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Open Access

Biased Bi-Population Evolutionary Algorithm for Energy-Efficient Fuzzy Flexible Job Shop Scheduling with Deteriorating Jobs

School of Information Science and Engineering, Harbin Institute of Technology, Weihai 264209, China
Department of Computer Science, Maynooth University, Maynooth, W23 F2H6, Ireland, and also with the School of Computing, Dublin City University, Dublin, D09 V209, Ireland
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Abstract

There are many studies about flexible job shop scheduling problem with fuzzy processing time and deteriorating scheduling, but most scholars neglect the connection between them, which means the purpose of both models is to simulate a more realistic factory environment. From this perspective, the solutions can be more precise and practical if both issues are considered simultaneously. Therefore, the deterioration effect is treated as a part of the fuzzy job shop scheduling problem in this paper, which means the linear increase of a certain processing time is transformed into an internal linear shift of a triangle fuzzy processing time. Apart from that, many other contributions can be stated as follows. A new algorithm called reinforcement learning based biased bi-population evolutionary algorithm (RB2EA) is proposed, which utilizes Q-learning algorithm to adjust the size of the two populations and the interaction frequency according to the quality of population. A local enhancement method which combimes multiple local search stratgies is presented. An interaction mechanism is designed to promote the convergence of the bi-population. Extensive experiments are designed to evaluate the efficacy of RB2EA, and the conclusion can be drew that RB2EA is able to solve energy-efficient fuzzy flexible job shop scheduling problem with deteriorating jobs (EFFJSPD) efficiently.

Keywords

energyflexible job shop schedulingQ-learning algorithmfuzzybi-population evolutionary algorithmdeteriorating effect

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Complex System Modeling and Simulation
Volume 4 Issue 1,
March 2024
Pages 15-32
DOI: 10.23919/CSMS.2023.0021
Cite this article:
Deng L, Zhu Y, Di Y, et al. Biased Bi-Population Evolutionary Algorithm for Energy-Efficient Fuzzy Flexible Job Shop Scheduling with Deteriorating Jobs. Complex System Modeling and Simulation, 2024, 4(1): 15-32. https://doi.org/10.23919/CSMS.2023.0021

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Received: 06 September 2023
Revised: 17 October 2023
Accepted: 29 October 2023
Published: 30 March 2024
© The author(s) 2024.

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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