An Explicit Model for Optimal Siting and Sizing of Electric Truck Charging Stations

The deployment of electric trucks is recognized as a crucial tool for reducing dependence on traditional fossil fuels and mitigating pollution from transportation systems. However, insufficient and unbalanced distribution of charging stations may hinder the use of electric trucks. This study develops an explicit mixed-integer linear program to optimize the siting and sizing of charging stations for electric trucks in general transport networks. The model incorporates the queuing dynamics of electric trucks at charging stations through a formulated set of first-come-first-served constraints, enabling the direct computation of the charging waiting time for each truck. The objective function minimizes the total system cost, comprising the charging station construction cost, the electric truck procurement cost, the electricity consumption cost, and the operational cost, consisting of travel times, queuing times, and the delay penalties of the trucks. To address the computational challenges in solving large-scale network problems, we propose a hybrid solution strategy combining a rolling horizon framework with a genetic algorithm, which enhances computational efficiency through problem decomposition and iterative optimization. Finally, numerical experiments are conducted on three road networks, including the Sioux Falls network and the Chicago network, to validate the effectiveness of the proposed model and algorithm.