【摘要】 在实际工业生产过程中，由于自动导引车(Automated Guided Vehicles, AGVs)资源有限，因此在柔性作业车间调度问题(Flexible Job-shop Scheduling Problem, FJSP)中考虑有限AGV数量约束(FJSP-AGV)的集成问题有重要的研究价值。传统的进化算法容易陷入局部最优，不适用于求解此类复杂程度较高的调度问题。针对以上难点，首先对FJSP-AGV集成问题建立数学模型；然后提出了基于启发式规则引导的改进遗传算法，算法针对不同编码段采用多种交叉、变异方式进化种群，同时在进化过程中作参数自适应调整，并通过启发式规则引导变异进行局部搜索，提高算法跳出局部最优的能力，从而实现系统最大完工时间的最小化。通过在两组中小规模数据集上与其他先进算法的对比分析可知，所提算法的整体求解效果最优。更多还原

【Abstract】 In the actual industrial production process, due to the limited resources of Automate Guided Vehicles(AGVs), the integrated problem FJSP-AGV comsidering the constraint of alimited number of AGVs in the Flexible Job-shop Scheduling Problem(FJSP) has significant research value. Traditional evolutionary algorithms are easy to fall into local optimum and are not suitable for solving this scheduling problem with high complexity.In light of the aforementioned challenges, it initially established a mathematical model for FJSP-AGV and subsequently proposed an improved genetic algorithm guided by heuristic rules. The algorithm utilized various crossover and mutation methods to evolve the population for different coding segments.Simultaneously, it adjusted parameters adaptively during the evolutionary process and guided mutations through heuristic rules for local search, thereby enhancing the algorithm′s capability to escape local optima and consequently minimize the maximum completion time of the system. Comparison and analysis with other advanced algorithms on two small and medium-sized datasets demonstrated that the algorithm proposed yielded the most comprehensive solving effect.更多还原