Abstract: At present,the multi-deep four-way shuttle warehousing system has problems such as multiple four-way shuttle conflicts and deadlocks,which cause the blockage of inbound and outbound tasks,affecting the overall efficiency of the system.Hopcroft-Tarjan algorithm was used to formulate route orientation strategy for the multi-deep storage area.A scheduling optimization model with the goal of minimum operating time was established,and an Improved Hybrid Genetic Algorithm (IHGA)  was designed to solve the optimization model.The adjustment of coding and the improvement of mutation repair mechanism could effectively avoid the problem of illegal solutions in the iterative process.A multi-position neighborhood exchange method based on task sorting was proposed for increasing the diversity of the solution space effectively.The case study showed that the route orientation strategy could effectively avoid conflicts and deadlocks and improve system operation efficiency.Meanwhile,the IHGA had faster convergence speed and higher optimization efficiency,which could effectively shorten the time of inbound and outbound operations and improve the efficiency of inbound and outbound scheduling.

Key words: multi-deep four-way shuttle warehousing system, route orientation, scheduling optimization, improved hybrid genetic algorithm, operating efficiency

摘要： 目前,多深度四向穿梭车仓储系统存在多台穿梭车同时作业易造成冲突死锁问题,导致出入库任务阻塞,进而影响系统的作业效率。针对该问题,采用Hopcroft-Tarjan算法对多深度储货区制定路径定向策略,以最小作业时间为目标构建出入库作业调度模型,并设计混合遗传算法对该模型进行求解。为避免迭代过程中易出现非法解的问题,对编码和变异修复机制进行了改进;提出一种基于任务排序的多位置邻域交换法,从而有效增加解空间的多样性。实例研究表明,路径定向策略可以有效避免冲突死锁问题,提升系统作业效率;同时,改进混合遗传算法的收敛速度更快、优化效率更高,能够有效地缩短出入库作业时间,提高出入库调度效率。

关键词: 多深度四向穿梭车仓储系统, 路径定向, 调度优化, 改进混合遗传算法, 作业效率