期刊
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
卷 67, 期 -, 页码 208-234出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trb.2014.05.005
关键词
Train dispatching; Rail network; Cumulative flow variable; Lagrangian relaxation
类别
资金
- National Natural Science Foundation of China [71201009]
- State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University [RCS2013ZZ001, RCS2012ZT003]
- Beijing Higher Education Young Elite Teacher Project [YETP0581]
- Research Fund for the Doctoral Program of Higher Education of China [20120009120015]
Train dispatching is critical for the punctuality and reliability of rail operations, especially for a complex rail network. This paper develops an innovative integer programming model for the problem of train dispatching on an N-track network by means of simultaneously rerouting and rescheduling trains. Based on a time-space network modeling framework, we first adapt a commonly used big-M method to represent complex if-then conditions for train safety headways in a multi-track context. The track occupancy consideration on typical single and double tracks is then reformulated using a vector of cumulative flow variables. This new reformulation technique can provide an efficient decomposition mechanism through modeling track capacities as side constraints which are further dualized through a proposed Lagrangian relaxation solution framework. We further decompose the original complex rerouting and rescheduling problem into a sequence of single train optimization subproblems. For each subproblem, a standard label correcting algorithm is embedded for finding the time dependent least cost path on a time-space network. The resulting dual solutions can be transformed to feasible solutions through priority rules. We present a set of numerical experiments to demonstrate the system-wide performance benefits of simultaneous train rerouting and rescheduling, compared to commonly-used sequential train rerouting and rescheduling approaches. (C) 2014 Elsevier Ltd. All rights reserved.
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