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Optimising Rolling Stock Planning including Maintenance with Constraint Programming and Quantum Annealing

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  • Patricia Bickert
  • Cristian Grozea
  • Ronny Hans
  • Matthias Koch
  • Christina Riehn
  • Armin Wolf

Abstract

We propose and compare Constraint Programming (CP) and Quantum Annealing (QA) approaches for rolling stock assignment optimisation considering necessary maintenance tasks. In the CP approach, we model the problem with an Alldifferent constraint, extensions of the Element constraint, and logical implications, among others. For the QA approach, we develop a quadratic unconstrained binary optimisation (QUBO) model. For evaluation, we use data sets based on real data from Deutsche Bahn and run the QA approach on real quantum computers from D-Wave. Classical computers are used to evaluate the CP approach as well as tabu search for the QUBO model. At the current development stage of the physical quantum annealers, we find that both approaches tend to produce comparable results.

Suggested Citation

  • Patricia Bickert & Cristian Grozea & Ronny Hans & Matthias Koch & Christina Riehn & Armin Wolf, 2021. "Optimising Rolling Stock Planning including Maintenance with Constraint Programming and Quantum Annealing," Papers 2109.07212, arXiv.org, revised Sep 2023.
    • Handle: RePEc:arx:papers:2109.07212
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    1. Lusby, Richard M. & Haahr, Jørgen Thorlund & Larsen, Jesper & Pisinger, David, 2017. "A Branch-and-Price algorithm for railway rolling stock rescheduling," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 228-250.
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