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\n\u65e5\u6642\uff1a2017\u5e746\u670817\u65e5\uff08\u571f\uff0914:30-17:00<\/p>\n
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\nhttp:\/\/www.aichi-u.ac.jp\/profile\/campus-nagoya.html<\/p>\n
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\nMulti-State Network Reliability Evaluation
\nMing J Zuo, University of Alberta, Canada<\/p>\n
ABSTRACT\uff1aA multi-state network under consideration consists of a source node, a sink node, and some independent failure prone components in between. The components can work at different levels of capacity. For such a network, we are interested in evaluating the probability that the flow from the source node to the sink node is equal to or greater than a demanded flow of d units. A general method for reliability evaluation of such multi-state networks is using minimal path (cut) vectors. A minimal path vector to system state d is called a d-MP. The reliability of such a network can be defined as the probability of the component state vector being not smaller than at least one of the d-MPs. Efficient evaluation of the reliability of such a network is essential for its reliability assurance. In this presentation, we report our recent progress in improving algorithms for efficient evaluation of network reliability. These include methods for generating minimal path sets for binary network, generating d-MPs for multi-state networks, and network reliability evaluation using the sum of disjoint products approach and the state space decomposition approach.<\/p>\n
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\nHeuristics for 2D and 3D packing problems
\nYannan HU, University of Nagoya, Aichi<\/p>\n
ABSTRACT\uff1aTwo-dimensional and three dimensional packing problems belong to a class of combinatorial optimization problems. These problems are related to real-world applications and they have been studied for a long time from both theoretical and practical points of view. In a packing problem, it involves packing a set of objects, called items, into containers. The objective is to pack all the items without overlap into containers as densely as possible. Packing problems are known as NP-hard and a series of heuristics have been developed. This speech introduces some typical heuristics for these problems. There are many variations for the objective functions such as to pack a single container as densely as possible or to pack all items into as few container as possible.
\nFor the two-dimensional packing problem, we consider the strip packing problem that has been intensively investigate. In a two-dimensional strip packing problem, we are given only a single rectangular container, called strip, whose width is fixed and height is unrestricted, and the objective is to minimize the height of the strip. We focus on packing items having two-dimensional shapes such as rectangles, rectilinear block or arbitrarily shaped polygons represented in bitmap format. A rectilinear block is a polygonal block whose interior angles are either 90 degrees or 270 degrees. These\u3000problems have applications in the wood, glass, and leather industries as well as LSI and VLSI design and newspaper paging.
\nFor the three-dimensional packing problem, we focus on the problem in which cuboid items and containers are given and the task is to pack all the items into as few containers as possible. This problem is also called container loading problem. This type of problem appears in various industrial applications and becomes one of the critical problems to reduce the cost of logistics and transportation.<\/p>\n
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\n\u611b\u77e5\u770c\u7acb\u5927\u5b66\u3000\u5965\u7530\u9686\u53f2\u3000okuda\u25cbist.aichi-pu.ac.jp<\/p>\n[contact-form-7 id=”2705″ title=”20170617″]\n
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