Adaptive Storage Reassignment in Order Picking Systems to Picker Learning and Change of Demand

Abstract

In today’s logistical environment, companies must quickly deliver many kinds of products to customers, owing to intensifying competition, shortening product lifecycle. Mid-supply-chain logistical warehouses play an important role of adjusting the difference between manufacturer supply and customer demand. At the warehouses, most of the work comprises picking. Thus, it is imperative to make the picking process more efficient. The working time for picking is determined by a combination of elements, such as warehouse layout, storage assignment, order batching, and routing. And, adaptive storage assignments to change of demand have been effective. In practice, if the storage assignment changes, the picker will have a learning curve negatively affecting picking time. A model handling such effects has already been proposed. However, it is assumed all of the picker learning is reset. This is impractical. Our study proposes a new model in which the picker only relearns the shelf for which a storage assignment has been changed. Furthermore, pickers’ learning grows with the number of storage-shelf accesses, the degree of picker’s learning affect the probability of picking errors and search time. Our study specifies the effectiveness of the storage-shelf learning method for handling adaptive storage assignments.