Abstract
Order picking is one of the most demanding activities in many warehouses in terms of capital and labor. In parts-to-picker systems, automated vehicles or cranes bring the parts to a human picker. The storage assignment policy, the assignment of products to the storage locations, influences order picking efficiency. Commonly used storage assignment policies, such as full turnover-based and class-based storage, only consider the frequency at which each product has been requested but ignore information on the frequency at which products are ordered jointly, known as product affinity. Warehouses can use product affinity to make informed decisions and assign multiple correlated products to the same inventory “pod” to reduce retrieval time. Existing affinity-based assignments sequentially cluster products with high affinity and assign the clusters to storage locations. We propose an integrated cluster allocation (ICA) policy to minimize the retrieval time of parts-to-picker systems based on both product turnover and affinity obtained from historical customer orders. We formulate a mathematical model that can solve small instances and develop a greedy construction heuristic for solving large instances. The ICA storage policy can reduce total retrieval time by up to 40% compared to full turnover-based storage and class-based policies. The model is validated using a real warehouse dataset and tested against uncertainties in customer demand and for different travel time models.
Highlights
Warehouses decouple supply from demand in supply chains
The results show that the integrated cluster allocation (ICA) policy leads to higher savings when affinity, order size, assortment size, and cluster size increase
We compare the order retrieval time of the ICA policy with the commonly used available storage locations. The class-based (ABC) and full turnover-based (FTB) policies, while taking into account the space-sharing effect of the class-based storage
Summary
Warehouses decouple supply from demand in supply chains. To increase the efficiency of warehouse operations, we focus on the order picking process. To turnover speed and storage space needed, affinity is another important attribute of the products, which can be derived from order history and/or demand fore casts If this attribute is ignored in the assignment, two products that are frequently requested together (e.g. peanut butter and jelly) might be assigned to storage locations far from each other, which can unnecessarily increase order picking time. By considering the affinity between the two products, it is possible to cluster them in several groups based on this correlation These clusters can be allocated to the storage locations to reduce order picking time. This paper proposes an integrated cluster allocation (ICA) storage assignment policy for parts-to-picker systems that minimizes the total order retrieval time by considering both product turnover and affinity concurrently.
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