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

  • Slim BelhaizaEmail author
  • Rym M’Hallah
  • Ghassen Ben Brahim
  • Gilbert Laporte
Article
  • 47 Downloads

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%.

Keywords

Evolutionary search Genetic algorithm Vehicle routing problem with multiple time windows Local search 

Notes

References

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mathematics and StatisticsKing Fahd University of Petroleum and MineralsDhahranKingdom of Saudi Arabia
  2. 2.Department of Statistics and Operations ResearchKuwait UniversityKuwait CityKuwait
  3. 3.Department of Computer SciencePrince Mohammad Bin Fahd UniversityDhahranKingdom of Saudi Arabia
  4. 4.CIRRELT and HEC MontréalMontrealCanada

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