5dfd\u5553\u53f8 (\u5927\u962a\u5927\u5b66) <\/td>\n<\/tr>\n
1-A-8<\/td>\n\u30d5\u30e9\u30f3\u30af\u30fb\u30a6\u30eb\u30d5\u6cd5\u306b\u57fa\u3065\u304f 1-\u30ce\u30eb\u30e0\u6b63\u5247\u5316\u30bd\u30d5\u30c8\u30de\u30fc\u30b8\u30f3\u6700\u9069\u5316
\u4e09\u661f\u8ad2\u592a\u6717 (\u4e5d\u5dde\u5927\u5b66, \u7406\u7814AIP) <\/td>\n<\/tr>\n
1-A-9<\/td>\n\u76ee\u7684\u95a2\u6570\u304c\u672a\u77e5\u306e\u51f8\u6700\u9069\u5316\u554f\u984c\u306b\u5bfe\u3059\u308b\u5c40\u6240\u7dda\u5f62\u56de\u5e30\u3092\u7528\u3044\u305f\u975e\u53b3\u5bc6 Frank–Wolfe \u6cd5<\/a>
\u5c71\u6839\u5927\u8f1d (\u6771\u4eac\u5de5\u696d\u5927\u5b66) <\/td>\n<\/tr>\n
1-A-10<\/td>\n\u30ed\u30d0\u30b9\u30c8\u591a\u76ee\u7684\u6700\u9069\u5316\u554f\u984c\u306b\u304a\u3051\u308b\u30b9\u30ab\u30e9\u30fc\u5316\u624b\u6cd5\u306b\u3064\u3044\u3066<\/a>
\u8352\u8c37\u6d0b\u8f14 (\u79cb\u7530\u770c\u7acb\u5927\u5b66) <\/td>\n<\/tr>\n
1-A-11<\/td>\n\u96c6\u5408\u5024\u304a\u3088\u3073\u30d5\u30a1\u30b8\u30a3\u96c6\u5408\u5024\u6700\u77ed\u7d4c\u8def\u554f\u984c<\/a>
\u91d1\u6b63\u9053 (\u5f18\u524d\u5927\u5b66) <\/td>\n<\/tr>\n
1-B-1<\/td>\n\u914d\u7f6e\u304c 2 \u5217\u306b\u306a\u308b\u9577\u65b9\u5f62\u30b9\u30c8\u30ea\u30c3\u30d7\u30d1\u30c3\u30ad\u30f3\u30b0\u554f\u984c\u306b\u5bfe\u3059\u308b 1.5 \u8fd1\u4f3c\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0<\/a>
\u6a4b\u672c\u82f1\u6a39 (\u6771\u4eac\u6d77\u6d0b\u5927\u5b66) <\/td>\n<\/tr>\n
1-B-2<\/td>\n\u968e\u5c64\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306b\u5bfe\u3059\u308b\u8a31\u5bb9\u7684\u76ee\u7684\u95a2\u6570\u306e\u7279\u5fb4\u4ed8\u3051\u3068\u95a2\u9023\u3059\u308b\u6700\u9069\u5316\u554f\u984c\u306b\u5bfe\u3059\u308b\u8fd1\u4f3c\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0<\/a>
\u5b89\u85e4\u548c\u654f (\u9759\u5ca1\u5927\u5b66) <\/td>\n<\/tr>\n
1-B-3<\/td>\n\u5e73\u5747\u907f\u96e3\u6642\u9593\u6700\u5c0f\u5316\u30d5\u30ed\u30fc\u3092\u7528\u3044\u305f\u7dca\u6025\u907f\u96e3\u8a08\u753b\u306b\u95a2\u3059\u308b\u7814\u7a76<\/a>
\u5897\u7530\u5eb7\u4f51 (\u5175\u5eab\u770c\u7acb\u5927\u5b66) <\/td>\n<\/tr>\n
1-B-4<\/td>\n\u5c40\u6240\u6700\u9069\u89e3\u306e\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306b\u3088\u308b\u591a\u69d8\u306a\u89e3\u306e\u751f\u6210\u6cd5\u3068\u89e3\u91c8\u6027<\/a>
\u6a4b\u672c\u9032 (\u56fd\u7acb\u60c5\u5831\u5b66\u7814\u7a76\u6240) <\/td>\n<\/tr>\n
1-B-5<\/td>\n\u6975\u5024\u7d71\u8a08\u306b\u57fa\u3065\u304f\u8fd1\u508d\u69cb\u9020\u89e3\u6790<\/a>
\u52a0\u5730\u592a\u4e00 (\u5c0f\u6a3d\u5546\u79d1\u5927\u5b66) <\/td>\n<\/tr>\n
1-B-6<\/td>\n\u30b9\u30b1\u30fc\u30eb\u30d5\u30ea\u30fc\u306e\u546a\u3044 —\u591a\u30b9\u30bf\u30fc\u30c8\u6cd5\u306b\u3088\u308b\u5927\u898f\u6a21\u7d44\u5408\u305b\u6700\u9069\u5316\u306e\u56f0\u96e3\u3055—<\/a>
\u5897\u5c71\u535a\u4e4b (\u6771\u4eac\u90fd\u7acb\u5927\u5b66) <\/td>\n<\/tr>\n
1-B-7<\/td>\nShrunk subspace via operator Sinkhorn iteration<\/a>
Cole Franks (Massachusetts Institute of Technology) <\/td>\n<\/tr>\n
1-B-8<\/td>\n\u6c7a\u5b9a\u30c0\u30a4\u30a2\u30b0\u30e9\u30e0\u306b\u57fa\u3065\u304f\u7dda\u5f62\u5236\u7d04\u4ed8\u304d\u6700\u9069\u5316\u554f\u984c\u306e\u62e1\u5f35\u5b9a\u5f0f\u5316
\u6d5c\u5d0e\u6674\u8f1d (\u4e5d\u5dde\u5927\u5b66) <\/td>\n<\/tr>\n
1-B-9<\/td>\n\u6df7\u5408\u6574\u6570\u7dda\u5f62\u6700\u9069\u5316\u306b\u3088\u308b\u30ab\u30fc\u30cd\u30eb SVM \u306e\u5909\u6570\u9078\u629e<\/a>
\u7530\u6751\u9686\u592a (\u6771\u4eac\u8fb2\u5de5\u5927\u5b66, \u30aa\u30af\u30c8\u30fc\u30d0\u30fc\u30fb\u30b9\u30ab\u30a4) <\/td>\n<\/tr>\n
1-B-10<\/td>\n\u5217\u751f\u6210\u6cd5\u3092\u7528\u3044\u305f\u5206\u5272\u914d\u9001\u7d4c\u8def\u554f\u984c\u306e\u6c42\u89e3\u9ad8\u901f\u5316<\/a>
\u7389\u4e95\u79c0\u660e (\u6c96\u96fb\u6c17\u5de5\u696d) <\/td>\n<\/tr>\n
1-B-11<\/td>\n\u5bb9\u5668\u5185\u5727\u529b\u3092\u8003\u616e\u3059\u308b\u6c34\u7d20\u30b5\u30d7\u30e9\u30a4\u30c1\u30a7\u30fc\u30f3\u6700\u9069\u5316\u554f\u984c\u306e\u7d39\u4ecb<\/a>
\u9d5c\u5ddd\u77e5\u54c9 (\u6771\u829d\u7814\u7a76\u958b\u767a\u30bb\u30f3\u30bf\u30fc) <\/td>\n<\/tr>\n
