The traditional Capacitated Vehicle Routing Problem (CVRP) minimizes the total distance of the routes under the capacity constraints of the vehicles. But more often, the objective involves multiple criteria including not only the total distance of the tour but also other factors such as travel costs, travel time, and fuel consumption.Moreover, in reality, there are numerous implicit preferences ingrained in the minds of the route planners and the drivers. Drivers, for instance, have familiarity with certain neighborhoods and knowledge of the state of roads, and often consider the best places for rest and lunch breaks. This knowledge is difficult to formulate and balance when operational routing decisions have to be made. This motivates us to learn the implicit preferences from past solutions and to incorporate these learned preferences in the optimization process. These preferences are in the form of arc probabilities, i.e., the more preferred a route is, the higher is the joint probability. The novelty of this work is the use of a neural network model to estimate the arc probabilities, which allows for additional features and automatic parameter estimation. This first requires identifying suitable features, neural architectures and loss functions, taking into account that there is typically few data available. We investigate the difference with a prior weighted Markov counting approach, and study the applicability of neural networks in this setting.
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。
PG$^2$Net: Personalized and Group Preferences Guided Network for Next Place Prediction
College of Information, Mechanical, and Electrical Engineering, Shanghai Normal University（上海师范大学信息与机电工程学院）；School of Information Science and Engineering, Shandong University（山东大学信息科学与工程学院）；Shanghai Municipal Country-rural Construction and Transportation Institute（上海市城乡建设和交通研究院）；AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院）
