协同优化电动车配送路线与充电，显著降低充电成本。
CARGO: A Co-Optimization Framework for EV Charging and Routing in Goods Delivery Logistics
- 联合优化配送路径与充电计划，兼顾时间窗约束。
- 相比基准策略，充电成本降低最多达39%。
- 适合城市物流、电动货运等场景的高效调度。
随着对可持续物流的关注增加，基于电动汽车（EV）的配送为城市配送提供了有前景的替代方案。然而，由于电池容量有限，电动车需要仔细规划充电策略，这取决于充电点（CP）的可用性、成本、距离以及车辆的当前电量（SoC）。我们提出 CARGO 框架，解决在时间窗内进行电动车配送的路线规划问题（EDRP），该问题联合优化配送路线与充电策略。在证明该问题为 NP-hard 后，我们提出了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。利用真实世界数据集，我们通过将启发式方法与 MILP 解进行比较，并与基线策略（最早截止时间优先，EDF；最近配送优先，NDF）进行对比评估。结果表明，相较于 EDF 和 NDF，充电成本分别降低最多达 39% 和 22%，同时完成相当数量的配送任务。
原文摘要 · Abstract (English)
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.
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