1-B-12<\/td>\n\u6642\u9593\u67a0\u5236\u7d04\u4ed8\u304d\u30c1\u30fc\u30e0\u30aa\u30ea\u30a8\u30f3\u30c6\u30fc\u30ea\u30f3\u30b0\u554f\u984c\u306b\u5bfe\u3059\u308b\u30d1\u30b9\u518d\u7d50\u5408<\/a>
\u4e39\u6cbb\u6625\u4eba (\u4e2d\u592e\u5927\u5b66) <\/td>\n<\/tr>\n
1-C-1<\/td>\n\u6df7\u5408\u6574\u6570\u8a08\u753b\u6cd5\u306b\u3088\u308b\u9244\u9053\u4e57\u52d9\u5272\u4ea4\u756a\u306e\u81ea\u52d5\u4f5c\u6210\u624b\u6cd5<\/a>
\u52a0\u85e4\u601c (\u9244\u9053\u7dcf\u5408\u6280\u8853\u7814\u7a76\u6240) <\/td>\n<\/tr>\n
1-C-2<\/td>\n\u751f\u7523\u30b9\u30b1\u30b8\u30e5\u30fc\u30ea\u30f3\u30b0\u306b\u304a\u3051\u308b\u30c7\u30a3\u30b9\u30d1\u30c3\u30c1\u30f3\u30b0\u30eb\u30fc\u30eb\u306e\u52d5\u7684\u306a\u5207\u308a\u66ff\u3048\u624b\u6cd5<\/a>
\u6e21\u90e8\u6d0b\u4ecb (\u4e09\u83f1\u91cd\u5de5\u696d) <\/td>\n<\/tr>\n
1-C-3<\/td>\n\u539f\u59cb\u30e2\u30f3\u30c6\u30ab\u30eb\u30ed\u6cd5\u3092\u7528\u3044\u305f\u30af\u30e9\u30b9\u30bf\u30c4\u30fc\u30eb\u306e\u30a6\u30a7\u30cf\u642c\u9001\u30b9\u30b1\u30b8\u30e5\u30fc\u30eb\u306e\u6700\u9069\u5316
\u4e09\u6d66\u771f\u548c (\u6771\u4eac\u30a8\u30ec\u30af\u30c8\u30ed\u30f3) <\/td>\n<\/tr>\n
1-C-4<\/td>\n\u5de1\u56de\u30c8\u30fc\u30ca\u30e1\u30f3\u30c8\u554f\u984c\u306b\u304a\u3051\u308b\u79fb\u52d5\u56de\u6570\u6700\u5c0f\u5316<\/a>
\u5c0f\u91ce\u9686\u898f (\u4e2d\u592e\u5927\u5b66) <\/td>\n<\/tr>\n
1-C-5<\/td>\n\u7dcf\u5f53\u305f\u308a\u30ea\u30fc\u30b0\u6226\u306b\u304a\u3051\u308b carry-over effect \u5024\u6700\u5c0f\u5316\u306e\u691c\u8a0e<\/a>
\u4e09\u4e95\u99ff\u8f1d (\u7b51\u6ce2\u5927\u5b66) <\/td>\n<\/tr>\n
1-C-6<\/td>\nRPA \u624b\u9806\u306e\u6700\u9069\u5316\u554f\u984c—\u6574\u6570\u8a08\u753b\u6cd5\u3092\u7528\u3044\u3066<\/a>
\u5409\u7530\u6c34\u7d00 (\u5357\u5c71\u5927\u5b66) <\/td>\n<\/tr>\n
1-C-7<\/td>\n\u5165\u9662\u60a3\u8005\u306e\u4e88\u6e2c\u306b\u57fa\u3065\u304f\u75c5\u5e8a\u30b9\u30b1\u30b8\u30e5\u30fc\u30ea\u30f3\u30b0<\/a>
\u677e\u6797\ufa4f\u52a0 (\u6771\u4eac\u7406\u79d1\u5927\u5b66) <\/td>\n<\/tr>\n
1-C-8<\/td>\n\u5c45\u9152\u5c4b\u30c1\u30a7\u30fc\u30f3\u5e97\u306b\u304a\u3051\u308b\u5f93\u696d\u54e1\u306e\u52e4\u52d9\u8868\u4f5c\u6210\u554f\u984c<\/a>
\u4e2d\u6751\u514b (\u96fb\u6c17\u901a\u4fe1\u5927\u5b66) <\/td>\n<\/tr>\n
1-C-9<\/td>\n\u30ed\u30d0\u30b9\u30c8\u6700\u9069\u5316\u6cd5\u306b\u3088\u308b\u624b\u8853\u5ba4\u306e\u30b9\u30b1\u30b8\u30e5\u30fc\u30ea\u30f3\u30b0—\u624b\u8853\u6642\u9593\u306e\u4e0d\u78ba\u5b9f\u6027—<\/a>
\u96e3\u6ce2\u798e\u4eba (\u6771\u4eac\u7406\u79d1\u5927\u5b66) <\/td>\n<\/tr>\n
1-C-10<\/td>\n\u5206\u5e03\u7684\u30ed\u30d0\u30b9\u30c8\u6700\u9069\u5316\u306b\u57fa\u3065\u304f\u5b89\u5168\u306a\u7d4c\u8def\u9078\u629e\u554f\u984c\u306b\u3064\u3044\u3066<\/a>
\u571f\u5c4b\u4f51\u592a (\u6176\u61c9\u7fa9\u587e\u5927\u5b66) <\/td>\n<\/tr>\n
1-C-11<\/td>\n\u914d\u7f6e\u5019\u88dc\u70b9\u304a\u3088\u3073\u9700\u8981\u70b9\u304c\u591a\u89d2\u5f62\u306b\u9650\u5b9a\u3055\u308c\u305f\u30df\u30cb\u30b5\u30e0\u578b\u306e\u8907\u6570\u65bd\u8a2d\u914d\u7f6e\u554f\u984c\u3068\u305d\u306e\u89e3\u6cd5<\/a>
\u5e73\u5ca1\u4f51\u6a39 (\u6176\u61c9\u7fa9\u587e\u5927\u5b66) <\/td>\n<\/tr>\n
1-C-12<\/td>\n\u9298\u67c4\u6570\u5236\u7d04\u4ed8\u304d\u6c7a\u5b9a\u4fc2\u6570\u6700\u5927\u5316\u30dd\u30fc\u30c8\u30d5\u30a9\u30ea\u30aa\u69cb\u7bc9\u554f\u984c\u306e\u52b9\u7387\u7684\u89e3\u6cd5<\/a>
\u7530\u4e2d\u514b\u5f18 (\u6176\u61c9\u7fa9\u587e\u5927\u5b66) <\/td>\n<\/tr>\n
1-D-1<\/td>\nDEA-BSC \u7d71\u5408\u30e2\u30c7\u30eb\u3092\u7528\u3044\u305f\u300c\u8907\u6570\u4e8b\u696d\u4f53\u3092\u6301\u3064\u4f01\u696d\u306e\u7d4c\u55b6\u8ab2\u984c\u89e3\u6c7a\u300d\u3078\u306e\u5fdc\u7528<\/a>
\u5e73\u7530\u4e00\u90ce (\u5175\u5eab\u770c\u7acb\u5927\u5b66) <\/td>\n<\/tr>\n
1-D-2<\/td>\n\u65b0\u898f\u6280\u8853\u306e\u958b\u767a\u30b3\u30b9\u30c8\u8a55\u4fa1\u624b\u6cd5\u306e\u958b\u767a<\/a>
\u9152\u4e95\u7406\u4f50 (\u6771\u829d) <\/td>\n<\/tr>\n
1-D-3<\/td>\nMaximum Russell graph measures and extended production possibility sets<\/a>
\u95a2\u8c37\u548c\u4e4b (\u6210\u8e4a\u5927\u5b66) <\/td>\n<\/tr>\n
