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Three multi-start data-driven evolutionary heuristics for the vehicle routing problem with multiple time windows

Author

Listed:
  • Slim Belhaiza

    (King Fahd University of Petroleum and Minerals)

  • Rym M’Hallah

    (Kuwait University)

  • Ghassen Ben Brahim

    (Prince Mohammad Bin Fahd University)

  • Gilbert Laporte

    (CIRRELT and HEC Montréal)

Abstract

This paper considers the vehicle routing problem with multiple time windows. It introduces a general framework for three evolutionary heuristics that use three global multi-start strategies: ruin and recreate, genetic cross-over of best parents, and random restart. The proposed heuristics make use of information extracted from routes to guide customized data-driven local search operators. The paper reports comparative computational results for the three heuristics on benchmark instances and identifies the best one. It also shows more than 16% of average cost improvement over current practice on a set of real-life instances, with some solution costs improved by more than 30%.

Suggested Citation

  • Slim Belhaiza & Rym M’Hallah & Ghassen Ben Brahim & Gilbert Laporte, 2019. "Three multi-start data-driven evolutionary heuristics for the vehicle routing problem with multiple time windows," Journal of Heuristics, Springer, vol. 25(3), pages 485-515, June.
    • Handle: RePEc:spr:joheur:v:25:y:2019:i:3:d:10.1007_s10732-019-09412-1
    DOI: 10.1007/s10732-019-09412-1
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    References listed on IDEAS

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    1. Pierre Hansen & Nenad Mladenović & José Moreno Pérez, 2010. "Variable neighbourhood search: methods and applications," Annals of Operations Research, Springer, vol. 175(1), pages 367-407, March.
    2. Zhang, Jianghua & Zhao, Yingxue & Xue, Weili & Li, Jin, 2015. "Vehicle routing problem with fuel consumption and carbon emission," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 234-242.
    3. Jean-Yves Potvin & Samy Bengio, 1996. "The Vehicle Routing Problem with Time Windows Part II: Genetic Search," INFORMS Journal on Computing, INFORMS, vol. 8(2), pages 165-172, May.
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