PERFORMANCE COMPARISON OF META-HEURISTICS FOR THE MULTIBLOCK WAREHOUSE ORDER PICKING PROBLEM

Abstract

This study focuses on streamlining the order-picking process in a warehouse. We consider determining the picking sequence of items in a pick-list to minimize the total traveled distance in a multiblock warehouse, where a low-level picker-to-parts manual picking system is employed. We assume that the items are stored randomly in the warehouse. First, we construct a distance matrix of the shortest path between any pair of items. Next, using the distance matrix, we implement two meta-heuristics-the tabu search algorithm and the iterated greedy algorithm-to determine the picking sequence with the minimum total traveled distance. Through a numerical study, the performances of the meta-heuristic algorithms are compared with those of popular rule-based heuristics (S-shape, largest gap, and Combined+) and the bestknown solutions. We conducted the numerical study in two stages. In the first stage, we considered a two-block rectangular warehouse, and in the second stage, we considered a three-block rectangular warehouse. The performance of the heuristics was calculated based on the optimal solution when available or the best calculated bound when the optimal solution is not available. We observed that the iterated greedy algorithm significantly outperforms the other heuristics for both stages.