Efficient Simulation-Assisted Service System Design

The problem of service system design aims to determine the optimal design for the structure and parameters of the service system among a set of competitive alternatives. In practice, service systems can be highly complex and large-scale, which sometimes renders analytical approaches not applicable. As a result, stochastic simulation has emerged as a popular choice for the design of service systems. In this research, we consider simulation-based service system design, and focus on the application to the design of electric vehicle charging stations. This problem is formulated as the ranking and selection (R&S) model in simulation optimization, and multi-fidelity simulation is adopted to further improve the efficiency of finding the best system design. We derive the asymptotic optimal sample allocation rule that determines the number of samples allocated to each design-fidelity pair, and develop a selection algorithm for implementation. Numerical results demonstrate that the proposed algorithm has superior performance compared to state-of-the-art competitors.