| Project/Area Number |
26350437
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Social systems engineering/Safety system
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| Research Institution | Waseda University |
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Principal Investigator |
Morito Susumu 早稲田大学, 理工学術院, 名誉教授 (50134193)
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| Co-Investigator(Kenkyū-buntansha) |
今泉 淳 東洋大学, 経営学部, 教授 (00257221)
椎名 孝之 早稲田大学, 理工学術院, 教授 (90371666)
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| Research Collaborator |
KATO Satoshi
FUKUMURA Naoto
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| Project Period (FY) |
2014-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2016: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2015: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2014: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 鉄道の計画立案 / 最適化 / 数理計画 / 乗務員運用計画 / 車両運用計画 / ダイヤ作成 / 交番作成 / 車両割当計画 / 混合整数計画 / 列生成法 / 終端効果 / 余裕時分配分 / ネットワーク投資計画 / 車両割当問題 / 乗務員交番作成問題 / 最適余裕時分配分 / 組み合わせ最適化 / 確率計画法 / 鉄道の計画 / 乗務員行路作成 / 必要車両数算定 |
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| Outline of Final Research Achievements |
Models and solution algorithms for planning railway operations have been developed. Topics studied include pairing generation for crew scheduling considering weekend timetables, rolling stock rostering and assignment, evaluation of required number of roling stock, allocation of running time supplements in timetabling, optimal design of railway network expansion, train platforming at a terminal station and its adjacent station to maximize throughput.
Optimization models based on mathematical programming and their solution algorithms have been developed. Column generation approaches were developed for crew pairing generation considering weekend timetables and also for the rolling stock assignment problem. The L-shaped algorithm was developed for the railway network expansion problem. For most problems studied, computational evaluations of the models and/or the algorithms were performed based on the real data, and their effetiveness was confirmed.
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