A DQL-NSGA-III algorithm for solving the flexible job shop dynamic scheduling problem
Hongtao Tang, Xiao Yu, Wei Zhang, Deming Lei, Jing Wang, Tao Xu
Abstract
In recent years, the flexible job shop dynamic scheduling problem (FJDSP) has received considerable attention; however, FJDSP with transportation resource constraint is seldom investigated. In this study, FJDSP with transportation resource constraint is considered and an improved non-dominated sorting genetic algorithm-III (NSGA-III) algorithm (DQNSGA) integrated with reinforcement learning (RL) is proposed. In DQNSGA, an initialization method based on heuristic rules and an insertional greedy decoding approach are designed, and a double-Q Learning with an improved ε -greedy strategy is used to adaptively adjust the key parameters of NSGA-III. An improved elite selection strategy is also applied. Through extensive experiments and practical case studies, this algorithm has been compared with three other well-known algorithms. The results demonstrate that DQNSGA exhibits significant effectiveness and superiority in all tests. The research presented in this paper enables effective adjustments of production plans in response to dynamic events, which is of critical importance for production management in the manufacturing industry.