A simulation-based optimization approach for the recharging scheduling problem of electric buses
提出一种仿真优化方法，通过简化群优化算法和回溯机制最小化电动公交车充电等待时间，在台湾43个数据集上验证了优于其他充电规则和算法。
• A simulation–optimization approach for electric bus (EB) recharging scheduling is proposed. • Aim is to minimize waiting time for EB charging, enhancing operational efficiency. • The SSO algorithm and a backtracking mechanism for optimal recharging scheduling is utilized. • The proposed approach in both deterministic and stochastic environments performs better. 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.