A Q-Learning-Assisted Co-Evolutionary Algorithm for Distributed Assembly Flexible Job Shop Scheduling Problems

With the development of economic globalization, distributed manufacturing is becoming more and more prevalent. Recently, integrated scheduling of distributed production and assembly has captured much concern. This research studies a distributed flexible job shop scheduling problem with assembly operations. Firstly, a mixed integer programming model is formulated to minimize the maximum completion time. Secondly, a Q-learning-assisted co-evolutionary algorithm is presented to solve the model: (1) Multiple populations are developed to seek required decisions simultaneously; (2) An encoding and decoding method based on problem features is applied to represent individuals; (3) A hybrid approach of heuristic rules and random methods is employed to acquire a high-quality population; (4) Three evolutionary strategies having crossover and mutation methods are adopted to enhance exploration capabilities; (5) Three neighborhood structures based on problem features are constructed, and a Q-learning-based iterative local search method is devised to improve exploitation abilities. The Q-learning approach is applied to intelligently select better neighborhood structures. Finally, a group of instances is constructed to perform comparison experiments. The effectiveness of the Q-learning approach is verified by comparing the developed algorithm with its variant without the Q-learning method. Three renowned meta-heuristic algorithms are used in comparison with the developed algorithm. The comparison results demonstrate that the designed method exhibits better performance in coping with the formulated problem.