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Open Access

A Discrete Artificial Bee Colony Algorithm for Stochastic Vehicle Scheduling

Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Division of Computer Science and Mathematics, University of Stirling, Stirling, FK9 4LA, UK
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Abstract

Vehicle scheduling plays a profound role in public transportation. Especially, stochastic vehicle scheduling may lead to more robust schedules. To solve the stochastic vehicle scheduling problem (SVSP), a discrete artificial bee colony algorithm (DABC) is proposed. Due to the discreteness of SVSP, in DABC, a new encoding and decoding scheme with small dimensions is designed, whilst an initialization rule and three neighborhood search schemes (i.e., discrete scheme, heuristic scheme, and learnable scheme) are devised individually. A series of experiments demonstrate that the proposed DABC with any neighborhood search scheme is able to produce better schedules than the benchmark results and DABC with the heuristic scheme performs the best among the three proposed search schemes.

Keywords

public transportationstochastic vehicle schedulingdiscrete artificial bee colonysearch scheme

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Complex System Modeling and Simulation
Volume 2 Issue 3,
September 2022
Pages 238-252
DOI: 10.23919/CSMS.2022.0012
Cite this article:
Li Y, Shen Y, Li J. A Discrete Artificial Bee Colony Algorithm for Stochastic Vehicle Scheduling. Complex System Modeling and Simulation, 2022, 2(3): 238-252. https://doi.org/10.23919/CSMS.2022.0012

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Received: 18 April 2022
Revised: 01 July 2022
Accepted: 05 July 2022
Published: 30 September 2022
© The author(s) 2022

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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