| 基于遗传算法的列车开行方案优化 |
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| 引用本文： | 孙亮,林翎,王莉. 基于遗传算法的列车开行方案优化[J]. 北京理工大学学报, 2016, 36(S2): 26-30 |
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| 作者姓名： | 孙亮 林翎 王莉 |
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| 作者单位： | 中国标准化研究院, 北京 100044,中国标准化研究院, 北京 100044,北京交通大学 交通运输学院, 北京 100044;北京市城市交通信息智能感知与服务工程技术研究中心, 北京 100044 |
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| 基金项目： | “十三五”国家重点研发计划资助项目（2016YFB1200401）；国家铁路局科技基金资助项目（2015Z057）；中央基本科研业务费资助项目（542016Y-4479） |
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| 摘 要： | 根据列车停站方案的4种模式，建立了列车开行方案双层规划模型.上层以总的运营费用最小和未服务的旅客数量最少为目标进行停站方案和开行频次的优化；给定停站方案和开行频次后，下层以服务旅客数量最大和旅客总的旅行时间最小为目标，建立了客流分配的混合整数规划.基于GA的开行方案优化算法实现了停站方案生成与客流分配循环反馈优化.最后以台湾高铁为实例分析，验证了本文模型和算法的有效性.
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| 关 键 词： | 铁路运输 开行方案 遗传算法 反馈优化 |
| 收稿时间： | 2016-10-30 |
Train Line Planning Based on Genetic Algorithm |
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| SUN Liang,LIN Ling and WANG Li. Train Line Planning Based on Genetic Algorithm[J]. Journal of Beijing Institute of Technology(Natural Science Edition), 2016, 36(S2): 26-30 |
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| Authors: | SUN Liang LIN Ling WANG Li |
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| Affiliation: | China National Institute of Standardization, Beijing 100191, China,China National Institute of Standardization, Beijing 100191, China and School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;Beijing Engineering Research Center of Urban Traffic Information Intelligent Sensing and Service Technologies, Beijing 100044, China |
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| Abstract: | A two-layer optimization model for train line planning was proposed based on the 4 modes of train stop-schedule. The purpose of the top-layer was to optimize the stop-schedule set and the service frequencies with the minimum of the total operation cost and unserved passengers. The purpose of the bottom-layer was to establish the specific stop-schedule and a mixed integer program for passenger flow assignment with the maximum of the served passenger volume and minimum of the total travel time. A GA-based line planning optimization algorithm was implemented to realize the feedback optimization of stop-schedule generation and passenger flow assignment. The validity of the model and algorithm was verified by the case study on Taiwan high speed railway. |
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| Keywords: | railway transportation line planning genetic algorithm feedback optimization |
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