1-D-4<\/td>\n\u7701\u30a8\u30cd\u4f4f\u5b85\u306e\u6d88\u8cbb\u8005\u9078\u597d\u3068\u652f\u6255\u610f\u601d\u984d<\/a>
\u5f8c\u9577\u62d3\u771f (\u6771\u4eac\u7406\u79d1\u5927\u5b66) <\/td>\n<\/tr>\n
1-D-5<\/td>\n\u5065\u5eb7\u5bff\u547d\u5ef6\u4f38\u306e\u4fa1\u5024—\u7d71\u8a08\u7684\u751f\u547d\u4fa1\u5024\u306b\u3088\u308b\u8a55\u4fa1—<\/a>
\u8c37\u6fa4\u53cb\u4eae (\u6771\u4eac\u7406\u79d1\u5927\u5b66) <\/td>\n<\/tr>\n
1-D-6<\/td>\n\u98a8\u5727\u6d41\u5206\u96e2\u30c7\u30a4\u30bf\u30e0\u306b\u304a\u3051\u308b\u6700\u9069\u635c\u7d22\u7d4c\u8def\u9593\u9694\u306e\u6539\u826f<\/a>
\u9f4b\u85e4\u9756\u6d0b (\u6d77\u4e0a\u4fdd\u5b89\u5927\u5b66\u6821) <\/td>\n<\/tr>\n
1-D-7<\/td>\n\u52a0\u91cd\u7dcf\u548c\u6bd4\u6cd5\u3068\u3057\u3066\u306e AHP \u3068\u8a55\u4fa1\u57fa\u6e96\u306e\u5c3a\u5ea6\u8abf\u6574\u6bd4<\/a>
\u98ef\u7530\u6d0b\u5e02 (\u516c\u7acb\u8acf\u8a2a\u6771\u4eac\u7406\u79d1\u5927\u5b66) <\/td>\n<\/tr>\n
1-D-8<\/td>\nAHP \u306e\u91cd\u8981\u5ea6\u306e\u30d1\u30ec\u30fc\u30c8\u6700\u9069\u6027\u3068\u51f8\u6027\u306b\u3064\u3044\u3066<\/a>
\u7530\u5730\u5b8f\u4e00 (\u540d\u53e4\u5c4b\u5927\u5b66) <\/td>\n<\/tr>\n
1-D-9<\/td>\n\u5e38\u8a2d\u7814\u7a76\u90e8\u4f1a\u300c\u610f\u601d\u6c7a\u5b9a\u6cd5\u300d\u5e74\u5ea6\u5831\u544a\u304a\u3088\u3073 2023 \u5e74\u5ea6\u8a08\u753b\u306b\u3064\u3044\u3066<\/a>
\u98ef\u7530\u6d0b\u5e02 (\u516c\u7acb\u8acf\u8a2a\u6771\u4eac\u7406\u79d1\u5927\u5b66) <\/td>\n<\/tr>\n
1-D-10<\/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<\/a>
\u6069\u7530\u525b (\u6771\u4eac\u5de5\u696d\u5927\u5b66) <\/td>\n<\/tr>\n
1-D-11<\/td>\nCO$_{2}$ \u6392\u51fa\u91cf\u3092\u8003\u616e\u3057\u305f 47 \u90fd\u9053\u5e9c\u770c\u306e\u751f\u7523\u52b9\u7387\u6027\u5206\u6790<\/a>
\u77f3\u7530\u51cc (\u6771\u4eac\u5de5\u696d\u5927\u5b66) <\/td>\n<\/tr>\n
1-E-1<\/td>\nIDTW \u3092\u7528\u3044\u305f\u5909\u5316\u70b9\u691c\u77e5\u3068\u30e1\u30bf\u30e9\u30d9\u30ea\u30f3\u30b0\u3092\u7528\u3044\u305f GBDT \u306b\u3088\u308b\u682a\u5f0f\u5909\u52d5\u4e88\u6e2c<\/a>
\u7530\u5d0e\u8aa0 (\u6771\u4eac\u5de5\u696d\u5927\u5b66) <\/td>\n<\/tr>\n
1-E-2<\/td>\n\u7d75\u6587\u5b57\u3092\u7528\u3044\u305f\u611f\u6027\u60c5\u5831\u306e\u63a8\u5b9a<\/a>
\u6d25\u6751\u5927\u609f (\u4e2d\u592e\u5927\u5b66) <\/td>\n<\/tr>\n
1-E-3<\/td>\n\u88fd\u9020\u5de5\u7a0b\u306b\u95a2\u3059\u308b\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u306e\u5206\u985e\u53ca\u3073\u7279\u5fb4\u628a\u63e1\u306e\u305f\u3081\u306e\u6a5f\u68b0\u5b66\u7fd2\u624b\u6cd5\u306e\u69cb\u7bc9<\/a>
\u5e73\u7530\u667a\u54c9 (\u5e83\u5cf6\u5927\u5b66) <\/td>\n<\/tr>\n
1-E-4<\/td>\n\u8ca8\u5e63\u6570\u91cf\u8aac\u306b\u57fa\u3065\u304f\u30e6\u30fc\u30c6\u30a3\u30ea\u30c6\u30a3\u30fc\u30c8\u30fc\u30af\u30f3\u306e\u6d41\u901a\u65b9\u5f0f\u306b\u95a2\u3059\u308b\u8003\u5bdf<\/a>
\u7389\u4e95\u99ff\u54c9 (\u5948\u826f\u5148\u7aef\u79d1\u5b66\u6280\u8853\u5927\u5b66\u9662\u5927\u5b66) <\/td>\n<\/tr>\n
1-E-5<\/td>\n\u30c0\u30a4\u30ca\u30df\u30c3\u30af\u975e\u5bfe\u79f0 t \u30b3\u30d4\u30e5\u30e9\u3092\u7528\u3044\u305f\u672c\u90a6\u682a\u5f0f\u5e02\u5834\u306e\u30b9\u30bf\u30a4\u30eb\u30fb\u30d5\u30a1\u30af\u30bf\u30fc\u9593\u306e\u4f9d\u5b58\u69cb\u9020\u306b\u95a2\u3059\u308b\u5206\u6790<\/a>
\u5937\u85e4\u7fd4 (\u7b51\u6ce2\u5927\u5b66) <\/td>\n<\/tr>\n
1-E-6<\/td>\n\u91d1\u878d\u5e02\u5834\u306e\u65e5\u4e2d\u30ea\u30bf\u30fc\u30f3\u5909\u52d5\u8981\u56e0\u306b\u95a2\u3059\u308b\u30d9\u30a4\u30ba\u63a8\u8ad6<\/a>
\u843d\u5408\u590f\u6d77 (\u5175\u5eab\u770c\u7acb\u5927\u5b66) <\/td>\n<\/tr>\n
1-E-7<\/td>\n\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u958b\u767a\u5de5\u6570\u4e88\u6e2c\u306b\u304a\u3051\u308b\u6570\u91cf\u5316 III \u985e\u306e\u6d3b\u7528<\/a>
\u7530\u540d\u7db2\u572d\u592a (\u6771\u4eac\u90fd\u7acb\u5927\u5b66) <\/td>\n<\/tr>\n
1-E-8<\/td>\n\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u958b\u767a\u5de5\u6570\u4e88\u6e2c\u30c4\u30fc\u30eb\u306e\u958b\u767a<\/a>
\u9152\u4e95\u65e5\u5411 (\u6771\u4eac\u90fd\u7acb\u5927\u5b66) <\/td>\n<\/tr>\n
