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To remain competitive in the current e-commerce environment, warehouses are expected to handle customer orders as efficiently and quickly as possible. Previous research on order picking in a static context has shown that integrating batching, routing and scheduling decisions leads to better results than addressing these planning problems individually. In this study we propose an integrated solution approach that is able to deal with dynamic order arrivals, a problem often encountered in practice. Furthermore, we demonstrate the need to anticipate on future order arrivals to keep customer service levels high. We develop a new large neighbourhood search algorithm to solve the online, integrated batching, routing and scheduling problem. First, the algorithm is shown to outperform the current state-of-the-art static solution algorithm. Next, we develop an experimental design based on real-life data, to test the applicability of the model in different settings. The results of this experimental design are used to obtain insights on the particularity of this online, integrated problem. The effect of several real-life characteristics is demonstrated by using an ANOVA, leading to several managerial insights that may help companies to operate efficiently without jeopardising customer satisfaction.

Integrated scheduling of order picking operations under dynamic order arrivals