面向最后一英里电动汽车物流的带时间窗的双准则Steiner旅行商问题
A Bi-criterion Steiner Traveling Salesperson Problem with Time Windows for Last-Mile Electric Vehicle Logistics
AI总结：
本文研究最后一英里电动货运车辆的节能与安全路径规划，将其建模为带时间窗的双准则Steiner旅行商问题（BSTSPTW），以能耗和交叉口左转次数为目标并考虑再生制动，通过精确模型与局部搜索启发式算法在合理时间内生成近优解。
摘要：
本文探讨最后一英里电动货运车辆的节能与安全路径规划问题。随着交通行业环境足迹的增加和电子商务的普及，货运公司有望从最优的、满足时间窗的路线中获益，这些路线能最小化能源消耗，同时减少交叉口的交通冲突，从而提高安全性。我们将此问题建模为带时间窗的双准则Steiner旅行商问题（BSTSPTW），以消耗的能源和交叉口的左转次数作为两个目标，同时考虑再生制动能力。我们首先讨论了一个基于标量化的精确混合整数规划模型，用于枚举小规模实例效率前沿上的点。对于更大规模的网络，我们开发了一种高效的基于局部搜索的启发式算法，该算法使用多个算子来强化和多样化搜索过程。我们使用基准数据和来自美国Austin的Amazon配送路线的真实实例展示了所提方法的实用性。与最先进求解器的比较表明，我们的启发式算法能在合理的时间预算内生成近优解，在满足实际配送约束的同时有效平衡了能源效率与安全性。
英文摘要：
This paper addresses the problem of energy-efficient and safe routing of last-mile electric freight vehicles. With the rising environmental footprint of the transportation sector and the growing popularity of E-Commerce, freight companies are likely to benefit from optimal time-window-feasible tours that minimize energy usage while reducing traffic conflicts at intersections and thereby improving safety. We formulate this problem as a Bi-criterion Steiner Traveling Salesperson Problem with Time Windows (BSTSPTW) with energy consumed and the number of left turns at intersections as the two objectives while also considering regenerative braking capabilities. We first discuss an exact mixed-integer programming model with scalarization to enumerate points on the efficiency frontier for small instances. For larger networks, we develop an efficient local search-based heuristic, which uses several operators to intensify and diversify the search process. We demonstrate the utility of the proposed methods using benchmark data and real-world instances from Amazon delivery routes in Austin, US. Comparisons with state-of-the-art solvers shows that our heuristics can generate near-optimal solutions within reasonable time budgets, effectively balancing energy efficiency and safety under practical delivery constraints.
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