TY - JOUR

T1 - A knowledge-based evolutionary algorithm for the multiobjective vehicle routing problem with time windows

AU - Chiang, Tsung Che

AU - Hsu, Wei Huai

N1 - Funding Information:
The authors are grateful to the editor and the reviewers for their valuable suggestions and comments. This research was supported by the National Science Council of the Republic of China (Taiwan) under Grant no. NSC100-2221-E-003-004.

PY - 2014/5

Y1 - 2014/5

N2 - This paper addresses the multiobjective vehicle routing problem with time windows (MOVRPTW). The objectives are to minimize the number of vehicles and the total distance simultaneously. Our approach is based on an evolutionary algorithm and aims to find the set of Pareto optimal solutions. We incorporate problem-specific knowledge into the genetic operators. The crossover operator exchanges one of the best routes, which has the shortest average distance, the relocation mutation operator relocates a large number of customers in non-decreasing order of the length of the time window, and the split mutation operator breaks the longest-distance link in the routes. Our algorithm is compared with 10 existing algorithms by standard 100-customer and 200-customer problem instances. It shows competitive performance and updates more than 1/3 of the net set of the non-dominated solutions.

AB - This paper addresses the multiobjective vehicle routing problem with time windows (MOVRPTW). The objectives are to minimize the number of vehicles and the total distance simultaneously. Our approach is based on an evolutionary algorithm and aims to find the set of Pareto optimal solutions. We incorporate problem-specific knowledge into the genetic operators. The crossover operator exchanges one of the best routes, which has the shortest average distance, the relocation mutation operator relocates a large number of customers in non-decreasing order of the length of the time window, and the split mutation operator breaks the longest-distance link in the routes. Our algorithm is compared with 10 existing algorithms by standard 100-customer and 200-customer problem instances. It shows competitive performance and updates more than 1/3 of the net set of the non-dominated solutions.

KW - Evolutionary algorithm

KW - Multiobjective

KW - Pareto optimal

KW - Time windows

KW - Vehicle routing problem

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U2 - 10.1016/j.cor.2013.11.014

DO - 10.1016/j.cor.2013.11.014

M3 - Article

AN - SCOPUS:84891442641

VL - 45

SP - 25

EP - 37

JO - Computers and Operations Research

JF - Computers and Operations Research

SN - 0305-0548

ER -