Flexible job shop scheduling optimization with limited AGV transportation based on reinforcement learning and SPEA2 algorithm

Aiming at the flexible job shop scheduling problem with limited AGV transportation, this paper presents an AGV and machine-integrated scheduling model with the optimisation objectives of minimising the maximum completion time and total energy consumption. It proposes an Improved SPEA2 Algorithm Combining DQN Algorithm (DQN-ISPEA2). The algorithm adopts a three-stage coding scheme for the workpiece, machine, and AGV. In the population initialisation stage, a hybrid initialisation strategy is employed to enhance the quality of the initial solution. DQN is used to adaptively select appropriate mutation operations, thereby enhancing the local search capability of the SPEA2 algorithm. Finally, it is compared with NSGA-III, NSGA-II, and SPEA2 algorithms on the MK and Kacem datasets. The experimental results demonstrate that the DQN-ISPEA2 algorithm yields satisfactory outcomes in most arithmetic cases, thereby verifying the feasibility and effectiveness of the proposed method in addressing the flexible job shop scheduling problem with limited AGV transportation. At the same time, the analysis of the impact of different AGV numbers on the optimisation objective reveals that the number of AGVs in the flexible workshop conforms to the law of diminishing marginal returns, providing a reference for actual manufacturing workshops when configuring AGVs.