Amazon Locker Capacity Management

• Published in: INFORMS journal on applied analytics 2024-03
• Main Authors: Sethuraman, Samyukta, Bansal, Ankur, Mardan, Setareh, Resende, Mauricio G. C., Jacobs, Timothy L.
• Format: Article
• Language: English
• ISSN: 2644-0865; 2644-0873
• DOI: 10.1287/inte.2023.0005

Amazon Locker is a self-service delivery or pickup location where customers can pick up packages and drop off returns. A basic first-come-first-served policy for accepting package delivery requests to lockers results in lockers becoming full with standard shipping speed (3- to 5-day shipping) packages, leaving no space for expedited packages, which are mostly next-day or two-day shipping. This paper proposes a solution to the problem of determining how much locker capacity to reserve for different ship-option packages. Yield management is a much-researched field with popular applications in the airline, car rental, and hotel industries. However, Amazon Locker poses a unique challenge in this field because the number of days a package will wait in a locker (package dwell time) is, in general, unknown. The proposed solution combines machine learning techniques to predict locker demand and package dwell time with linear programming to maximize throughput in lockers. The decision variables from this optimization provide optimal capacity reservation values for different ship options. This resulted in a year-over-year increase of 9% in Locker throughput worldwide during the holiday season of 2018, impacting millions of customers. History: This paper was refereed.