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 (GHG) 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 (JRC) 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 (MILP) 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 min 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.

将货物及时运输给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城市地区（包括洛杉矶、纽约和旧金山）显着增加了温室气体（GHG）排放。为了通过促进货运电气化来减少温室气体排放，本文提出了电动卡车的联合路由和充电（JRC）调度。相关优化问题的目标是最小化运输、充电和迟到成本。路段的大量可能组合以及充电决策和充电持续时间的大量组合导致电动卡车可以做出的可能决策的组合爆炸。因此，即使在确定性情况下，由于组合复杂性，所产生的混合整数线性规划（MILP）问题也极具挑战性。因此，采用基于代理水平的拉格朗日松弛（SLBLR）方法将整个问题分解为复杂度明显较低的卡车子问题。在协调方面，每个卡车子问题都根据拉格朗日乘子的值独立于其他子问题进行求解。除了作为引导和协调卡车的手段之外，乘数还可以作为卡车透明和解释性决策的基础。测试结果表明，即使是小实例，经过几天的求解也无法使用现成的求解器 CPLEX 进行求解。另一方面，SLBLR 方法对于小情况可以在几分钟内获得接近最优的解决方案，对于大情况可以在 30 分钟内获得接近最优的解决方案。此外，事实证明，随着电池容量的增加，总成本显着降低；此外，随着充电功率的增加，所需的卡车数量也会减少。