华南理工大学学报（自然科学版）2025，Vol.53Issue(6)：91-103,13.DOI:10.12141/j.issn.1000-565X.240440
环线电动公交车辆调度与司机排班的联合优化
Joint Optimization of Loop Line Electric Bus Vehicle Scheduling and Driver Scheduling
摘要
Abstract
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.关键词
城市交通/联合调度/多目标粒子群算法/环线电动公交/ε约束处理Key words
urban traffic/joint scheduling/multi-objective particle swarm algorithm/loop line electric bus/ε constraint processing分类
交通工程引用本文复制引用
胡宝雨,齐月,贾佃精,程国柱..环线电动公交车辆调度与司机排班的联合优化[J].华南理工大学学报（自然科学版）,2025,53(6):91-103,13.基金项目
中国博士后科学基金项目(2023M740558) （2023M740558）
黑龙江省自然科学基金项目(YQ2022E003) （YQ2022E003）
东北林业大学中央高校基本科研业务费专项资金资助项目(2572023CT21-04)Supported by the China Postdoctoral Science Foundation(2023M740558)and the Natural Science Foundation of Heilongjiang Province(YQ2022E003) （2572023CT21-04）
