亚马逊储物柜容量管理

Samyukta Sethuraman, Ankur Bansal, Setareh Mardan, Mauricio G. C. Resende, Timothy L. Jacobs
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引用次数: 0

摘要

亚马逊储物柜是一个自助式送货或取货地点,客户可以在此取货或退货。储物柜接受包裹递送请求的基本政策是先到先得,这导致储物柜被标准运输速度(3 至 5 天运输)的包裹占满,没有空间留给加急包裹,而加急包裹大多是次日达或两日达。本文提出了一种解决方案,以解决确定为不同运输选择的包裹预留多少储物柜容量的问题。收益管理是一个备受研究的领域,在航空、汽车租赁和酒店行业都有广泛应用。然而,亚马逊储物柜给这一领域带来了独特的挑战,因为包裹在储物柜中等待的天数(包裹停留时间)通常是未知的。所提出的解决方案将预测储物柜需求和包裹停留时间的机器学习技术与线性规划相结合,以实现储物柜吞吐量的最大化。该优化方案的决策变量可为不同的船舶选项提供最优的容量预留值。这使得 2018 年假期期间全球储物柜吞吐量同比增长 9%,影响了数百万客户。历史:本文已通过评审。
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Amazon Locker Capacity Management
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.
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