Publication Type

Journal Article

Version

acceptedVersion

Publication Date

7-2022

Abstract

Solving the multi-objective vehicle routing problem with stochastic demand (MO-VRPSD) is challenging due to its non-deterministic property and conflicting objectives. Most multi -objective evolutionary algorithm dealing with this problem update current population without any guidance from previous searching experience. In this paper, a multi -objective evolutionary algorithm based on artificial neural networks is proposed to tackle the MO-VRPSD. Particularly, during the evolutionary process, a radial basis function net-work (RBFN) is exploited to learn the potential knowledge of individuals, generate hypoth-esis and instantiate hypothesis. The RBFN evaluates individuals with different scores and generates new individuals with higher quality while taking into account the non -dominated relationship between individuals. Moreover, integrated with a specific non -dominated sorting strategy, i.e., ENS-SS, along with several effective heuristic operations, the proposed algorithm performs favorably for solving the MO-VRPSD. The experimental results based on the modified Solomon benchmark instances verified the effectiveness of the respective components, and the superiority to other multi-objective evolutionary algorithms. (c) 2022 Elsevier Inc. All rights reserved.

Keywords

Vehicle routing problem;Stochastic demand;Learnable evolution model;Multi -objective evolutionary algorithm;Radial basis function network;Vehicle routing problem;Stochastic demand;Learnable evolution model;Multi -objective evolutionary algorithm;Radial basis function network

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering | Theory and Algorithms

Research Areas

Intelligent Systems and Optimization

Publication

Information Sciences

Volume

609

First Page

387

Last Page

410

ISSN

0020-0255

Identifier

10.1016/j.ins.2022.07.087

Publisher

Elsevier

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1016/j.ins.2022.07.087

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