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

Slim Belhaiza (), Rym M’Hallah (), Ghassen Ben Brahim () and Gilbert Laporte ()
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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

Journal of Heuristics, 2019, vol. 25, issue 3, No 6, 485-515

Abstract: 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 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10732-019-09412-1

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