A Discrete Grey Wolf Optimizer with an Active-Decoding Strategy for Reconfigurable Manufacturing System Scheduling Problem

Reconfigurable manufacturing systems offer enhanced flexibility to adapt to rapidly changing market demands. However, the reconfigurability of equipment introduces significant challenges to production scheduling, complicating optimization. This paper addresses the scheduling problem in reconfigurable manufacturing systems and proposes a discrete grey wolf optimizer algorithm with an active-decoding strategy (DGWO). A novel operation-configuration encoding scheme is proposed to comprehensively represent the solution space, accompanied by an active-decoding strategy that maximizes solution exploration and minimizes idle time. In the GWO, two crossover operators are introduced to enhance the search space, while the random walk strategy is introduced to prevent the algorithm from falling into premature convergence. Additionally, four neighborhood structures are defined based on the encoding space, and an efficient randomized enhanced local search is developed based on these structures to improve the algorithm's exploitation capability. The proposed DGWO algorithm is evaluated on 60 benchmark instances and compared with several related algorithms, demonstrating superior effectiveness and convergence performance.