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An optimization to schedule train operations with phase-regular framework for intercity rail lines. (English) Zbl 1253.90153

Summary: The most important operating problem for intercity rail lines, which are characterized with the train operations at rapid speed and high frequency, is to design a service-oriented schedule with the minimum cost. This paper proposes a phase-regular scheduling method which divides a day equally into several time blocks and applies a regular train-departing interval and the same train length for each period under the period-dependent demand conditions. A nonlinear mixed zero-one programming model, which could accurately calculate the passenger waiting time and the in-train crowded cost, is developed in this study. A hybrid genetic algorithm associated with the layered crossover and mutation operation is carefully designed to solve the proposed model. Finally, the effectiveness of the proposed model and algorithm is illustrated through the application to Hefei-Wuhan intercity rail line in China.

MSC:

90B90 Case-oriented studies in operations research
90B35 Deterministic scheduling theory in operations research
90C11 Mixed integer programming
90C09 Boolean programming
90C30 Nonlinear programming
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[1] N. S. A. Ghoneim and S. C. Wirasinghe, “Optimum zone structure during peak periods for existing urban rail lines,” Transportation Research B, vol. 20, no. 1, pp. 7-18, 1986.
[2] J. W. Goossens, S. van Hoesel, and L. Kroon, “On solving multi-type railway line planning problems,” European Journal of Operational Research, vol. 168, no. 2, pp. 403-424, 2006. · Zbl 1101.90038 · doi:10.1016/j.ejor.2004.04.036
[3] C. Liebchen, “The first optimized railway timetable in practice,” Transportation Science, vol. 42, no. 4, pp. 420-435, 2008. · doi:10.1287/trsc.1080.0240
[4] M. T. Claessens, N. M. Van Dijk, and P. J. Zwaneveld, “Cost optimal allocation of rail passenger lines,” European Journal of Operational Research, vol. 110, no. 3, pp. 474-489, 1998. · Zbl 0948.90097 · doi:10.1016/S0377-2217(97)00271-3
[5] K. Ghoseiri, F. Szidarovszky, and M. J. Asgharpour, “A multi-objective train scheduling model and solution,” Transportation Research B, vol. 38, no. 10, pp. 927-952, 2004. · doi:10.1016/j.trb.2004.02.004
[6] M. B. Khan and X. Zhou, “Stochastic optimization model and solution algorithm for robust double-track train-timetabling problem,” IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 1, pp. 81-89, 2010. · doi:10.1109/TITS.2009.2030588
[7] X. Zhou and M. Zhong, “Single-track train timetabling with guaranteed optimality: branch-and-bound algorithms with enhanced lower bounds,” Transportation Research B, vol. 41, no. 3, pp. 320-341, 2007. · doi:10.1016/j.trb.2006.05.003
[8] K. Nachtigall and S. Voget, “Minimizing waiting times in integrated fixed interval timetables by upgrading railway tracks,” European Journal of Operational Research, vol. 103, no. 3, pp. 610-627, 1997. · Zbl 0921.90068 · doi:10.1016/S0377-2217(96)00284-6
[9] A. de Palma and R. Lindsey, “Optimal timetables for public transportation,” Transportation Research B, vol. 35, no. 8, pp. 789-813, 2001. · doi:10.1016/S0191-2615(00)00023-0
[10] S. Nguyen, S. Pallottino, and F. Malucelli, “A modeling framework for passenger assignment on a transport network with timetables,” Transportation Science, vol. 35, no. 3, pp. 238-249, 2001. · Zbl 1041.90501 · doi:10.1287/trsc.35.3.238.10152
[11] R. C. W. Wong, T. W. Y. Yuen, K. W. Fung, and J. M. Y. Leung, “Optimizing timetable synchronization for rail mass transit,” Transportation Science, vol. 42, no. 1, pp. 57-69, 2008. · doi:10.1287/trsc.1070.0200
[12] L. Meng and X. Zhou, “Robust single-track train dispatching model under a dynamic and stochastic environment: a scenario-based rolling horizon solution approach,” Transportation Research B, vol. 45, no. 7, pp. 1080-1102, 2011. · doi:10.1016/j.trb.2011.05.001
[13] M. Carey and I. Crawford, “Scheduling trains on a network of busy complex stations,” Transportation Research B, vol. 41, no. 2, pp. 159-178, 2007. · doi:10.1016/j.trb.2006.02.002
[14] G. Caimi, F. Chudak, M. Fuchsberger, M. Laumanns, and R. Zenklusen, “A new resource-constrained multicommodity flow model for conflict-free train routing and scheduling,” Transportation Science, vol. 45, no. 2, pp. 212-227, 2011. · doi:10.1287/trsc.1100.0349
[15] Y. H. Chang, C. H. Yeh, and C. C. Shen, “A multiobjective model for passenger train services planning: application to Taiwan’s high-speed rail line,” Transportation Research B, vol. 34, no. 2, pp. 91-106, 2000. · doi:10.1016/S0191-2615(99)00013-2
[16] M. Gen and R. W. Cheng, Genetic Algorithms and Engineering Optimization, John Wiley & Son, New York, NY, USA, 2000.
[17] H. M. Niu, “Determination of the skip-stop scheduling for a congested transit line by bilevel genetic algorithm,” International Journal of Computational Intelligence Systems, vol. 4, no. 6, pp. 1158-1167, 2011.
[18] J. Gao,, R. Chen, and Q. Pan, “A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem,” International Journal of Computational Intelligence Systems, vol. 4, no. 4, pp. 497-508, 2011.
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