The timely transportation of goods to customers is an essential component of economic activities. However, heavy-duty diesel trucks used for goods delivery significantly contribute to greenhouse gas emissions within many large metropolitan areas, including Los Angeles, New York, and San Francisco. To reduce GHG emissions by facilitating freight electrification, this paper proposes Joint Routing and Charging scheduling for electric trucks. The objective of the associated optimization problem is to minimize the cost of transportation, charging, and tardiness. A large number of possible combinations of road segments as well as a large number of combinations of charging decisions and charging durations leads to a combinatorial explosion in the possible decisions electric trucks can make. The resulting mixed-integer linear programming problem is thus extremely challenging because of the combinatorial complexity even in the deterministic case. Therefore, a Surrogate Level-Based Lagrangian Relaxation (SLBLR) method is employed to decompose the overall problem into significantly less complex truck subproblems. In the coordination aspect, each truck subproblem is solved independently of other subproblems based on the values of Lagrangian multipliers. In addition to serving as a means of guiding and coordinating trucks, multipliers can also serve as a basis for transparent and explanatory decision-making by trucks. Testing results demonstrate that even small instances cannot be solved using the off-the-shelf solver CPLEX after several days of solving. The SLBLR method, on the other hand, can obtain near-optimal solutions within a few minutes for small cases, and within 30 minutes for large ones. Furthermore, it has been demonstrated that as battery capacity increases, the total cost decreases significantly; moreover, as the charging power increases, the number of trucks required decreases as well.

翻译：货物及时送达客户是经济活动的重要组成部分。然而，用于货物运输的重型柴油卡车在许多大都市地区（包括洛杉矶、纽约和旧金山）显著加剧了温室气体排放。为通过货运电动化减少温室气体排放，本文提出针对电动卡车的路径与充电联合调度方法。相关优化问题的目标是最小化运输成本、充电成本及延迟成本。大量道路段组合与充电决策和充电时长的组合导致电动卡车可能做出的决策出现组合爆炸。即使在确定性情况下，由此产生的混合整数线性规划问题也因组合复杂性而极具挑战性。因此，采用基于代理层级的拉格朗日松弛（SLBLR）方法将整体问题分解为复杂度显著降低的卡车子问题。在协调层面，每个卡车子问题基于拉格朗日乘子的值独立于其他子问题求解。拉格朗日乘子除了作为引导和协调卡车的手段外，还可作为支持卡车透明且可解释决策的基础。测试结果表明，即使小规模实例也无法在数日求解内使用现成求解器CPLEX解决。而SLBLR方法能在几分钟内为小规模案例获得近优解，大规模案例则可在30分钟内完成。此外，研究表明电池容量增加时总成本显著下降；同时，充电功率提升时所需卡车数量也会减少。