| Project/Area Number |
09650442
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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 |
System engineering
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| Research Institution | Kyoto Institute of Technology |
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Principal Investigator |
SANNOMIYA Nobuo Kyoto Institute of Technology Professor Faculty of Engineering and Design, 工芸学部, 教授 (60026044)
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| Co-Investigator(Kenkyū-buntansha) |
IIMA Hitoshi Kyoto Institute of Technology Research Faculty of Engineering and Design Associa, 工芸学部, 助手 (70273547)
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| Project Period (FY) |
1997 – 1998
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| Project Status |
Completed (Fiscal Year 1998)
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| Budget Amount *help |
¥3,100,000 (Direct Cost: ¥3,100,000)
Fiscal Year 1998: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 1997: ¥2,200,000 (Direct Cost: ¥2,200,000)
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| Keywords | scheduling / combinatorial optimization / genetic algorithm / rescheduling / production system / flexibility / production ordering problem |
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| Research Abstract |
1, A method for solving large-scale scheduling problems based on search space decomposition.
When the problem includes many variables, the individual in GA is represented in terms of plural chromosomes. Consequently the search space is decomposed and the optimal solution is obtained efficiently by applying heuristics to the search in the decomposed space.
2, A method for constructing GA to treat the problem constraints flexibly
The individual description is defined and the genetic operators are designed so as to obtain a feasible solution for the problem with complicated constraints.
3, A method of rescheduling by using GA
When the problem includes constraint variations, the initial population is constructed so as to solve a rescheduling problem easily.
4, An optimal schedule for large-scale job shop processes with various constraints
The individual description and introduction of heuristics are proposed for solving large-scale job shop scheduling problems with additional constraints.
5, An optimal schedule for flow shop processes with parallel machines
For treating complicated flow shop scheduling problems, GA is designed by combining some dispatching rules with the conventional genetic operators.
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