A real-life study on the value of integrated optimization in order picking operations under dynamic order arrivals

Abstract

Optimizing the order picking operations is indispensable for warehouses that promise a high customer service level. While many areas for improvement have been identified and studied in the literature, a large gap remains between academia and practice. To help with closing this gap, we perform a case-study in collaboration with a spare-parts warehouse in Belgium. In this study, we optimize the order picking operations of the company, using the actual warehouse layout and real order data. A state-of-the-art online integrated order batching, picker routing and batch scheduling algorithm is adapted to consider multiple real-life constraints. More specifically, the dynamic arrival of new orders is considered, and a capacity constraint on the sorting installation should be respected. Furthermore, a new waiting strategy is studied in which order pickers can temporarily postpone certain orders, as combining them with possible future order arrivals may allow for more efficient overall picking performance. Finally, the performance of the current operating policy is compared with that of both a seed batching heuristic and our metaheuristic algorithm by use of an ANOVA analysis. The results indicate that the number of order pickers can be reduced by 12.5% if the new optimization algorithm is used, accompanied by an improvement in the offered customer service level.