标题： CARGO：用于货物配送物流中电动汽车充电和路径规划的协同优化框架 显示英文标题

标题： CARGO: A Co-Optimization Framework for EV Charging and Routing in Goods Delivery Logistics

摘要： 随着对可持续物流日益增长的兴趣，基于电动汽车（EV）的配送为城市配送提供了一个有前景的替代方案。 然而，由于电动汽车的电池容量有限，需要仔细规划充电。 这取决于诸如充电点（CP）的可用性、成本、距离以及车辆的电量（SoC）等因素。 我们提出了CARGO，一个解决基于电动汽车的配送路线规划问题（EDRP）的框架，该框架联合优化配送时间窗口内的路线规划和充电。 在证明了该问题的NP难性之后，我们提出了一种基于混合整数线性规划（MILP）的精确解法和一种计算高效的启发式方法。 使用真实世界的数据集，我们通过将启发式方法与MILP解法进行比较，并将其与基准策略最早截止时间优先（EDF）和最近配送优先（NDF）进行对比，评估了我们的方法。 结果表明，与EDF和NDF相比，充电成本分别减少了最多39%和22%，同时完成了相当数量的配送。 显示英文摘要

摘要： With growing interest in sustainable logistics, electric vehicle (EV)-based deliveries offer a promising alternative for urban distribution. However, EVs face challenges due to their limited battery capacity, requiring careful planning for recharging. This depends on factors such as the charging point (CP) availability, cost, proximity, and vehicles' state of charge (SoC). We propose CARGO, a framework addressing the EV-based delivery route planning problem (EDRP), which jointly optimizes route planning and charging for deliveries within time windows. After proving the problem's NP-hardness, we propose a mixed integer linear programming (MILP)-based exact solution and a computationally efficient heuristic method. Using real-world datasets, we evaluate our methods by comparing the heuristic to the MILP solution, and benchmarking it against baseline strategies, Earliest Deadline First (EDF) and Nearest Delivery First (NDF). The results show up to 39% and 22% reductions in the charging cost over EDF and NDF, respectively, while completing comparable deliveries.