In a flexible job shop environment, using Automated Guided Vehicles (AGVs) to transport jobs and process materials is an important way to promote the intelligence of the workshop. Compared with single-load AGVs, multi-load AGVs can improve AGV utilization, reduce path conflicts, etc. Therefore, this study proposes a history-guided regional partitioning algorithm (HRPEO) for the flexible job shop scheduling problem with limited multi-load AGVs (FJSPMA). First, the encoding and decoding rules are designed according to the characteristics of multi-load AGVs, and then the initialization rule based on the branch and bound method is used to generate the initial population. Second, to prevent the algorithm from falling into a local optimum, the algorithm adopts a regional partitioning strategy. This strategy divides the solution space into multiple regions and measures the potential of the regions. After that, cluster the regions into multiple clusters in each iteration, and selects individuals for evolutionary search based on the set of clusters. Third, a local search strategy is designed to improve the exploitation ability of the algorithm, which uses a greedy approach to optimize machines selection and transportation sequence according to the characteristics of FJSPMA. Finally, a large number of experiments are carried out on the benchmarks to test the performance of the algorithm. Compared with multiple advanced algorithms, the results show that the HRPEO has a better advantage in solving FJSPMA.

翻译：在柔性作业车间环境中，使用自动导引车（AGV）运输工件和加工物料是推动车间智能化的重要途径。与单载AGV相比，多载AGV能够提高AGV利用率、减少路径冲突等。因此，本研究针对有限多载AGV的柔性作业车间调度问题（FJSPMA），提出一种历史引导的区域划分进化优化算法（HRPEO）。首先，根据多载AGV的特点设计编码与解码规则，并采用基于分支定界法的初始化规则生成初始种群。其次，为防止算法陷入局部最优，算法采用区域划分策略。该策略将解空间划分为多个区域并评估区域潜力，随后在每次迭代中将区域聚类为多个簇，并基于簇集合选择个体进行进化搜索。第三，设计了一种局部搜索策略以提升算法的开采能力，该策略采用贪心方法，根据FJSPMA的特点优化机器选择和运输序列。最后，在基准测试集上进行了大量实验以验证算法性能。与多种先进算法的对比结果表明，HRPEO在求解FJSPMA问题上具有更好的优势。