We study the problem of learning the preferences of drivers and planners in the context of last mile delivery. Given a data set containing historical decisions and delivery locations, the goal is to capture the implicit preferences of the decision-makers. We consider two ways to use the historical data: one is through a probability estimation method that learns transition probabilities between stops (or zones). This is a fast and accurate method, recently studied in a VRP setting. Furthermore, we explore the use of machine learning to infer how to best balance multiple objectives such as distance, probability and penalties. Specifically, we cast the learning problem as a structured output prediction problem, where training is done by repeatedly calling the TSP solver. Another important aspect we consider is that for last-mile delivery, every address is a potential client and hence the data is very sparse. Hence, we propose a two-stage approach that first learns preferences at the zone level in order to compute a zone routing; after which a penalty-based TSP computes the stop routing. Results show that the zone transition probability estimation performs well, and that the structured output prediction learning can improve the results further. We hence showcase a successful combination of both probability estimation and machine learning, all the while using standard TSP solvers, both during learning and to compute the final solution; this means the methodology is applicable to other, real-life, TSP variants, or proprietary solvers.
Preference-Aware Delivery Planning for Last-Mile Logistics
Singapore Management University（新加坡管理大学）
最后一公里物流配送路径优化是一个具有挑战性的问题，已经引起了许多研究者的关注。这些问题通常被建模和解决为具有挑战性的现实世界约束的车辆路径问题（VRP）的变体（例如，时间窗口、优先级）。然而，尽管在解决这些VRP实例方面进行了数十年的扎实研究，但我们仍然看到优化的路线与实践者实际偏好的路线之间存在显着差距。这些差距大部分是由于被优化的内容和实践者实际关心的内容之间的差异，在许多情况下很难准确定义。在本文中，我们提出了一种新的分层路由优化与学习参数，结合了优化和机器学习方法的优势。我们的分层路由器首先解决了一个区域级的旅行商问题与学习的权重上的各种区域级别的功能，与区域访问序列固定，然后我们解决了停止级的车辆路径问题作为最短的哈密尔顿路径问题。贝叶斯优化方法，然后引入，使我们能够调整的权重分配给不同的区域功能用于解决区域级旅行推销员问题。通过使用Amazon Last Mile Routing Research Challenge提供的真实交付数据集，我们展示了同时拥有优化和机器学习组件的重要性。我们还演示了如何使用路由相关的功能来识别我们可能有困难的情况。这为进一步研究我们如何解决这些困难的情况铺平了道路。
A Branch-and-Price Algorithm for Fast and Equitable Last-Mile Relief Aid Distribution
Department of Mathematics and Industrial Engineering, Polytechnique Montréal（蒙特利尔理工学院数学与工业工程系）；College of Engineering, Koç University（科克大学工程学院）；Department of Statistics and Operations Research, University of Vienna（维也纳大学统计与运筹学系）
LaDe: The First Comprehensive Last-mile Delivery Dataset from Industry
Cainiao Network（菜鸟网络）；Beijing Jiaotong University（北京交通大学）；National University of Singapore（新加坡国立大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
DeepLethe；Tsinghua University（清华大学）；Shanghai Jiao Tong University（上海交通大学）；Beihang University（北京航空航天大学）；Xiamen University（厦门大学）；Nanjing University（南京大学）；Beijing Jiaotong University（北京交通大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Tianjin University（天津大学）；Shanghai University of Finance and Economics（上海财经大学）；Harvard University（哈佛大学）；Stanford University（斯坦福大学）；University College London（伦敦大学学院）；Cornell University（康奈尔大学）；Huawei（华为）；Atlassian；Meta；Microsoft Research Asia（微软亚洲研究院）；UWits Technology (Beijing) Co., Ltd.（优维思科技（北京）有限公司）；Bopai Management Consulting (Shenzhen) Co., Ltd.（博湃管理咨询（深圳）有限公司）；Exploration and Development Research Institute, PetroChina Southwest Oil & Gasfield Company（中国石油西南油气田公司勘探开发研究院）；Kunlun Digital Techn（昆仑数字技术）
ResearchStudio-Reel: Automate the Last Mile of Research from Paper to Poster, Video, and Blog
Microsoft Research（微软研究院）；National University of Singapore（新加坡国立大学）；Nanyang Technological University（南洋理工大学）；Tsinghua University（清华大学）；Peking University（北京大学）；Shanghai Jiao Tong University（上海交通大学）；Westlake University（西湖大学）；CFAR, A*STAR（计算科学与工程研究所，新加坡科技研究局）
