Highlights Propose a collaborative optimization strategy for train timetabling problem. Propose a stochastic optimization method with random and dynamic passenger demand. Provide a method of formulating a robust train timetable with energy-efficient strategy. Design the variable neighborhood search algorithm for generating high-quality solutions.

    Abstract Considering the dynamics and randomness of passenger demand, this paper investigates a train timetabling problem in the stochastic environment for an urban rail transit system. With the scenario-based representation of passenger distribution, an integer nonlinear programming (INLP) model is first formulated to simultaneously optimize the total number of train services, headway settings and speed profile selection decision during the planning time horizon, in which the expected total service cost is treated as the objective function. Through an analysis of the features of the nonlinear constraints, a reformulation method is proposed to develop an equivalent integer linear programming (ILP) model that can be easily solved by commercial software. Moreover, a variable neighborhood search algorithm is developed to find the approximate optimal solutions for large-scale problems within the tolerable computing time. Finally, two sets of numerical experiments, with the operation environments of a simple urban rail transit line and Fuzhou Metro Line 1, are implemented to verify the solution quality and effectiveness of the proposed methods.


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    Title :

    Train timetabling with dynamic and random passenger demand: A stochastic optimization method


    Contributors:
    Gong, Congcong (author) / Shi, Jungang (author) / Wang, Yanhui (author) / Zhou, Housheng (author) / Yang, Lixing (author) / Chen, Dewang (author) / Pan, Hanchuan (author)


    Publication date :

    2020-12-31




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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