Abstract: 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.

Key words: flexible job shop scheduling, multiple time factors, automatic guided vehicle, multi-objective optimization, NSGA-Ⅱ algorithm

摘要： 为解决智能制造环境中具有多时间和多AGV约束的柔性作业车间调度问题，构建了以最小化最大完工时间、最小化总延期、最小化设备总负荷为目标的机器/AGV双约束多目标调度模型，模型中综合考虑加工时间、工件到达时间、交货期等多时间因素，进行了多AGV和机器集成调度。为求解该模型，设计了新的AGV调度规则和改进的NSGA-Ⅱ算法，算法中提出了基于工序的扩展染色体编码方式和基于AGV分配的贪婪式解码策略，同时设计了不同参数控制的多种群二元锦标赛选择和分段交叉变异策略以及基于Pareto级的去重精英保留策略，以促进个体协同优化搜索。通过实例实验，分析了不同AGV数量任务分配方案下的模型有效性，对4个案例的仿真测试和同类算法比较解也验证了改进NSGA-Ⅱ算法求解该模型的有效性。

关键词: 柔性作业车间调度, 多时间因素, 自动导引小车, 多目标优化, NSGA-Ⅱ算法