| Project/Area Number |
11450154
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
System engineering
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| Research Institution | Kyoto Institute of Technology |
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Principal Investigator |
SANNOMIYA Nobuo Kyoto Institute of Technology, Faculty of Engineering and Design, Professor, 工芸学部, 教授 (60026044)
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| Co-Investigator(Kenkyū-buntansha) |
IIMA Hitoshi Kyoto Institute of Technology, Faculty of Engineering and Design, Research Associate, 工芸学部, 助手 (70273547)
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| Project Period (FY) |
1999 – 2001
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| Project Status |
Completed (Fiscal Year 2001)
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| Budget Amount *help |
¥6,600,000 (Direct Cost: ¥6,600,000)
Fiscal Year 2001: ¥1,300,000 (Direct Cost: ¥1,300,000)
Fiscal Year 2000: ¥1,800,000 (Direct Cost: ¥1,800,000)
Fiscal Year 1999: ¥3,500,000 (Direct Cost: ¥3,500,000)
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| Keywords | scheduling / job shop process / flowshop process / genetic algorithm / large-scale system / production system / diversity / multi-objective optimization / 多目的 / 分解法 / 組合せ最適化 / 大規模性 / 柔軟性 |
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| Research Abstract |
1. Proposition of a new selection procedure capable of keeping a diverse population
It was shown that the proposed selection procedure (called Partial Enumeration Selection Method : PESM) can keep a high diversity population through the generations without deteriorating the accuracy of the solution. The robustness of the algorithm was also shown for variations of several schedule parameters.
2. Proposition of decomposition and search space reduction methods
It was shown from computational experiments for large-scale scheduling problems that the proposed decomposition and search space reduction methods work well with genetic algorithms.
3. Design of genetic algorithms capable of dealing flexibly with complex constraints
The design of genetic algorithms was proposed in such a way that the scheduling problems can be solved by only adding a module related to the added constraints. The scheduling problems of a job shop process with parallel machines and the worker allocation problem in a job shop process were solved by the proposed algorithm. Moreover a new decoding method was proposed for no-buffer job shop problems.
4. Application to multi-objective optimization problems
The PESM-based genetic algorithm was applied to solving multi-objective flowshop problems and smooth Pareto fonts were obtained.
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