摘要
传统的货位优化方法没有充分利用库存量单位(Stock keeping units,SKUs)之间的相关性关系。以一种存在相关性需求的'波次分区拣货、整体补货'的周期性环境为对象,以最小化最大的分区拣货时间为目标建立货位优化的数学模型,提出相关性强度的概念和计算方法,设计出基于相关性的货位指派算法(Storage allocation based on correlations,SABC)和不考虑相关性的随机货位指派算法(Storage allocation based on random,SABR)算法,SABC算法以体积—订单指数(Cube per order index,COI)法则的解为初始解,通过定量化的'相关性位置交换策略'将相关性强的'SKUs对'指派到相近的货位中来提高拣货效率。测试结果表明:SABC算法具有较好的收敛性,其收敛速度明显优于SABR算法,求解质量比COI法平均改进约7.6%～25.1%,比SABR算法平均改进约1.36%～14.50%;需求相关性强度越高,拣货效率提升潜力越大。
The traditional studies on slotting lack exploitation to the correlation of stock keeping units（SKUs）.In a wave-picking zone-based dynamic picking system where the whole replenishment is periodic,the concept and calculation way of the SKU＇s correlation strength are proposed,a mix integer program model for slotting to minimize the max pick wave make span among all zones is described.The storage allocation based on correlations（SABC） algorithm which is based on SKUs＇ demand correlation and storage allocation based on random（SABR） algorithm which ignores the SKUs＇ demand correlation are developed.The SABC algorithm sets the cube per order index（COI） solution as the initial solution;the slots interchange policy is proposed to reduce the pick wave make span by reassign the SKUs pair to the closed slots.The promising computational results show that the SABC algorithm has perfect convergence,and it needs much less CPU time than the SABR algorithm.The solution of SABC algorithm is better than both COI and SABC algorithm,the average improvement vary from 7.6% to 25.1% and from 1.36% to 14.5% respectively.As the correlation strength becomes stronger,there will be more improvements to the picking efficiency.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2011年第20期75-80,88,共7页
Journal of Mechanical Engineering
基金
浙江省科技厅(2009C31025)
浙江省教育厅(Y201018235)资助项目
关键词
货位优化
库存量单位相关性
基于相关性的货位指派算法算法
位置变换策略
负荷均衡
拣货效率
Storage allocation Stock keeping units＇ correlations Storage allocation based on correlations algorithm Slots interchange policy Load balancing Picking efficiency
