MQL-MM: A Meta-Q-Learning-Based Multi-Objective Metaheuristic for Energy-Efficient Distributed Fuzzy Hybrid Blocking Flow-Shop Scheduling Problem

Since severe environmental problem in manufacturing industries is becoming increasingly prominent, energy-efficient production scheduling has gained more and more attentions. This paper studies an energy-efficient distributed fuzzy hybrid blocking flow-shop scheduling problem (EEDFHBFSP), where proc...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2024, p.1-1
Main Authors: Shao, Zhongshi, Shao, Weishi, Chen, Jianrui, Pi, Dechang
Format: Article
Language:English
Subjects:
Aluminum
Distributed flow-shop scheduling with blocking
Energy consumption
Energy-efficient scheduling
Evolutionary computation
Fuzzy time
Job shop scheduling
Meta-learning
Metaheuristics
Production
Q-learning
Search problems
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Summary:Since severe environmental problem in manufacturing industries is becoming increasingly prominent, energy-efficient production scheduling has gained more and more attentions. This paper studies an energy-efficient distributed fuzzy hybrid blocking flow-shop scheduling problem (EEDFHBFSP), where processing time and setup time are uncertain. The objective is to minimize fuzzy makespan and total fuzzy energy consumption simultaneously. To solve such problem, a mixed-integer linear programming model is firstly presented to format it. Then, a meta-Q-learning-based multi-objective metaheuristic (MQL-MM) is proposed. In MQL-MM, a machine-position-based dispatch rule is designed as the decoding scheme. A decomposition-based constructive heuristic is employed to generate the initial population with high quality and diversity. Several problem-specific search operators are developed to explore and exploit the solution space. A meta-Q-learning-based multi-objective search framework is presented to guide the using of search operators, which includes a meta-training phase and an adaptive search phase. The meta-training phase is employed to train the search operators to construct the Q-learning model. The adaptation search phase utilizes such model to conduct the automatic selection of the search operators. Moreover, an energy saving strategy is designed to improve the candidate solutions. Finally, we conduct extensive experiments. The experimental results show that the designs of MQL-MM are effective, and MQL-MM performs better than several well-performing methods on solving EEDFHBFSP.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2024.3399314