Joint optimization of train scheduling and maintenance planning in a railway network: A heuristic algorithm using Lagrangian relaxation

•Propose a joint optimization model for train scheduling and maintenance planning in a railway network.•Develop a heuristic algorithm using Lagrangian relaxation to solve the ILP model.•Apply the proposed model and algorithm to a practical problem in the Chinese railway network. Train scheduling and...

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Bibliographic Details
Published in:Transportation research. Part B: methodological Vol. 134; pp. 64 - 92
Main Authors: Zhang, Chuntian, Gao, Yuan, Yang, Lixing, Gao, Ziyou, Qi, Jianguo
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.04.2020
Elsevier Science Ltd
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ISSN:0191-2615, 1879-2367
Online Access:Get full text
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Summary:•Propose a joint optimization model for train scheduling and maintenance planning in a railway network.•Develop a heuristic algorithm using Lagrangian relaxation to solve the ILP model.•Apply the proposed model and algorithm to a practical problem in the Chinese railway network. Train scheduling and maintenance planning compete for the resources in a railway network. A commonly used way is dealing with maintenance planning first and then train scheduling, or vice versa. In this paper, we propose a joint optimization model for the two problems in a railway network with double-track, where the upstream and downstream trains are independent and a maintenance task on a section cannot be split or disrupted. In order to solve the model, a heuristic algorithm using Lagrangian relaxation is developed. Due to the large number of constraints, we use a dynamic constraint-generation technique in the iterations of the sub-gradient optimization procedure. We apply the model and algorithm to a practical problem in the Chinese railway network, in which some additional trains are inserted into a fixed existing timetable and the maintenance plan on the involved high-speed railway sections is adjusted. The computational results illustrate the effectiveness and efficiency of the proposed model and algorithm.
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ISSN:0191-2615
1879-2367
DOI:10.1016/j.trb.2020.02.008