Paper Info
Title
Three multi-start data-driven evolutionary heuristics for the vehicle routing problem with multiple time windows.
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%.
Year
DOI
Venue
2019
10.1007/s10732-019-09412-1
Journal of Heuristics
Keywords
Field
DocType
Evolutionary search, Genetic algorithm, Vehicle routing problem with multiple time windows, Local search
Mathematical optimization,Vehicle routing problem,Data-driven,Overcurrent,Average cost,Heuristics,Operator (computer programming),Local search (optimization),Genetic algorithm,Mathematics
Journal
Volume
Issue
ISSN
25
3
1381-1231
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Slim Belhaiza1364.35
R. M'hallah249231.55
Ghassen Ben Brahim3559.05
Gilbert Laporte41469.53