1-E-9<\/td>\n\u30ea\u30ab\u30ec\u30f3\u30c8\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u7528\u3044\u305f\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u30d5\u30a9\u30fc\u30eb\u30c8\u767a\u898b\u6570\u4e88\u6e2c<\/a>
\u5ea7\u9593\u6e09 (\u6771\u4eac\u90fd\u7acb\u5927\u5b66) <\/td>\n<\/tr>\n
1-E-10<\/td>\n\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30ef\u30fc\u30e0\u30e2\u30c7\u30eb\u306b\u3088\u308b\u65b0\u88fd\u54c1\u666e\u53ca\u30e2\u30c7\u30eb<\/a>
\u4f50\u85e4\u5927\u8f14 (\u65e5\u672c\u96fb\u4fe1\u96fb\u8a71) <\/td>\n<\/tr>\n
1-E-11<\/td>\nLee-Carter \u30e2\u30c7\u30eb\u306e\u4e00\u8cab\u30e2\u30c7\u30eb\u306e\u63d0\u6848\u3068\u305d\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u63a8\u5b9a\u53ca\u3073\u5c06\u6765\u4e88\u6e2c<\/a>
\u91d1\u6fa4\u601c\u592e (\u6771\u4eac\u7406\u79d1\u5927\u5b66) <\/td>\n<\/tr>\n
1-E-12<\/td>\n\u80ba\u764c\u6cbb\u7642\u306b\u304a\u3051\u308b\u65e9\u671f\u4ecb\u5165\u52b9\u679c\u306e\u8a55\u4fa1\u30e2\u30c7\u30eb<\/a>
\u4f50\u3005\u6728\u9686\u6674 (\u7b51\u6ce2\u5927\u5b66) <\/td>\n<\/tr>\n
1-F-1<\/td>\n\u7af6\u4e89\u5e02\u5834\u306b\u304a\u3051\u308b\u70ad\u7d20\u6392\u51fa\u91cf\u53d6\u5f15\u5236\u5ea6\u3068\u518d\u88fd\u9020\u306e\u6709\u52b9\u6027\u8a55\u4fa1
\u5742\u672c\u5b5d\u88d5 (\u6771\u4eac\u7406\u79d1\u5927\u5b66) <\/td>\n<\/tr>\n
1-F-2<\/td>\n\u79fb\u52d5\u8ddd\u96e2\u3068\u7acb\u3061\u5bc4\u308a\u65bd\u8a2d\u3092\u8003\u616e\u3057\u305f\u4e00\u822c\u4ecb\u8b77\u4e88\u9632\u65bd\u8a2d\u306e\u7acb\u5730\u306e\u691c\u8a0e<\/a>
\u4e2d\u5c71\u548c\u594f (\u6771\u4eac\u7406\u79d1\u5927\u5b66) <\/td>\n<\/tr>\n
1-F-3<\/td>\n\u6bd4\u4f8b\u578b\u793e\u4f1a\u6307\u6a19\u306e\u30b9\u30b1\u30fc\u30eb\u4e0d\u63c3\u3044\u554f\u984c (\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8)<\/a>
\u8d99\u4eae (\u4eac\u90fd\u5927\u5b66) <\/td>\n<\/tr>\n
1-F-4<\/td>\n\u9053\u8def\u7db2\u4e0a\u306e\u56fa\u5b9a\u65bd\u8a2d\u306b\u5bfe\u3059\u308b\u9700\u8981\u5272\u5f53\u554f\u984c<\/a>
\u7530\u4e2d\u5065\u4e00 (\u6176\u61c9\u7fa9\u587e\u5927\u5b66) <\/td>\n<\/tr>\n
1-F-5<\/td>\n\u30ca\u30e9\u67af\u308c\u9632\u9664\u7528\u5927\u91cf\u96c6\u7a4d\u578b\u304a\u3068\u308a\u4e38\u592a\u306e\u6700\u9069\u914d\u7f6e<\/a>
\u4f0a\u9ad8\u9759 (\u6771\u4eac\u7406\u79d1\u5927\u5b66) <\/td>\n<\/tr>\n
1-F-6<\/td>\nExact Solution Methods for the $p$-Median Problem with Manhattan Distance and a River with Crossings<\/a>
Thomas Byrne (University of Strathclyde) <\/td>\n<\/tr>\n
1-F-7<\/td>\n\u5b9f\u7e3e\u30c7\u30fc\u30bf\u304c\u5c11\u306a\u3044\u5e83\u544a\u306b\u5bfe\u3059\u308b\u5165\u672d\u984d\u6700\u9069\u5316\u30ed\u30b8\u30c3\u30af<\/a>
\u5ddd\u4e0a\u5b5d\u4ecb (negocia) <\/td>\n<\/tr>\n
1-F-8<\/td>\n\u5b9a\u984d\u306e\u30a4\u30f3\u30bb\u30f3\u30c6\u30a3\u30d6\u4ed8\u4e0e\u306b\u304a\u3051\u308b\u4e88\u7b97\u5236\u7d04\u3092\u8003\u616e\u3057\u305f\u30a2\u30c3\u30d7\u30ea\u30d5\u30c8\u30e2\u30c7\u30ea\u30f3\u30b0
\u677e\u4e95\u8ad2\u751f (\u30ea\u30af\u30eb\u30fc\u30c8) <\/td>\n<\/tr>\n
1-F-9<\/td>\n\u7af6\u5408\u5e02\u5834\u306b\u304a\u3051\u308b\u52d5\u7684\u4fa1\u683c\u306e\u540c\u8abf\u3068\u305d\u306e\u7d99\u7d9a\u6761\u4ef6\u306b\u3064\u3044\u3066<\/a>
\u4f50\u85e4\u516c\u4fca (\u795e\u5948\u5ddd\u5927\u5b66) <\/td>\n<\/tr>\n
1-F-10<\/td>\n\u88fd\u54c1\u6d41\u901a\u30c7\u30fc\u30bf\u3092\u6d3b\u7528\u3057\u305f\u65e5\u3005\u306e\u9700\u8981\u5909\u52d5\u306b\u8ffd\u968f\u3059\u308b\u9700\u8981\u4e88\u6e2c\u624b\u6cd5<\/a>
\u9577\u5149\u53f8 (\u4e09\u83f1\u96fb\u6a5f) <\/td>\n<\/tr>\n
1-F-11<\/td>\n\u4e00\u6b21\u306e\u6700\u5927\u30fb\u6700\u5c0f\u306e\u671f\u5f85\u5024\u306b\u5bfe\u3059\u308b\u8aa4\u5dee\u3068\u30d6\u30ec\u30a4\u30af\u30dd\u30a4\u30f3\u30c8\u6570\u306e\u4fdd\u8a3c\u4ed8\u304d\u533a\u5206\u7dda\u5f62\u8fd1\u4f3c<\/a>
\u9ad8\u6fa4\u967d\u592a\u6717 (\u9752\u5c71\u5b66\u9662\u5927\u5b66) <\/td>\n<\/tr>\n
1-G-1<\/td>\n\u4e09\u83f1\u96fb\u6a5f\u306b\u304a\u3051\u308b OR \u9069\u7528\u4e8b\u4f8b
\u5e73\u5d8b\u4e00\u8cb4 (\u4e09\u83f1\u96fb\u6a5f) <\/td>\n<\/tr>\n
1-G-2<\/td>\n\u6620\u50cf\u304b\u3089\u4eba\u7269\u884c\u52d5\u3092\u7406\u89e3\u3059\u308b\u3046\u307e\u3044\u624b\u3068\u5fdc\u7528\u4e8b\u4f8b
