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An estimation of distribution algorithm for public transport driver scheduling

Author

Listed:
  • Yindong Shen
  • Jingpeng Li
  • Kunkun Peng

Abstract

Public transport driver scheduling is a process of selecting a set of duties for the drivers of vehicles to form a number of legal driver shifts. The problem usually has two objectives which are minimising both the total number of shifts and the total shift cost, while taking into account some constraints related to labour and company rules. A commonly used approach is firstly to generate a large set of feasible shifts by domain-specific heuristics, and then to select a subset to form the final schedule by an integer programming method. This paper presents an estimation of distribution algorithm (EDA) to deal with the subset selection problem which is NP-hard. To obtain a candidate schedules, the EDA applies a number of rules, with each rule corresponding to a particular way of selecting a shift. Computational results from some real-world instances of drive scheduling demonstrate the availability of this approach.

Suggested Citation

  • Yindong Shen & Jingpeng Li & Kunkun Peng, 2017. "An estimation of distribution algorithm for public transport driver scheduling," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 28(2), pages 245-262.
    • Handle: RePEc:ids:ijores:v:28:y:2017:i:2:p:245-262
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    Cited by:

    1. Vicente P. Soloviev & Pedro Larrañaga & Concha Bielza, 2022. "Estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables," Journal of Combinatorial Optimization, Springer, vol. 44(2), pages 1077-1098, September.
    2. Heil, Julia & Hoffmann, Kirsten & Buscher, Udo, 2020. "Railway crew scheduling: Models, methods and applications," European Journal of Operational Research, Elsevier, vol. 283(2), pages 405-425.

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