Demand-oriented integration optimization of train timetabling and rolling stock circulation planning with flexible train compositions: A column-generation-based approach

Hanchuan Pan, Lixing Yang and Zhe Liang

European Journal of Operational Research, 2023, vol. 305, issue 1, 184-206

Abstract: Train timetabling and rolling stock circulation planning are two important optimization problems that arise in the daily operations of urban rail transit lines. This study proposes a novel approach to simultaneously optimize the train timetable and rolling stock circulation plan with flexible train compositions on an urban rail transit line in order to better match the passenger demand and supplied transport capacity, and further improve operation flexibility and extract gains in efficiency, in which the dynamic passenger demand is particularly considered in the formulation process. We first propose two integer linear programming models that are formulated on time-space networks by considering flexible coupling/splitting activities. The models consist of restricted master problems for optimizing the total generalized cost and subproblems that are used to generate different train composition routes. An effective column-generation-based diving heuristic algorithm is developed to solve the problem of interest, and it is able to simultaneously obtain the train timetable and rolling stock circulation plan. Several sets of experimental scenarios, involving relatively small-scale cases and a case derived from real-world Shanghai Metro data, are tested to verify the effectiveness of the proposed model and algorithm.

Keywords: Timetabling; Rolling stock circulation planning; Flexible train composition; Urban rail transit (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:305:y:2023:i:1:p:184-206

DOI: 10.1016/j.ejor.2022.05.039

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