\u5289\u5065\u5168 (NEC \u30d3\u30b8\u30e5\u30a2\u30eb\u30a4\u30f3\u30c6\u30ea\u30b8\u30a7\u30f3\u30b9\u7814\u7a76\u6240) <\/td>\n<\/tr>\n
1-G-3<\/td>\n\u6e96\u5929\u9802\u885b\u661f\u30b7\u30b9\u30c6\u30e0 (\u307f\u3061\u3073\u304d) \u306e\u6982\u8981\u53ca\u3073\u5229\u6d3b\u7528\u4e8b\u4f8b<\/a>
\u795e\u85e4\u82f1\u4fca (NEC \u30bd\u30ea\u30e5\u30fc\u30b7\u30e7\u30f3\u30a4\u30ce\u30d9\u30fc\u30bf) <\/td>\n<\/tr>\n
1-G-4<\/td>\n\u30a4\u30aa\u30f3\u306e\u81a8\u5927\u306a\u5229\u7528\u5c65\u6b74\u304b\u3089\u5f97\u3089\u308c\u305f\u9867\u5ba2\u7406\u89e3
\u8d99\u30b3\u30f3 (\u30a4\u30aa\u30f3) <\/td>\n<\/tr>\n
1-G-5<\/td>\nEC \u30b5\u30a4\u30c8\u306b\u3088\u308b\u58f2\u4e0a\u306e\u5909\u5316\u70b9\u691c\u77e5\u3092\u7528\u3044\u305f PR \u52b9\u679c\u6e2c\u5b9a<\/a>
\u9ad8\u6a4b\u7531\u6a39 (\u5b87\u90fd\u5bae\u5927\u5b66, \u30a2\u30a4\u30ef\u5e83\u544a\u682a\u5f0f\u4f1a\u793e) <\/td>\n<\/tr>\n
1-G-6<\/td>\n\u4e0a\u6c34\u9053\u306e\u6301\u7d9a\u53ef\u80fd\u306a\u7ba1\u8def\u66f4\u65b0\u671f\u306b\u95a2\u3059\u308b\u5206\u6790<\/a>
\u5ddd\u8fba\u601c (\u7b51\u6ce2\u5927\u5b66) <\/td>\n<\/tr>\n
1-G-7<\/td>\n\u9053\u8def\u5229\u7528\u8005\u3068\u9053\u8def\u7ba1\u7406\u8005\u3068\u306e\u9f5f\u9f6c—\u4eba\u6d41\u30c7\u30fc\u30bf\u3092\u7528\u3044\u305f\u5206\u6790—<\/a>
\u4e00\u4e95\u76f4\u4eba (\u7b51\u6ce2\u5927\u5b66) <\/td>\n<\/tr>\n
1-G-8<\/td>\n\u6027\u98a8\u4fd7\u60c5\u5831\u30b5\u30a4\u30c8\u306e\u53e3\u30b3\u30df\u6295\u7a3f\u304b\u3089\u518d\u69cb\u7bc9\u3059\u308b\u5168\u56fd\u898f\u6a21\u306e\u6027\u63a5\u89e6\u30cd\u30c3\u30c8\u30ef\u30fc\u30af<\/a>
\u4f0a\u6771\u5553 (\u9577\u5d0e\u5927\u5b66) <\/td>\n<\/tr>\n
1-G-9<\/td>\nOn a Pyramid Structure in Social Networks and its Application<\/a>
Lyu Wenruo (Kyoto University) <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n

\u3000<\/p>\n

\uff12\u65e5\u76ee (2023\/3\/8)<\/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
2-A-1<\/td>\n\u4e0a\u4e0b\u9650\u5236\u7d04\u4ed8\u304d\u6700\u9069\u5316\u554f\u984c\u306b\u5bfe\u3059\u308b\u6709\u52b9\u5236\u7d04\u30d6\u30ed\u30c3\u30af Barzilai–Borwein \u6cd5<\/a>
\u4e2d\u5c71\u821c\u6c11 (\u96fb\u6c17\u901a\u4fe1\u5927\u5b66) <\/td>\n<\/tr>\n
2-A-2<\/td>\nGlobal convergence and acceleration of fixed point iterations of union upper semicontinuous operators
Jan Harold Alcantara (Academia Sinica) \u203b\u767a\u8868\u53d6\u6d88<\/td>\n<\/tr>\n
2-A-3<\/td>\nAccelerating Inexact Successive Quadratic Approximation for Regularized Optimization Through Manifold Identification<\/a>
Lee Ching-pei (Academia Sinica) <\/td>\n<\/tr>\n
2-A-4<\/td>\n\u69cb\u9020\u7684\u5236\u7d04\u3092\u6301\u3064\u6700\u9069\u5316\u554f\u984c\u306b\u5bfe\u3059\u308b majorization-minimization \u624b\u6cd5\u306e\u69cb\u7bc9<\/a>
\u5c71\u5ddd\u667a\u4e5f (\u6771\u4eac\u5de5\u696d\u5927\u5b66) <\/td>\n<\/tr>\n
2-A-5<\/td>\n2 \u3064\u306e\u96f6\u70b9\u5b9a\u7406\u3068 $n$ \u6b21\u5143\u4e2d\u9593\u5024\u306e\u5b9a\u7406<\/a>
\u5ddd\u5d0e\u82f1\u6587 (\u4e5d\u5dde\u5927\u5b66) <\/td>\n<\/tr>\n
2-A-6<\/td>\n\u81ea\u52d5\u5fae\u5206\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u6027\u80fd\u6bd4\u8f03<\/a>
\u6a80\u5bdb\u6210 (\u95a2\u897f\u5927\u5b66) <\/td>\n<\/tr>\n
2-A-7<\/td>\nNew Formulation of Optimal Floor Layout Planning Optimization by LocalSolver<\/a>
\u30b7\u30f3\u30ab\u30ed\u30a6\u30df\u30ab\u30e9\u30a4 (\u30bb\u30c3\u30af) <\/td>\n<\/tr>\n
2-A-8<\/td>\nDX \u3092\u9032\u5316\u3055\u305b\u308b LocalSolver 11.5<\/a>
\u5bae\u5d0e\u77e5\u660e (MSI \u682a\u5f0f\u4f1a\u793e) <\/td>\n<\/tr>\n
2-B-1<\/td>\n\u30d9\u30a4\u30ba\u76f8\u95a2\u5747\u8861\u3068 no-regret dynamics<\/a>
\u85e4\u4e95\u6d77\u6597 (\u56fd\u7acb\u60c5\u5831\u5b66\u7814\u7a76\u6240) <\/td>\n<\/tr>\n
2-B-2<\/td>\n\u505c\u6b62\u898f\u5247\u3092\u6301\u3064\u5206\u6570\u578b\u5fae\u5206\u30b2\u30fc\u30e0\u306b\u3064\u3044\u3066
\u671d\u7530\u667a\u4e5f (\u5e83\u5cf6\u5e02\u7acb\u5927\u5b66) <\/td>\n<\/tr>\n
