讲座题目：The Driver-Aide Problem: Coordinated Logistics for Last-Mile Delivery（司机辅助问题：最后一英里配送的协调物流）

主讲人： 张锐   科罗拉多大学博尔德分校

时间：2024年6月11日10:00

地点：学院216

主办单位：武汉大学经济与管理学院管理科学与工程系

内容摘要：

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.

最后一英里配送是物流网络的关键组成部分，约占成本的30%-35%。随着配送量的增加，卡车行驶时间变得不可持续地长。为了解决这个问题，包括联邦快递和UPS在内的许多物流公司都采用了“司机助手”来协助送货。助手可以通过两种方式帮助司机。作为一名“跳跃者”，助手与司机一起准备和派送包裹，从而缩短了在指定站点的服务时间。作为“帮手”，助手可以在递送包裹的地点独立工作，司机可以离开去其他地点派送包裹，然后返回。给定一组配送地点、行驶时间、服务时间、跳跃节省的费用和帮手的服务时间，目标是确定交付路径和使用助手的最有效方式（例如，有时作为跳跃者，有时作为帮手），以最小化总路径时间。我们将这个问题建模为一个具有指数数量变量和指数数量约束的整数规划，并提出了一种分支-剪枝-定价方法来求解。我们的数值实验基于工业合作伙伴提供的真实数据和亚马逊发布的数据集的模拟实例。基于亚马逊数据集的实例表明，这种新颖的运营模式平均可以减少35.8%的路径时间和22.0%的成本。更重要的是，我们的结果描述了这种新的运营模式可以在路径时间和成本方面显著节省的条件。我们的计算结果表明，当服务区域密集且卡车速度较高（≥10英里/小时）时，具有跳跃者和帮手的模式最有效。再加上经济分析，我们得出了经验法则（准确率接近100%）来预测是否使用该助手以及以何种模式使用该助手。我们发现，50%以上的时间用于配送（而不是行驶）的服务配送路径是提供最大利益的路径。这些路径的特点是交货地点密度高。

主讲人简介：

张锐是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。他的作品集被选为2022年INFORMS Computing Society (ICS)奖的亚军。