目的 提高双深度四向穿梭车仓储系统出入库作业效率,降低设备的空载率。方法 针对多设备的加减速特性、并行作业的特性,以及出库作业中可能触发倒箱作业的概率性场景,以最小化出入库任务分配方案的总完工时间为优化目标,剖析多个四向穿梭车和多个提升机的复合作业流程,构建相应的数学模型,并提出一种多策略改进哈里斯鹰优化算法来求解该模型,利用离散事件仿真来记录各穿梭车作业总完工时间,通过Python进行过程仿真分析,来获取最优任务分配方案和最短总完工时间。结果 选取3种不同规模的任务算例,将文中所提的多策略改进哈里斯鹰优化算法,与标准哈里斯鹰优化算法、遗传算法、粒子群算法的求解表现进行对比。结果 表明,在不同规模任务案例中改进算法的求解质量均最优。结论 文中提出的复合作业任务分配方法能够有效提升双深度四向穿梭车仓储系统的作业效率。
Abstract
The work aims to enhance the efficiency of inbound and outbound operations in a double-depth four-way shuttle based storage and retrieval system and minimize equipment idle rates. Considering the acceleration/deceleration dynamics of multiple devices, the characteristics of parallel operations, and probabilistic scenarios where outbound tasks might trigger box-turning operations, with the objective of minimizing the total completion time of inbound/outbound task allocation, the integrated operational processes of multiple four-way shuttles and elevators were analyzed, and a corresponding mathematical model was constructed. A multi-strategy improved Harris Hawk optimization algorithm (MHHO) was proposed for model solution. Discrete event simulation was employed to record the total operation time of each shuttle, while Python-based process simulation was utilized to derive the optimal task scheduling scheme and the shortest total completion time. Three task examples of varying scales were selected to compare the performance of the multi-strategy improved Harris hawk optimization algorithm proposed in this work against the standard Harris Hawk optimization algorithm, genetic algorithm, and particle swarm optimization algorithm. The improved algorithm yielded superior solution quality across all task scales. The proposed compound task allocation method effectively enhances the operational efficiency of the double-depth four-way shuttle based storage and retrieval system.
关键词
双深度四向穿梭车仓储系统 /
复合作业 /
任务分配 /
离散事件仿真 /
哈里斯鹰优化算法
Key words
double-depth four-way shuttle based storage and retrieval system /
compound operations /
task allocation /
discrete event simulation /
Harris Hawk optimization
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基金
河南省科技攻关计划（232103810085,242102220029）; 河南省科技研发计划联合基金（242103810064）
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