为了解决环线电动公交车辆和司机任务分配不均衡的问题，提出了一种联合优化调度模型。该模型主要通过顺、逆时针方向互相调配车辆和司机来提高整体利用率。在给定环形线路和人车不固定的情况下，综合考虑车辆行驶里程、工作量、充电桩数量、车辆充电时间、司机工作时间、休息时间等约束，以公交企业总运营成本最小化和时刻表总调整量最小化为目标制定有序充电管理计划和车辆、司机调度方案。在求解方面，首先将混合整数非线性规划模型通过线性转换转化成线性规划模型，并使用CPLEX求解器得到调度方案；其次采用多目标粒子群算法（MOPSO）和基于\epsilon约束处理机制的改进多目标粒子群算法（\epsilon-MOPSO）分别求解调度方案，并通过网格法确保外部档案集的收敛性和均匀性。最后以北京市环线公交200路（内、外环）为例进行验证，对比分析CPLEX求解器、传统多目标粒子群算法以及提出的基于\epsilon约束处理机制的改进多目标粒子群算法的计算结果。研究证实了改进算法的有效性，且优化后的调度方案分别将车辆数从28降低到23，共减少17.86%；司机数从28降低到25，共减少10.71%。车队规模和司机数量减少，从而降低了企业总运营成本；时刻表平均每个发车时刻调整4.13 min，发车更均匀保证了乘客的需求。由此提升了公共交通的运营效率，具有重要的现实意义。

To address the issue of unbalanced task distribution between electric bus vehicles and drivers in loop line, this study proposed a joint optimal scheduling model, which mainly improves the overall utilization rate by adjusting vehicles and drivers in clockwise and counterclockwise directions. Given a fixed loop route and non-fixed vehicle-driver assignments, the model considers various constraints such as vehicle mileage, workload, number of charging stations, charging duration, driver working and rest times. It aims to minimize both the total operating cost of the transit enterprise and the total timetable adjustment, while formulating an orderly charging management plan and scheduling strategy for vehicles and drivers. In the aspect of solution, the mixed integer nonlinear programming model was transformed into linear programming model by linear transformation, and the scheduling scheme was obtained by using CPLEX solver. Additionally, a multi-objective particle swarm algorithm (MOPSO) and improved multi-objective particle swarm algorithm (ε-MOPSO) based on constraint processing mechanism were used to solve the scheduling scheme respectively, and the convergence and uniformity of external file set were ensured by grid method. The proposed approach is validated through a case study on Beijing’s Route 200 (inner and outer loop lines). A comparative analysis of the results obtained from the CPLEX solver, the traditional MOPSO, and the improved ε-MOPSO confirms the effectiveness of the improved algorithm.The optimized scheduling plan reduces the number of vehicles from 28 to 23 (a 17.86% reduction) and the number of drivers from 28 to 25 (a 10.71% reduction), thereby lowering the total operating cost. The timetable adjustments average 4.13 minutes per departure, resulting in more evenly spaced departures and better meeting passenger demand. This significantly enhances the operational efficiency of public transportation and holds substantial practical significance.