On-Time Last-Mile Delivery: Order Assignment with Travel-Time Predictors
研究如何利用配送数据提升最后一英里配送的准时性，提出一个整合行程时间预测与订单分配优化的框架，并通过真实数据实验验证其效果，强调从运营数据中学习驾驶员行为的重要性。
We study how delivery data can be applied to improve the on-time performance of last-mile delivery services. Motivated by the delivery operations and data of a food delivery service provider, we discuss a framework that integrates travel-time predictors with order-assignment optimization. Such integration enables us to capture the driver’s routing behavior in practice as the driver’s decision-making process is often unobservable or intricate to model. Focusing on the order-assignment problem as an example, we discuss the classes of tractable predictors and prediction models that are highly compatible with the existing stochastic and robust optimization tools. We further provide reformulations of the integrated models, which can be efficiently solved with the proposed branch-and-price algorithm. Moreover, we propose two simple heuristics for the multiperiod order-assignment problem, and they are built upon single-period solutions. Using the delivery data, our numerical experiments on a real-world application not only demonstrate the superior performance of our proposed order-assignment models with travel-time predictors, but also highlight the importance of learning behavioral aspects from operational data. We find that a large sample size does not necessarily compensate for the misspecification of the driver’s routing behavior. This paper was accepted by Hamid Nazerzadeh, big data analytics.