Clustering techniques for stock location and order-picking in a distribution center

In this paper, the stock location and order-picking problems in a distribution center where items are distributed in less-than-case-lot quantities are addressed. Clustering techniques are applied to group items in the slots of gravity-flow racks and to sequence the picking lists by customers. Based on the order-item-quantity rule, two similarity measures are defined for items and customers, respectively. A zero–one integer programming model developed for optimal items- and customers-grouping is presented. An exact primal–dual type algorithm is explored and implemented, where real world data collected from a local distribution center are employed. Simulation results indicate the potential benefits of the proposed techniques.