On-Time Last-Mile Delivery: Order Assignment with Travel-Time Predictors

Sheng Liu (), Long He () and Zuo-Jun Max Shen ()
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Sheng Liu: Rotman School of Management, University of Toronto, Toronto, Ontario M5S 3E6, Canada
Long He: NUS Business School, National University of Singapore, Singapore 119245
Zuo-Jun Max Shen: Department of Industrial Engineering and Operations Research and Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, California 94720

Management Science, 2021, vol. 67, issue 7, 4095-4119

Abstract: 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.

Keywords: last-mile delivery; routing behavior; prediction; robust optimization; branch and price (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (12)

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