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Real time management of a metro rail terminus

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  • Flamini, Marta
  • Pacciarelli, Dario

Abstract

This paper addresses a scheduling problem arising in the real time management of a metro rail terminus. It mainly consists in routing incoming trains through the station and scheduling their departures with the objective of optimizing punctuality and regularity of train service. The purpose of this work is to develop an automated train traffic control system, able to directly implement most traffic control actions, without the authorization of the local area manager. The scheduling problem is modeled as a bicriteria job shop scheduling problem with additional constraints. The two objective functions, in lexicographical order, are the minimization of tardiness/earliness and the headway optimization. The problem is solved in two steps. At first a heuristic builds a feasible solution by considering the first objective function. Then the regularity is optimized without deteriorating the first objective function. Computational results show that the system is able to manage the terminus very efficiently.

Suggested Citation

  • Flamini, Marta & Pacciarelli, Dario, 2008. "Real time management of a metro rail terminus," European Journal of Operational Research, Elsevier, vol. 189(3), pages 746-761, September.
    • Handle: RePEc:eee:ejores:v:189:y:2008:i:3:p:746-761
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    References listed on IDEAS

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    Cited by:

    1. Wang, Yihui & Tang, Tao & Ning, Bin & Meng, Lingyun, 2017. "Integrated optimization of regular train schedule and train circulation plan for urban rail transit lines," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 105(C), pages 83-104.
    2. Ahmadian, Mohammad Mahdi & Salehipour, Amir & Cheng, T.C.E., 2021. "A meta-heuristic to solve the just-in-time job-shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 288(1), pages 14-29.
    3. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    4. Corman, Francesco & D'Ariano, Andrea & Pacciarelli, Dario & Pranzo, Marco, 2010. "A tabu search algorithm for rerouting trains during rail operations," Transportation Research Part B: Methodological, Elsevier, vol. 44(1), pages 175-192, January.
    5. Wang, Yihui & D’Ariano, Andrea & Yin, Jiateng & Meng, Lingyun & Tang, Tao & Ning, Bin, 2018. "Passenger demand oriented train scheduling and rolling stock circulation planning for an urban rail transit line," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 193-227.
    6. Erfan Hassannayebi & Seyed Hessameddin Zegordi & Mohammad Reza Amin-Naseri & Masoud Yaghini, 2018. "Optimizing headways for urban rail transit services using adaptive particle swarm algorithms," Public Transport, Springer, vol. 10(1), pages 23-62, May.

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