The impact of integrated cluster-based storage allocation on parts-to-picker warehouse performance

• Published in: Transportation research. Part E, Logistics and transportation review Logistics and transportation review, 2021-02, Vol.146, p.102207, Article 102207
• Main Authors: Mirzaei, Masoud, Zaerpour, Nima, de Koster, René
• Format: Article
• Language: English
• Subjects: Analytical and simulation modeling; Automated warehousing; Cluster-based storage; Correlated storage; Performance analysis; Product affinity; Storage policy
• ISSN: 1366-5545; 1878-5794
• DOI: 10.1016/j.tre.2020.102207

•We develop an integrated cluster-based product-to-storage allocation method.•Order picking travel time in parts-to-picker warehouses may be strongly reduced.•Reduction can already be achieved for fairly low levels of product correlation.•An integrated approach to cluster-based allocation outperforms a sequential one.•Benefits are limited for small order sizes and very skewed demand curve. 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.