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.

翻译：亚马逊储物柜是一种自助式配送/取件站点，客户可在此取包裹并办理退货。采用先到先得原则处理储物柜包裹配送请求时，标准配送速度（3-5天送达）的包裹常导致储物柜满载，导致无法为次日达或两日达的加急包裹预留空间。本文提出一种解决方案，用于确定不同配送选项包裹的储物柜容量预留量。收益管理虽在航空、租车及酒店行业已得到广泛研究，但亚马逊储物柜因包裹在柜中存放天数（即包裹驻留时间）通常未知而构成独特挑战。本研究提出的方案融合了机器学习技术以预测储物柜需求与包裹驻留时间，并采用线性规划以最大化储物柜吞吐量。该优化模型决策变量为不同配送选项提供了最优容量预留值。此方法使2018年假日季全球储物柜吞吐量同比增长9%，惠及数百万客户。