Research on Multi-Objective Flexible Job Shop Scheduling with Multiple AGVs and Machines Integration

In order to solve the flexible job shop scheduling problem with multi-time and multi-AGV constraints in the intelligent manufacturing environment, a machine AGV dual-constraint multi-objective scheduling with the goal of minimizing the maximum completion time, minimizing the total delay, and minimizing the total equipment load is constructed. In the model, multi-time factors such as processing time, workpiece arrival time, and delivery date are comprehensively considered in the model, and multi-AGV and machine integrated scheduling are carried out. In order to solve this model, a new AGV scheduling rule and an improved NSGA-Ⅱ algorithm are designed. In the algorithm, a process-based extended chromosome encoding method and a greedy decoding strategy based on AGV allocation are proposed. At the same time, a variety of group binary tournament selection and segmented cross mutation strategies controlled by different parameters and a Pareto-based deduplication elite retention strategy are designed to promote individual collaborative optimization search. Through example experiments, the model validity of different AGV quantities task allocation schemes is analyzed. The simulation test and similar algorithm of four cases have also verified the effectiveness of the improved NSGA-II algorithm to solve the model. © The Author(s) 2023.