| Project/Area Number |
06650580
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
交通工学・国土計画
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| Research Institution | Muroran Institute of Technology |
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Principal Investigator |
TAMURA Tohru Muroran Institute of Technology, Dep.of Civil Eng.and Architecture, Associate Professor, 工学部, 助教授 (80163690)
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| Co-Investigator(Kenkyū-buntansha) |
MASUYA Yuzo Hokkaido College, Senshu University Dep.of Civil Eng., Professor, 北海道短期大学・土木科, 教授 (70002045)
SAITO Kazuo Muroran Institute of Technology, Dep.of Civil Eng.and Architecture, Professor, 工学部, 教授 (00001222)
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| Project Period (FY) |
1994 – 1995
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| Project Status |
Completed (Fiscal Year 1995)
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| Budget Amount *help |
¥2,400,000 (Direct Cost: ¥2,400,000)
Fiscal Year 1995: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 1994: ¥1,400,000 (Direct Cost: ¥1,400,000)
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| Keywords | Genetic Algorithm / Multiple Modes Scheduling / Transpotation Planning / 交通ネットワーク / 空港計画 / スケジューリング分析 / 鉄道計画 |
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| Research Abstract |
Genetic Algorithm (GA) is known well by J.D.Bagley's study in 1967.GA includes generally three genetic operators, selection, crossover and mutation. The lack of dependence on function gradients makes it more suitable to such problems, like as discrete optimization design problems and optimization design problems with non-convexities or disjointness in design space. The method is tried to apply to Multiple Modes Scheduling in this paper. GA is examined through comparing with exact algorithm (enumeration method) and Monte Carlo Method in practical application. The following are the major points of this paper.
(1). Design variables which corresponding to the airline and railway routes are coded directlt to a string. (2). Fitness function is used to avoid the premature convergence to a local solution. (3). Although Multiple Modes Scheduling has been a difficult problem, GA could provide some solution to such problem very easily. (4). The method proposed in this paper was shown to be effective for improvement of GA's reliability. The results suggest that GA is more effective for the optimization of large size airline and railway networks.
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