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A hybrid Constraint Programming/Mixed Integer Programming framework for the preventive signaling maintenance crew scheduling problem

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  • M. Pour, Shahrzad
  • Drake, John H.
  • Ejlertsen, Lena Secher
  • Rasmussen, Kourosh Marjani
  • Burke, Edmund K.

Abstract

A railway signaling system is a complex and interdependent system which should ensure the safe operation of trains. We introduce and address a mixed integer optimisation model for the preventive signal maintenance crew scheduling problem in the Danish railway system. The problem contains many practical constraints, such as temporal dependencies between crew schedules, the splitting of tasks across multiple days, crew competency requirements and several other managerial constraints. We propose a novel hybrid framework using Constraint Programming to generate initial feasible solutions to feed as ‘warm start’ solutions to a Mixed Integer Programming solver for further improvement. We apply this hybrid framework to a section of the Danish rail network and benchmark our results against both direct application of a Mixed Integer Programming solver and modelling the problem as a Constraint Optimisation Problem. Whereas the current practice of using a general purpose Mixed Integer Programming solver is only able to solve instances over a two-week planning horizon, the hybrid framework generates good results for problem instances over an eight-week period. In addition, the use of a Mixed Integer Programming solver to improve the initial solutions generated by Constraint Programming is shown to be significantly superior to addressing the problem as a Constraint Optimisation Problem.

Suggested Citation

  • M. Pour, Shahrzad & Drake, John H. & Ejlertsen, Lena Secher & Rasmussen, Kourosh Marjani & Burke, Edmund K., 2018. "A hybrid Constraint Programming/Mixed Integer Programming framework for the preventive signaling maintenance crew scheduling problem," European Journal of Operational Research, Elsevier, vol. 269(1), pages 341-352.
    • Handle: RePEc:eee:ejores:v:269:y:2018:i:1:p:341-352
    DOI: 10.1016/j.ejor.2017.08.033
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    References listed on IDEAS

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    1. Baldi, Mauro M. & Heinicke, Franziska & Simroth, Axel & Tadei, Roberto, 2016. "New heuristics for the Stochastic Tactical Railway Maintenance Problem," Omega, Elsevier, vol. 63(C), pages 94-102.
    2. Ashish K. Nemani & Suat Bog & Ravindra K. Ahuja, 2010. "Solving the Curfew Planning Problem," Transportation Science, INFORMS, vol. 44(4), pages 506-523, November.
    3. S Boğ & A K Nemani & R K Ahuja, 2011. "Iterative algorithms for the curfew planning problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(4), pages 593-607, April.
    4. Wen, M. & Li, R. & Salling, K.B., 2016. "Optimization of preventive condition-based tamping for railway tracks," European Journal of Operational Research, Elsevier, vol. 252(2), pages 455-465.
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    Cited by:

    1. Aarabi, Fatemeh & Batta, Rajan, 2020. "Scheduling spatially distributed jobs with degradation: Application to pothole repair," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
    2. Fei Peng & Xian Fan & Puxin Wang & Mingan Sheng, 2022. "A Time-Space Network-Based Optimization Method for Scheduling Depot Drivers," Sustainability, MDPI, vol. 14(21), pages 1-19, November.
    3. Bahman Naderi & Rubén Ruiz & Vahid Roshanaei, 2023. "Mixed-Integer Programming vs. Constraint Programming for Shop Scheduling Problems: New Results and Outlook," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 817-843, July.

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