  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 (3-5 day shipping) packages, and
leaving no space left 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 since 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, and 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 holiday season of 2018, impacting millions
of customers.
