A data-intensive analysis augmented simulation model of an order picking operation
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Order picking is the most labor-intensive function of distribution centers (DC) in the food and beverage store industry. An efficient order picking process supports this industry’s supply chain to move high volumes of products between the DC and the retail stores. This thesis focuses on the storage location assignment problem to deciding via an algorithm based on Association Rules Mining (ARM) the most adequate location of incoming products. The algorithm analyzes hundreds of orders received by the DC to find correlated products that are ordered frequently together by retail stores. The algorithm then assigns correlated products to storage locations that are close to each other in order to minimize order picking times. The results of computer simulation experiments using data from a real distribution center will be presented to evaluate the performance of the DC layout resulting from ARM.