Publication Type

Journal Article

Version

publishedVersion

Publication Date

8-2021

Abstract

The multi-objective vehicle routing problem with stochastic demand (MO-VRPSD) is much harder to tackle than other traditional vehicle routing problems (VRPs), due to the uncertainty in customer demands and potentially conflicted objectives. In this paper, we present an improved multi-objective learnable evolution model (IMOLEM) to solve MO-VRPSD with three objectives of travel distance, driver remuneration and number of vehicles. In our method, a machine learning algorithm, i.e., decision tree, is exploited to help find and guide the desirable direction of evolution process. To cope with the key issue of "route failure" caused due to stochastic customer demands, we propose a novel chromosome representation based on priority with bubbles. Moreover, an efficient nondominated sort using a sequential search strategy (ENS-SS) in conjunction with some heuristic operations are leveraged to handle the multi-objective property of the problem. Our algorithm is evaluated on the instances of modified Solomon VRP benchmark. Experimental results show that the proposed IMOLEM is capable to find better Pareto front of solutions and also deliver superior performance to other evolutionary algorithms. (C) 2021 Elsevier B.V. All rights reserved.

Keywords

Vehicle routing problems, Stochastic demand, Learnable evolution model, Multi-objective evolutionary algorithm

Discipline

OS and Networks | Transportation

Research Areas

Intelligent Systems and Optimization

Publication

Knowledge-Based Systems

Volume

230

First Page

1

Last Page

19

ISSN

0950-7051

Identifier

10.1016/j.knosys.2021.107378

Publisher

Elsevier

Copyright Owner and License

Authors

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