With an increasing demand from emerging logistics businesses, Vehicle Routing Problem with Private fleet and common Carrier (VRPPC) has been introduced to manage package delivery services from a supplier to customers. However, almost all of existing studies focus on the deterministic problem that assumes all parameters are known perfectly at the time when the planning and routing decisions are made. In reality, some parameters are random and unknown. Therefore, in this paper, we consider VRPPC with hard time windows and random demand, called Optimal Delivery Planning (ODP). The proposed ODP aims to minimize the total package delivery cost while meeting the customer time window constraints. We use stochastic integer programming to formulate the optimization problem incorporating the customer demand uncertainty. Moreover, we evaluate the performance of the ODP using test data from benchmark dataset and from actual Singapore road map.
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。
Deep Reinforcement Learning for Crowdsourced Urban Delivery: System States Characterization, Heuristics-guided Action Choice, and Rule-Interposing Integration
