1. Decomposition and approximate dynamic programming approach to optimization of train timetable and skip-stop plan for metro networks.
- Author
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Yuan, Yin, Li, Shukai, Liu, Ronghui, Yang, Lixing, and Gao, Ziyou
- Subjects
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DYNAMIC programming , *TIME perspective , *NONLINEAR programming , *HEURISTIC algorithms , *QUALITY of service , *INTEGER programming , *SIMULATED annealing - Abstract
Carefully coordinating train timetables of different operating lines can help reduce transfer delays, which in turn reduces station crowding and improves overall service quality. This paper explores the optimization to train timetable and skip-stop plans that aims to minimize the total passenger waiting time and station crowding. The problem is formulated as a mixed-integer non-linear programming model. To effectively address the complexity of the model, a decomposition and approximate dynamic programming approach is designed to convert the original network-level problem into a series of small-scale subproblems, one for each operating line, to be solved quickly in a distributed manner. The effectiveness and practicability of the model and algorithm are demonstrated on two case networks: a small-scale synthetic network of three metro lines and a real-world network based on Beijing metro. The computational results illustrate that the proposed strategy to generate train timetables and skip-stop plans can effectively reduce passenger waiting time and station crowing. The proposed decomposition and approximate dynamic programming approach is also shown to perform more efficiently than traditional heuristic algorithms, such as genetic algorithm and simulated annealing algorithm for large-scale networks. • The optimization of train timetable and skip-stop plans of metro networks is considered. • A mixed-integer nonlinear programming model is formulated for this problem. • A decomposition and approximate dynamic programming approach is designed. • The optimized strategy reduces passenger waiting time and station crowing. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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