报告人简介：

张锐是美国科罗拉多大学博尔德分校利兹商学院战略、创业和运营系的副教授。他是商业分析硕士项目的主任。在此之前，他曾担任运营博士项目的主任。此外，他还是INFORMS Journal on Computing和Networks的副主编。他的研究兴趣是定量方法，尤其是规范性分析方法。他的研究聚焦于收益管理问题、最后一英里配送和社交网络上的影响力最大化问题。他的工作发表在Operations Research, Manufacturing & Service Operations Management, INFORMS Journal on Computing, INFORMS Journal on Optimization, Naval Research Logistics, European Journal of Operational Research和Networks,等杂志上。此外，他还获得了多个最佳论文奖，作品集被选为2022年INFORMS Computing Society (ICS)奖的亚军。

报告简介：

Last-mile delivery is a critical component of logistics networks, accounting for approximately 30%–35% of costs. 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 povided 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.