A new policy for scattered storage assignment to minimize picking travel distances

Abstract

Various retail and e-commerce companies face the challenge of picking a large number of time-critical customer orders that include both a small number of items and multiple order lines. To reduce the unproductive work time of order pickers, several storage assignment policies have been proposed in the literature and in practice. In case of the scattered storage assignment (SSA) policy individual items are intentionally distributed to multiple positions in the picking area to increase the probability that items belonging to the same order can be picked at nearby positions. In this paper, we examine our recently proposed SSA policy that seeks to minimize the sum of pairwise distances (SPD) between all item positions that belong to the same order, including a drop-off point. We develop an efficient variable neighborhood search (VNS) metaheuristic to solve large instances in a reasonable computation time. We tested our SSA-SPD strategy by implementing a picking algorithm that considers multiple drop-off points and tracks inventory in the meantime. Our results show that our SSA-SPD policy helps reduce picking distances by up to 36% compared to a random scatter policy and 56% compared to a volume-based policy, depending on the number of order lines and drop-off points in the problem instance.