| Project/Area Number |
10680428
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
社会システム工学
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| Research Institution | Osaka University |
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Principal Investigator |
ISHII Hiroaki Graduate School of Engineering, Osaka University, Professor, 大学院・工学研究科, 教授 (90107136)
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| Co-Investigator(Kenkyū-buntansha) |
SHIODE Shogo Faculty of Economics, Kobegakuin University, Professor, 経済学部, 教授 (40154174)
SAITO Seiji Graduate School of Engineering, Osaka University, Lecturer, 大学院・工学研究科, 講師 (90225714)
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| Project Period (FY) |
1998 – 1999
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| Project Status |
Completed (Fiscal Year 1999)
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| Budget Amount *help |
¥2,900,000 (Direct Cost: ¥2,900,000)
Fiscal Year 1999: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 1998: ¥1,900,000 (Direct Cost: ¥1,900,000)
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| Keywords | Fuzzy random variable / Variational inequality problem / Mollifier / Fuzzy duedate / Fuzzy processing time / Fuzzy random facility location problem / Fuzzy random knapsack problem / Fuzzy random spanning problem / ファジィ関数 / ファジィ微分 / 配置問題 / スケジューリング / 情報の価値 / 解の概念 / 組合せ最適化 |
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| Research Abstract |
This research is to investigate value of information to combinatorial optimization. The aim of this research is to clarify the effects of random and fuzzy factors in the combinatorial optimization models. Especially the solutions are changed according to the existence of uncertainty and/or ambiguity. First we considered linear programming problems under both random and fuzzy factors. Based on the results on so called these fuzzy random linear programming problems, we considered scheduling problems with fuzzy data and fuzzy constraint, spanning tree problems with fuzzy random edge costs, knapsack problem with fuzzy and random coefficients and facility location problem with fuzzy distance and fuzzy goal. In order to find the difference between the solution of ordinary problem and that of fuzzy random version, we introduced fuzzy random variables to above problems of combinatorial optimization and proposed efficient algorithms for solving their deterministic equivalent problems. We obtained some results on solutions of these problems. Further we investigated also basic mathematical analysis of value of information by considering functional analysis, especially variational inequality and introduced a new concept of fuzzy derivatives to apply optimization under ambiguity.
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