EXPRESS: Enhancing Electric Vehicle Charging Station Design Using Multi-Fidelity Simulations

We study simulation-assisted service system design, where stochastic simulation is used to select the best design from a finite set of structural or parametric alternatives. Since high-fidelity simulation can be prohibitively time-consuming, we adopt a multi-fidelity approach that combines expensive high-fidelity runs with cheaper, coarser low-fidelity runs to estimate system performance and compare designs. This research is motivated by the design of an integrated electric vehicle (EV) fast-charging station. We formulate the design problem under the fixed-budget ranking and selection (R&S) framework, in which the simulation budget is allocated across fidelity levels and design alternatives to maximize the probability of correct selection (PCS) of the best design. We derive an asymptotic solution, develop a selection algorithm that satisfies the resulting optimality conditions, and establish its consistency and asymptotic optimality. We further demonstrate the algorithm’s empirical performance through an EV fast-charging station case study and a set of synthetic examples. These theoretical and empirical results provide actionable guidance on when and how multi-fidelity simulation can improve best-design selection in complex service system design problems.