Amazon Locker Capacity Management

Samyukta Sethuraman (), Ankur Bansal (), Setareh Mardan (), Mauricio G. C. Resende () and Timothy L. Jacobs ()
Additional contact information
Samyukta Sethuraman: Sponsored Products Advertising, Amazon Ads, Palo Alto, California 94301
Ankur Bansal: Kotak Mahindra Bank, Gurugram, Haryana 122002, India
Setareh Mardan: Retail Pricing, Pricing Research, Amazon, Seattle, Washington 98109
Mauricio G. C. Resende: Industrial & Systems Engineering, University of Washington, Seattle, Washington 98195
Timothy L. Jacobs: ATS Science & Engineering, Amazon Transportation Services, Amazon, Bellevue, Washington 98004

Interfaces, 2024, vol. 54, issue 6, 455-470

Abstract: 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.

Keywords: logistics; capacity management; machine learning; linear programming (search for similar items in EconPapers)
Date: 2024
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