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The timely transportation of goods to customers is an essential component ofeconomic activities. However, heavy-duty diesel trucks used for goods deliverysignificantly contribute to greenhouse gas emissions within many largemetropolitan areas, including Los Angeles, New York, and San Francisco. Toreduce GHG emissions by facilitating freight electrification, this paperproposes Joint Routing and Charging scheduling for electric trucks. Theobjective of the associated optimization problem is to minimize the cost oftransportation, charging, and tardiness. A large number of possiblecombinations of road segments as well as a large number of combinations ofcharging decisions and charging durations leads to a combinatorial explosion inthe possible decisions electric trucks can make. The resulting mixed-integerlinear programming problem is thus extremely challenging because of thecombinatorial complexity even in the deterministic case. Therefore, a SurrogateLevel-Based Lagrangian Relaxation (SLBLR) method is employed to decompose theoverall problem into significantly less complex truck subproblems. In thecoordination aspect, each truck subproblem is solved independently of othersubproblems based on the values of Lagrangian multipliers. In addition toserving as a means of guiding and coordinating trucks, multipliers can alsoserve as a basis for transparent and explanatory decision-making by trucks.Testing results demonstrate that even small instances cannot be solved usingthe 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 forsmall cases, and within 30 minutes for large ones. Furthermore, it has beendemonstrated that as battery capacity increases, the total cost decreasessignificantly; moreover, as the charging power increases, the number of trucksrequired decreases as well.

Toward Efficient Transportation Electrification of Heavy-Duty Trucks: Joint Scheduling of Truck Routing and Charging