Robust crossline and charging scheduling optimization for electric bus

Electric bus cross-line scheduling is highly vulnerable to travel time uncertainty, which can disrupt interline connections, miss charging windows, and trigger cascading service failures. To address this critical challenge, we propose a two-stage robust optimization model that jointly optimizes cross-line vehicle scheduling and charging planning while explicitly promoting equity in fleet workload and charging infrastructure utilization. We develop a novel hybrid exact solution framework integrating the ε-constraint method to generate Pareto-optimal trade-offs among equity metrics, and an improved column-and-constraint generation algorithm to identify worst-case scenarios via random sampling and cutting-plane constraints. Validated using comprehensive real-world data from five bus lines in Panjin, China, the proposed approach reduces required fleet size by 5.71% and total operating costs by 4.25% compared to conventional single-line scheduling, while significantly improving operational and charging equity. These results demonstrate the model’s potential to enhance the robustness, efficiency, and fairness of electric bus operations in uncertain environments.