2-B-3<\/td>\n\u4ee3\u66ff\u95a2\u4fc2\u306b\u304a\u3044\u3066\u4e92\u3044\u306b\u5bfe\u79f0\u306a\u30b0\u30eb\u30fc\u30d7\u304b\u3089\u306a\u308b\u30af\u30fc\u30eb\u30ce\u30fc\u5be1\u5360\u5e02\u5834\u306b\u304a\u3051\u308b\u5168\u4f53\u63d0\u643a\u306e $\\delta$-\u5b89\u5b9a\u6027<\/a>
\u658e\u85e4\u529f\u5927\u6717 (\u6176\u61c9\u7fa9\u587e\u5927\u5b66) <\/td>\n<\/tr>\n
2-B-4<\/td>\n\u591a\u81d3\u5668\u30de\u30c3\u30c1\u30f3\u30b0: \u814e\u81d3\u3068\u809d\u81d3<\/a>
\u6e21\u9089\u71c3 (\u6771\u4eac\u5de5\u696d\u5927\u5b66) <\/td>\n<\/tr>\n
2-B-5<\/td>\n\u63d0\u643a\u306b\u91cd\u8907\u3092\u8a31\u3057\u305f\u63d0\u643a\u5f62\u30b2\u30fc\u30e0\u306b\u304a\u3051\u308b\u4ef2\u88c1\u30b3\u30a2\u306e\u975e\u7a7a\u6027<\/a>
\u5409\u6fa4\u99ff\u6689 (\u96fb\u6c17\u901a\u4fe1\u5927\u5b66) <\/td>\n<\/tr>\n
2-B-6<\/td>\n\u4e0d\u5b8c\u5099\u60c5\u5831\u5354\u529b\u30b2\u30fc\u30e0\u306e\u8fd1\u4f3c Shapley \u5024<\/a>
\u685d\u5c4b\u8061 (\u5927\u6771\u6587\u5316\u5927\u5b66) <\/td>\n<\/tr>\n
2-B-7<\/td>\n2 \u90e8\u30b0\u30e9\u30d5\u4e0a\u306e\u30dd\u30d4\u30e5\u30e9\u30fc\u30de\u30c3\u30c1\u30f3\u30b0\u306e\u6700\u5927\u6570<\/a>
\u590f\u4e95\u6167 (\u7b51\u6ce2\u5927\u5b66) <\/td>\n<\/tr>\n
2-B-8<\/td>\nPolyhedral Clinching Auctions for Indivisible Goods<\/a>
\u4f50\u85e4\u826f\u4eae (\u6771\u4eac\u5927\u5b66) <\/td>\n<\/tr>\n
2-B-9<\/td>\n\u4e0d\u53ef\u5206\u306a\u30bf\u30b9\u30af\u306e\u516c\u5e73\u914d\u5206\u554f\u984c\u306b\u304a\u3051\u308b EFX \u914d\u5206<\/a>
\u5c0f\u6797\u4f51\u8f14 (\u4eac\u90fd\u5927\u5b66) <\/td>\n<\/tr>\n
2-C-1<\/td>\nM\/M\/1 \u5f85\u3061\u884c\u5217\u3092\u57fa\u306b\u3057\u305f\u5f85\u3061\u884c\u5217\u30b2\u30fc\u30e0\u306b\u304a\u3051\u308b\u95be\u5024\u6226\u7565\u3092\u518d\u73fe\u3059\u308b\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u30fb\u30d9\u30fc\u30b9\u30fb\u30e2\u30c7\u30eb
\u30e4\u30f3\u30ea\u30f3\u30a2\u30a6\u30f3 (\u9632\u885b\u5927\u5b66\u6821) <\/td>\n<\/tr>\n
2-C-2<\/td>\n\u30dd\u30a2\u30bd\u30f3\u904e\u7a0b\u306e\u307f\u3067\u99c6\u52d5\u3055\u308c\u308b\u78ba\u7387\u7684\u5236\u5fa1\u554f\u984c\u3068\u5909\u5206\u4e0d\u7b49\u5f0f\u306e\u7c98\u6027\u89e3<\/a>
\u8c4a\u6cc9\u6d0b (\u65e9\u7a32\u7530\u5927\u5b66) <\/td>\n<\/tr>\n
2-C-3<\/td>\n2 \u6b21\u5143\u6e96\u51fa\u751f\u6b7b\u6ec5\u904e\u7a0b\u306b\u304a\u3051\u308b\u5b9a\u5e38\u5206\u5e03\u306e\u6f38\u8fd1\u89e3\u6790\u306b\u95a2\u3059\u308b\u307e\u3068\u3081<\/a>
\u5c0f\u6ca2\u5229\u4e45 (\u99d2\u6fa4\u5927\u5b66) <\/td>\n<\/tr>\n
2-C-4<\/td>\n\u30bc\u30ed\u5207\u65ad\u30fb\u30bc\u30ed\u904e\u5270\u8907\u5408\u5206\u5e03\u306b\u57fa\u3065\u3044\u305f\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u4fe1\u983c\u6027\u8a55\u4fa1<\/a>
\u5449\u656c\u99b3 (\u5e83\u5cf6\u5927\u5b66) <\/td>\n<\/tr>\n
2-C-5<\/td>\nA Note on Optimal Testcase Generation for Baundary Value Analysis<\/a>
Xiujing Guo (\u5e83\u5cf6\u5927\u5b66) <\/td>\n<\/tr>\n
2-C-6<\/td>\nReliability Evaluation of Multi-state Linear Consecutive-$k$ Systems<\/a>
\u5468\u857e (\u5c71\u53e3\u5927\u5b66) <\/td>\n<\/tr>\n
2-C-7<\/td>\nOptimal Opportunity-based Age Replacement Policies with NPV Approach<\/a>
Jing Wu (Hiroshima University) <\/td>\n<\/tr>\n
2-C-8<\/td>\n\u30de\u30eb\u30b3\u30d5\u9023\u9396\u306b\u3088\u308b\u30a2\u30af\u30c6\u30a3\u30d6\u30e9\u30fc\u30cb\u30f3\u30b0\u306e\u6709\u52b9\u6027\u8a55\u4fa1\u30e2\u30c7\u30eb<\/a>
\u5317\u539f\u3042\u3086\u307f (\u65e9\u7a32\u7530\u5927\u5b66) <\/td>\n<\/tr>\n
2-C-9<\/td>\n\u62e1\u6563\u904e\u7a0b\u306e\u6700\u7d42\u901a\u904e\u6642\u523b\u3068\u30c7\u30d5\u30a9\u30eb\u30c8\u6642\u640d\u5931\u7387\u306e\u5206\u5e03
\u6c5f\u4e0a\u96c5\u5f66 (\u4eac\u90fd\u5927\u5b66) <\/td>\n<\/tr>\n
2-D-1<\/td>\n\u4e0d\u78ba\u5b9f\u6027\u4e0b\u306e CSR \u6295\u8cc7\u3068\u5e02\u5834\u30ea\u30b9\u30af<\/a>
\u5927\u8cc0\u96c4\u592a\u90ce (\u6771\u4eac\u7406\u79d1\u5927\u5b66) <\/td>\n<\/tr>\n
2-D-2<\/td>\n\u30d1\u30f3\u30c7\u30df\u30c3\u30af\u50b5\u306e\u4fa1\u683c\u8a55\u4fa1\u30e2\u30c7\u30eb<\/a>
\u56db\u30ce\u5bae\u88d5\u8cb4 (\u6176\u61c9\u7fa9\u587e\u5927\u5b66) <\/td>\n<\/tr>\n
2-D-3<\/td>\nComparison of Parametric NHPP-based Software Reliability Models<\/a>
