2018/09/26 by Guang Yang, Yang, Guang, Cun Mu +1
Engineering · #Advanced Manufacturing and Logistics Optimization #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Optimization and Packing Problems #Vehicle Routing Optimization Methods
paper · pdf · doi:10.48550/arxiv.1809.10210
openalex publication_date 2018/09/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Having the right assortment of shipping boxes in the fulfillment warehouse to pack and ship customer's online orders is an indispensable and integral part of nowadays eCommerce business, as it will not only help maintain a profitable business but also create great experiences for customers. However, it is an extremely challenging operations task to strategically select the best combination of tens of box sizes from thousands of feasible ones to be responsible for hundreds of thousands of orders daily placed on millions of inventory products. In this paper, we present a machine learning approach to tackle the task by formulating the box design problem prescriptively as a generalized version of weighted k-medoids clustering problem, where the parameters are estimated through a variety of descriptive analytics. We test this machine learning approach on fulfillment data collected from Walmart U.S. eCommerce, and our approach is shown to be capable of improving the box utilization rate by more than 10%.