An UtilityBased Storage Assignment Strategy for eCommerce Warehouse Management Order picking is one of the most labor intensive and time consuming parts of ecommerce warehouse order management. An effective solution to the item storage assignment problem can significantly improve the efficiency of warehouse operations and improve customer service levels. The problem, however, is quite challenging due to distinct item characteristics (volume, velocity, variety), multiple item relationships (cooccurrence patterns, complementarity, compatibility and substitution effects), layout configurations (single vs multiblock, pure vs mixedshelves, dedicated vs scattered storage), orderpicking strategies (standalone, batched, zoned) and routing strategies (traversal, return, midpoint) and is NP hard. Several heuristic approaches have been proposed in the literature to address the storage assignment problem. More recent and stateoftheart approaches to the problem have considered item relationships to improve the efficiency of order picking. We argue that such methods are largely myopic in nature and consider just pairwise item correlations and also ignore quantities of items in an order. Moreover, current methods make several simplifying assumptions about layout configurations, orderpicking and routing strategies. In this paper, we propose a novel utilitybased storage assignment strategy for the item storage assignment problem. It uses a topk high utility itemset mining method and a heuristic algorithm for effective storage assignment. We conduct rigorous experimental evaluation of the proposed idea and compare it against other related methods. The experimental results on wide variety of layout configurations, routing strategies and order distributions show very promising results for the proposed storage assignment strategy. The findings of this study are likely to be useful for warehouse managers in improving operational efficiencies and delivering better customer service levels.

Operational workload balancing in manual order picking Abstract   A growing ecommerce market and increasing customer requirements put extra pressure on order picking operations. Collecting large quantities of relative small orders within limited time windows makes workload balancing in order picking a challenging and complicated task. Therefore, warehouse managers experience difficulties in balancing the daily workload of order pickers in every pick zone. This paper introduces the operational workload balancing problem within the domain of order picking. A mathematical model is introduced to describe the new order picking planning problem. Furthermore, an iterated local search algorithm is developed to solve the operational workload balancing problem efficiently and effectively. The benefits of daily workload balancing in order picking operations are analysed by means of a reallife case.