A simulation-based optimization approach for the recharging scheduling problem of electric buses

This study proposes a simulation-based optimization approach to address the recharging scheduling problem of electric buses to minimize charging waiting time. Poor scheduling could lead to longer waiting times and potentially affect operation schedules regarding time and service quality. This study addresses a simulation-based optimization framework to evaluate various performance metrics during electric bus service, including waiting times, charging costs, and the utilization of charging piles. In this study, we propose a hybrid approach, simplified swarm optimization (SSO), which is an evolutionary algorithm with a backtracking (BT) mechanism and dynamic charging in a simulation framework. Based on the dynamic charging, SSO is used to determine the additional charging in terms of battery capacities, and a BT mechanism is employed to enhance algorithm efficiency and achieve breakthroughs in solution quality. A case study from Taiwan with 43 generated datasets was conducted in deterministic and stochastic situations to compare the effectiveness and efficiency among three charging rules (i.e., full charging rule, flexible charging rule, dynamic charging rule) and two algorithms (i.e., particle swarm optimization and SSO) The results indicate the superior performance in all scenarios by using a statistical test, which offers effective decision support for bus operators’ electric bus recharging scheduling.