Warehouses and warehouse-related operations have long been a field of interest for researchers. One of the areas that researchers focus on is the Storage Location Assigning Problem (SLAP or Slotting). The goal in this field is to find the best location in a warehouse to store the products. With the current COVID-19 pandemic, there is a shopping paradigm shift towards e-commerce, which even after the pandemic will not return to the old state. This paradigm shift raises the need for better performing multi-pick warehouses. In this paper, we propose a clustering method based on the gravity model. We show that for warehouses in which there is more than one pick per trip, our proposed method improves the performance.
Gravity Clustering: a Correlated Storage Location Assignment Problem Approach