The Driver-Aide Problem:Coordinated Logistics for Last-MileDelivery
Abstract: The Driver-Aide Problem:Coordinated Logistics for Last-MileDelivery
Last-mile delivery is a critical component of logistics networks, accounting for approximately 30%–35% of costs. As delivery volumes have increased, truck route times have become unsustainably long. To address this issue, many logistics companies, including FedEx and UPS, have resorted to using a “driver aide” to assist with deliveries. The aide can assist the driver in two ways. As a “jumper,” the aide works with the driver in preparing and delivering packages, thus reducing the service time at a given stop. As a “helper,” the aide can independently work at a location delivering packages, and the driver can leave to deliver packages at other locations and then return. Given a set of delivery locations, travel times, service times, jumper’s savings, and helper’s service times, the goal is to determine both the delivery route and the most effective way to use the aide (e.g., sometimes as a jumper and sometimes as a helper) to minimize the total routing time. We model this problem as an integer program with an exponential number of variables and an exponential number of constraints and propose a branch-cut-and-price approach for solving it. Our computational experiments are based on simulated instances built on real-world data provided by an industrial partner and a data set released by Amazon. The instances based on the Amazon data set show that this novel operation can lead to, on average, a 35.8% reduction in routing time and 22.0% in cost savings. More importantly, our results characterize the conditions under which this novel operation mode can lead to significant savings in terms of both the routing time and cost. Our computational results show that the driver aide with both jumper and helper modes is most effective when there are denser service regions and when the truck’s speed is higher (≥10 miles per hour). Coupled with an economic analysis, we come up with rules of thumb (that have close to 100% accuracy) to predict whether to use the aide and in which mode. Empirically, we find that the service delivery routes with greater than 50% of the time devoted to delivery (as opposed to driving) are the ones that provide the greatest benefit. These routes are characterized by a high density of delivery locations.
Speaker Bio
Rui Zhang is an associate professor in the Strategy, Entrepreneurship, and Operations division at Leeds School of Business, University of Colorado Boulder. He is the Faculty Director of the Master’s Program in Business Analytics. Before that, he served as the Director of the Ph.D. Program in Operations. Furthermore, he serves as Associate Editor for INFORMS Journal on Computing and Networks. His research interests are quantitative methods, especially prescriptive analytics techniques. His work focuses on revenue management problems, last-mile delivery, and influence maximization problems on social networks. His work has been published in Operations Research, Manufacturing & Service Operations Management, INFORMS Journal on Computing, INFORMS Journal on Optimization, Naval Research Logistics, European Journal of Operational Research, and Networks, among others. In addition, he has won several Best Paper awards. A collection of his work was selected as the runner-up for the 2022 INFORMS Computing Society (ICS) Prize.