\u674e\u601d\u6a4b (\u5e83\u5cf6\u5927\u5b66) <\/td>\n<\/tr>\n
2-D-4<\/td>\nThe effects of labor choice on investment and output dynamics<\/a>
Haejun Jeon (\u6771\u4eac\u7406\u79d1\u5927\u5b66) <\/td>\n<\/tr>\n
2-D-5<\/td>\nContinuous-time optimal execution in a Markovian environment<\/a>
\u6df1\u6fa4\u6b63\u5f70 (\u5927\u962a\u5927\u5b66) <\/td>\n<\/tr>\n
2-D-6<\/td>\nModel uncertainty for statistical arbitrage<\/a>
\u5409\u5ddd\u5927\u4ecb (\u95a2\u897f\u5927\u5b66) <\/td>\n<\/tr>\n
2-D-7<\/td>\nGeneralized Recovery Theorem \u3092\u7528\u3044\u305f\u6295\u8cc7\u5bb6\u4e0d\u5b89\u6295\u8cc7\u624b\u6cd5<\/a>
\u9234\u6728\u5eb7\u5e73 (\u3086\u3046\u3061\u3087\u9280\u884c) <\/td>\n<\/tr>\n
2-D-8<\/td>\n\u30bb\u30eb\u30d5\u30b9\u30c8\u30ec\u30fc\u30b8\u6295\u8cc7\u306e\u6570\u7406
\u9060\u85e4\u548c\u7d00 (\u5e1d\u4eac\u5e73\u6210\u5927\u5b66) <\/td>\n<\/tr>\n
2-D-9<\/td>\nThe effect of both sides operational flexibility in capacity investment under model uncertainty<\/a>
Imai Junichi (Keio University) <\/td>\n<\/tr>\n
2-E-1<\/td>\n\u9078\u6319\u533a\u5272\u554f\u984c\u306b\u5bfe\u3059\u308b\u30d2\u30e5\u30fc\u30ea\u30b9\u30c6\u30a3\u30af\u30b9\u3092\u7528\u3044\u305f ZDD \u69cb\u7bc9\u306e\u52b9\u7387\u5316<\/a>
\u5343\u539f\u826f\u592a (\u4e2d\u592e\u5927\u5b66) <\/td>\n<\/tr>\n
2-E-2<\/td>\nMDD \u3092\u7528\u3044\u305f\u5909\u66f4\u306e\u5c11\u306a\u3044\u9078\u6319\u533a\u5272\u306e\u5217\u6319<\/a>
\u5e02\u91ce\u9054\u4e5f (\u4eac\u90fd\u5927\u5b66) <\/td>\n<\/tr>\n
2-E-3<\/td>\n\u9078\u6319\u533a\u306e\u898f\u6a21\u3092\u6700\u5c0f\u904a\u8aac\u8ddd\u96e2\u304b\u3089\u8a08\u6e2c\u3059\u308b\u8a66\u307f\u3068\u5408\u533a\u8a55\u4fa1\u3078\u306e\u5fdc\u7528<\/a>
\u6839\u672c\u4fca\u7537 (\u6587\u6559\u5927\u5b66) <\/td>\n<\/tr>\n
2-E-4<\/td>\n\u30ed\u30d0\u30b9\u30c8\u5de1\u56de\u30bb\u30fc\u30eb\u30b9\u30de\u30f3\u554f\u984c\u306b\u5bfe\u3059\u308b\u30b3\u30a2\u9078\u629e\u6cd5<\/a>
\u9577\u8c37\u5ddd\u548c\u6a39 (\u9759\u5ca1\u5927\u5b66) <\/td>\n<\/tr>\n
2-E-5<\/td>\n\u751f\u6d3b\u570f\u3068\u4e16\u4ee3\u3092\u610f\u8b58\u3057\u305f\u9078\u6319\u533a\u5272\u308a<\/a>
\u677e\u539f\u5343\u6ce2 (\u7b51\u6ce2\u5927\u5b66) <\/td>\n<\/tr>\n
2-E-6<\/td>\n\u30dd\u30a4\u30f3\u30c8\u30fb\u30c8\u30a5\u30fb\u30dd\u30a4\u30f3\u30c8\u578b\u822a\u7a7a\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u8a2d\u8a08\u306b\u5bfe\u3059\u308b\u5229\u7528\u8005\u914d\u5206\u306e\u5c0e\u5165<\/a>
\u65e5\u6bd4\u91ce\u5c0b\u4f2f (\u5357\u5c71\u5927\u5b66) <\/td>\n<\/tr>\n
2-E-7<\/td>\n\u683c\u5b50\u72b6\u9053\u8def\u7db2\u306b\u304a\u3051\u308b\u53f3\u6298\u30b3\u30b9\u30c8\u3092\u8003\u616e\u3057\u305f\u6700\u5c0f\u30b3\u30b9\u30c8\u7d4c\u8def<\/a>
\u4e09\u6d66\u82f1\u4fca (\u5357\u5c71\u5927\u5b66) <\/td>\n<\/tr>\n
2-E-8<\/td>\n\u5927\u962a\u30fb\u95a2\u897f\u4e07\u535a 2025 \u306e\u30b7\u30e3\u30c8\u30eb\u30d0\u30b9\u4ea4\u901a\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3<\/a>
\u6e05\u6c34\u4ec1 (NTT \u30b3\u30df\u30e5\u30cb\u30b1\u30fc\u30b7\u30e7\u30f3\u79d1\u5b66\u57fa\u790e\u7814\u7a76\u6240) <\/td>\n<\/tr>\n
2-E-9<\/td>\nJR \u84b2\u7530\u99c5\u6771\u897f\u81ea\u7531\u901a\u8def\u65b0\u8a2d\u306b\u3068\u3082\u306a\u3046\u30d0\u30b9\u5229\u4fbf\u6027\u5411\u4e0a\u306e\u52b9\u679c<\/a>
\u7530\u53e3\u6771 (\u30d9\u30af\u30c8\u30eb\u7dcf\u7814) <\/td>\n<\/tr>\n
2-F-1<\/td>\n\u30a2\u30a4\u30c6\u30e0\u63a8\u85a6\u306b\u304a\u3051\u308b\u516c\u5e73\u6027\u8003\u616e\u306e\u305f\u3081\u306e\u4e8c\u6bb5\u968e\u6700\u9069\u5316
\u6ff1\u7530\u8ce2\u543e (\u30ea\u30af\u30eb\u30fc\u30c8) <\/td>\n<\/tr>\n
2-F-2<\/td>\n\u751f\u5f92\u500b\u5225\u6700\u9069\u306a\u554f\u984c\u30bb\u30c3\u30c8\u306e\u7de8\u6210<\/a>
\u77f3\u4e95\u5eb7\u8cb4 (Classi) <\/td>\n<\/tr>\n
2-F-3<\/td>\nSample Average Approximation \u6cd5\u306b\u3088\u308b\u78ba\u7387\u5236\u7d04\u6761\u4ef6\u4ed8\u304d\u306e\u624b\u8853\u30b9\u30b1\u30b8\u30e5\u30fc\u30eb\u6700\u9069\u5316<\/a>
\u7af9\u6751\u822a (\u6771\u4eac\u5de5\u696d\u5927\u5b66) <\/td>\n<\/tr>\n
2-F-4<\/td>\n\u9ad8\u6b21\u5143\u907a\u4f1d\u5b50\u30c7\u30fc\u30bf\u89e3\u6790\u7406\u8ad6 (Theory 2) \u306e\u5b8c\u6210\u5831\u544a 1<\/a>
