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

Zhongshi Shao, Weishi Shao, Jianrui Chen, Dechang Pi

Abstract: Since severe environmental problem in manufacturing industries is becoming increasingly prominent, energy-efficient production scheduling has gained more and more attentions. This article 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 first presented to format it. Then, a meta-Q-learning-based multiobjective 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 (DCH) 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 multiobjective 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.

External IDs:dblp:journals/tec/ShaoSCP25