基于双指令的倍深式货位优化

Optimization of Double-Deep Location Based on Dual Command

随着可利用土地资源的日益稀缺,仓储企业为降低成本,提高存储密度,对紧致化仓储要求愈加迫切。提高存储密度的一个重要途径是尽量减少巷道,与单倍货架相比,倍深式货架节约了巷道空间,从而实现了紧致化仓储。在考虑堆垛机的运行时间和堆垛机在货位的停留时间的基础上,建立堆垛机执行交叉存取指令的倍深式货位优化模型。为减少翻箱操作次数,根据倍深式货架特征提出Close&Open算法和指派算法对模型进行求解。仿真实验显示:批量存储的效率比单一存储的效率高;当存储密度为80%时,与随机存储算法对比,Close&Open算法提高了27.76%,指派算法提高了30.12%。

With the increasingly scarce land resources available, warehousing enterprises increasingly urgent to be the requirements of the compact storage to reduce costs and improve storage density. It＇s an important way to minimize the roadway for increasing the storage density. Compared with the single rack, double-deep racks can save the roadway space and achieve a compact storage. Based on the running time of the crane and the residence time of the crane in the locations, optimization model of double-deep racks based on dual command is established. According to the characteristics of doubledeep racks, Close ＆ Open algorithm is to solve the problem that reduce the number of rummaging operations. The simu lation results show that the mass storage efficiency is higher than that of single storage. When the storage density is 80%, the Close ＆ Open algorithm is improved by 27. 76% and the assignment algorithm is improved by 30.12% compared with the random storage algorithm.