摘要
为了提高公共自行车的使用效率和用户满意度水平,保证国内公共交通服务的合理运行与发展,根据公共自行车用车峰时和谷时的不同调度目标,建立两时期车辆调度模型。用车谷时以调度车路径最短为优化目标,用车峰时以用户满意度最高为优化目标。融合遗传算法(Genetic Algorithm, GA)和蚁群算法(Ant Colony System, ACS),形成遗传混合蚁群算法(Genetic HybridAnt Colony System Algorithm, GA-ACS),并将融合后的算法应用于调度模型中,以提升获得优化的车辆调度方案的求解速度和质量。群智能算法在不同数据集上的性能比较结果表明,与传统蚁群算法相比,遗传混合蚁群算法在求解速度和求解质量上都有更好的表现,在较短的时间内至少可以缩短10%的调度路程,因此该算法模型可以用于解决实际的公共自行车调度问题。
To improve the service efficiency and customer satisfaction degree of public bicycle and ensure the rational operation and development of domestic public transportation services, two-stage bicycle scheduling model was established. The aim of this model was to get the shortest length of the bicycle carrier during valley period and to maximize customer satisfaction degree during peak period. GA (Genetic Algorithm) and ACS (Ant Colony System) were merged together, which was called Genetic Hybrid Ant Colony System Algorithm (GA-ACS). The merged algorithm was applied to the scheduling model to improve the solving speed and quality of bicycle scheduling solution. Through comparing the performance of different algorithms on different datasets, the results indicate that GA-ACS has better performance in solving speed and quality than ACS, and it shortens at least 10% of the scheduling distance in a short period. It is concluded that GA-ACS could be used to solve practical public bicycle scheduling problem.
作者
崔元洋
邢晓红
陈思尧
文凯
秦显辉
娄勃凯
CUI Yuan-yang;XING Xiao-hong;CHEN Si-yao;WEN Kai;QIN Xian-hui;LOU Bo-kai(College of Mechanical and Electrical Engineering and Automation, Nanhang Jincheng College, Nanjing 211156, China;College of Air Transport and Engineering, Nanhang Jincheng College, Nanjing 211156, China)
出处
《交通运输研究》
2019年第2期17-27,共11页
Transport Research
基金
江苏省高等学校大学生实践创新训练计划(201813655017X)
关键词
智能交通
公共自行车
车辆调度优化
遗传混合蚁群算法
运输调度模型
服务质量
intelligent transportation
public bicycle
bicycle scheduling optimization
GA-ACS (Genetic Hybrid Ant Colony System)
transportation scheduling model
service quality
