Abstract: Flying-V layout is a classic non-traditional warehouse layout, which operates more efficiently compared with traditional warehouse layout. Here a crossing aisles design approach to the flying-V warehouse layout is studied. Firstly, the crossing aisles are abstracted into a conjoint curve, and non-complete random storage assignment strategy based on different probability in each picking aisle is considered, and then an optimization model for crossing aisle layout is built to minimize average picking distance. By adopting parallel search strategy, an improved particle swarm optimization algorithm with extremedisturbed operator(EDO-PSO)is developed. The specific encoding and operators are devised. Especially, in order to avoid local optimum, the periodic disturbing operator is given. Moreover, the algorithm performance is analyzed by comparing with other improved PSO algorithms with Benchmark functions. Finally, a case study is used to evaluate the effectiveness of the proposed algorithm. The calculation results demonstrate the proposed approach can effectively shorten the total picking distance in the same storage assignment strategy. Contributions of the paper are the modeling and algorithm to crossing aisle layout design in flying-V warehouse.

摘要： V型仓储布局是一种典型的非传统布局方式,针对V型布局主通道设计的问题,将主通道抽象为若干个点连接而成的折线通道,每条拣货通道按物动量大小对仓库进行分区,采用更加符合实际的存取货物作业的概率不相等的非完全随机存储策略,建立最小化平均拣货距离的仓库主通道设计数学优化模型。其次,设计了基于极值扰动算子的改进粒子群优化算法(EDO-PSO)进行算法求解,利用极值扰动算子解决易陷入局部最优问题,采用并行深度搜索策略,提高算法性能,并用Benchmark函数与其他改进PSO算法对比验证算法性能。最后,结合具体实验数据仿真分析,计算结果表明,该方法在相同货位分配策略下,能有效缩短总拣货距离,验证了方法的有效性。

关键词: 非传统仓储, V型布局, 通道设计, 粒子群算法, 物动量分类