Preprint Article Version 1 This version is not peer-reviewed

An Improved Genetic Algorithm for Vehicle Routing Problem with Time Windows

Version 1 : Received: 11 April 2018 / Approved: 12 April 2018 / Online: 12 April 2018 (15:55:54 CEST)

How to cite: Wei, C.; Qiu, C.; Xin, Q.; Fan, Z. An Improved Genetic Algorithm for Vehicle Routing Problem with Time Windows. Preprints 2018, 2018040169 (doi: 10.20944/preprints201804.0169.v1). Wei, C.; Qiu, C.; Xin, Q.; Fan, Z. An Improved Genetic Algorithm for Vehicle Routing Problem with Time Windows. Preprints 2018, 2018040169 (doi: 10.20944/preprints201804.0169.v1).

Abstract

The vehicle routing problem (VRP) has attracted lots of attentions recently and efficient and advanced solutions for VRP are not only playing a very important role in logistics and operational research but also impact significantly on a number of environmental economic and social issues due to potential traffic congestion, environmental pollution, noise pollution, and so on. In this paper, we propose an external archive guided multi-objective evolutionary algorithm (EAG-MOEA/D) to solve the vehicle routing problem with time windows (VRPTW). This is a well-known combinatorial multi-objective optimization problem that addresses using a homogeneous fleet of capacitated vehicles to serve a set of customers within fixed time intervals. The objective is to minimize the fleet size and total length of the routes simultaneously. Combining with our new local search method, Timing Pool Local Search (TPLS), we demonstrate our new genetic algorithm EAG-MOEA/D-TPLS can obtain more non-dominated solutions and outperform the current state-of-the-art algorithms in the literature. Moreover, the experimental results also show that our EAG-MOEA/D-TPLS algorithm has a better performance than most famous MOEA/D scheme on 56 Solomon instances. We hope that our work can stimulate the further research on multi-objective evolutionary algorithms for the VRPTW.

Subject Areas

combinatorial multi-objective optimization; Vehicle Routing Problem with Time Windows (VRPTW); Timing Pool Local Search (TPLS); genetic algorithms;

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