\u65b0\u6751\u79c0\u4e00 (\u6210\u8e4a\u5927\u5b66) <\/td>\n<\/tr>\n
2-F-5<\/td>\n\u9ad8\u6b21\u5143\u907a\u4f1d\u5b50\u30c7\u30fc\u30bf\u89e3\u6790\u7406\u8ad6 (Theory 2) \u306e\u5b8c\u6210\u5831\u544a 2<\/a>
\u65b0\u6751\u79c0\u4e00 (\u6210\u8e4a\u5927\u5b66) <\/td>\n<\/tr>\n
2-F-6<\/td>\n\u9ad8\u6b21\u5143\u907a\u4f1d\u5b50\u30c7\u30fc\u30bf\u89e3\u6790\u7406\u8ad6 (Theory 2) \u306e\u5b8c\u6210\u5831\u544a 3<\/a>
\u65b0\u6751\u79c0\u4e00 (\u6210\u8e4a\u5927\u5b66) <\/td>\n<\/tr>\n
2-F-7<\/td>\n\u6a5f\u68b0\u5b66\u7fd2\u3092\u5229\u7528\u3057\u305f ADHD \u306e\u50be\u5411\u3092\u6301\u3064\u5150\u7ae5\u751f\u5f92\u306e\u5b66\u7fd2\u52b9\u679c\u306e\u4e88\u6e2c<\/a>
\u5c0f\u7528\u60a0\u592a (\u795e\u5948\u5ddd\u5927\u5b66) <\/td>\n<\/tr>\n
2-F-8<\/td>\n\u8cb4\u91d1\u5c5e\u6bd4\u306e\u985e\u4f3c\u6bd4\u306b\u3088\u308b\u76f4\u89d2\u4e09\u89d2\u5f62\u3068\u7b49\u89d2\u87ba\u65cb\u3092\u6d3b\u7528\u3057\u305f\u30ea\u30de\u30bd\u30f3 (\u30d1\u30b9\u30ab\u30eb\u306e\u8778\u725b\u5f62) \u306e\u4f5c\u753b\u3084\u6b63\u5f26\u6ce2\u87ba\u65cb\u306e\u5e7e\u4f55\u5b66\u7684\u7279\u6027\u306e\u518d\u8003<\/a>
\u4e2d\u897f\u771f\u609f (\u5927\u962a\u5de5\u696d\u5927\u5b66) <\/td>\n<\/tr>\n
2-G-1<\/td>\n\u7d71\u8a08\u30fbAI \u3092\u6d3b\u7528\u3057\u696d\u52d9\u52b9\u7387\u5316\u3092\u9032\u3081\u305f\u4e8b\u4f8b\u7d39\u4ecb\u3068\u3001\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u6559\u80b2\u3067\u306e MATLAB \u6d3b\u7528\u4e8b\u4f8b<\/a>
\u5927\u958b\u5b5d\u6587 (MathWorks Japan) <\/td>\n<\/tr>\n
2-G-2<\/td>\n\u6c34\u51e6\u7406\u30d7\u30e9\u30f3\u30c8\u306b\u304a\u3051\u308b\u88c5\u7f6e\u914d\u7f6e\u8a2d\u8a08\u81ea\u52d5\u5316\u306b\u95a2\u3059\u308b\u53d6\u308a\u7d44\u307f<\/a>
\u8acb\u5ddd\u514b\u4e4b (Fracta Leap) <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n","protected":false},"excerpt":{"rendered":"

\u516c\u958b\u306b\u540c\u610f\u3044\u305f\u3060\u3044\u305f\u65b9\u306e\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8\u3092\u63b2\u8f09\u3057\u3066\u304a\u308a\u307e\u3059\u3002 \u203b\u7b2c1\u8457\u8005\u306e\u307f\u63b2\u8f09\u3057\u3066\u3044\u307e\u3059\u3002 \uff11\u65e5\u76ee (2023\/3\/7) 1-A-1 \u30d8\u30c3\u30bb\u884c\u5217\u306e\u5bfe\u89d2\u6210\u5206\u3092\u7528\u3044\u305f\u8fd1\u63a5\u52fe\u914d\u6cd5\u67f3\u4e0b\u7fd4\u592a\u90ce (\u4e2d\u592e\u5927\u5b66) 1-A-2 \u591a\u76ee\u7684\u6700\u9069 … <\/p>\n

Continue reading “2023\u5e74\u6625\u5b63\u7814\u7a76\u767a\u8868\u4f1a\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":5207,"menu_order":223,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_mc_calendar":[]},"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false,"education-pro-homepage-thumb":false},"uagb_author_info":{"display_name":"\u7ba1\u7406\u8005","author_link":"https:\/\/orsj.org\/?author=1"},"uagb_comment_info":0,"uagb_excerpt":"\u516c\u958b\u306b\u540c\u610f\u3044\u305f\u3060\u3044\u305f\u65b9\u306e\u30a2\u30d6\u30b9\u30c8\u30e9\u30af\u30c8\u3092\u63b2\u8f09\u3057\u3066\u304a\u308a\u307e\u3059\u3002 \u203b\u7b2c1\u8457\u8005\u306e\u307f\u63b2\u8f09\u3057\u3066\u3044\u307e\u3059\u3002 \uff11\u65e5\u76ee (2023…","_links":{"self":[{"href":"https:\/\/orsj.org\/index.php?rest_route=\/wp\/v2\/pages\/5587"}],"collection":[{"href":"https:\/\/orsj.org\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/orsj.org\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/orsj.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/orsj.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=5587"}],"version-history":[{"count":3,"href":"https:\/\/orsj.org\/index.php?rest_route=\/wp\/v2\/pages\/5587\/revisions"}],"predecessor-version":[{"id":5590,"href":"https:\/\/orsj.org\/index.php?rest_route=\/wp\/v2\/pages\/5587\/revisions\/5590"}],"up":[{"embeddable":true,"href":"https:\/\/orsj.org\/index.php?rest_route=\/wp\/v2\/pages\/5207"}],"wp:attachment":[{"href":"https:\/\/orsj.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5587"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}