公平的当天交付
Same-Day Delivery with Fairness
摘要：
在过去几年，对当日送达（SDD）的需求迅速增长，尤其是在COVID-19疫情期间。快速增长并非没有挑战。2016年，由于会员集中度低，距离仓库较远，某些少数族裔社区被排除在亚马逊SDD服务之外，引发了对公平性的担忧。在本文中，我们研究的问题，提供公平的SDD服务的客户。服务区域被划分为不同的区域。在一天的过程中，客户请求SDD服务，并且请求的时间和交付地点事先不知道。调度员动态地分配车辆，以便在交付截止日期之前将货物交付给接受的客户。除了整体服务率（效用），我们最大化所有地区的最小区域服务率（公平性）。我们将问题建模为多目标马尔可夫决策过程，并开发了深度Q学习解决方案方法。我们引入了一种从费率到实际服务的新型学习转换，这创造了一个稳定而高效的学习过程。计算结果表明，我们的方法在减轻不公平的空间和时间在不同的客户地理位置的有效性。我们还表明，这种有效性是有效的与不同的仓库位置，为企业提供了一个机会，以实现更好的公平从任何位置。此外，我们认为忽略服务公平性的影响，结果表明，我们的政策最终优于公用事业驱动的基线时，客户对服务水平有很高的期望。
英文摘要：
The demand for same-day delivery (SDD) has increased rapidly in the last few years and has particularly boomed during the COVID-19 pandemic. The fast growth is not without its challenge. In 2016, due to low concentrations of memberships and far distance from the depot, certain minority neighborhoods were excluded from receiving Amazon's SDD service, raising concerns about fairness. In this paper, we study the problem of offering fair SDD-service to customers. The service area is partitioned into different regions. Over the course of a day, customers request for SDD service, and the timing of requests and delivery locations are not known in advance. The dispatcher dynamically assigns vehicles to make deliveries to accepted customers before their delivery deadline. In addition to the overall service rate (utility), we maximize the minimal regional service rate across all regions (fairness). We model the problem as a multi-objective Markov decision process and develop a deep Q-learning solution approach. We introduce a novel transformation of learning from rates to actual services, which creates a stable and efficient learning process. Computational results demonstrate the effectiveness of our approach in alleviating unfairness both spatially and temporally in different customer geographies. We also show this effectiveness is valid with different depot locations, providing businesses with an opportunity to achieve better fairness from any location. Further, we consider the impact of ignoring fairness in service, and results show that our policies eventually outperform the utility-driven baseline when customers have a high expectation on service level.
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