智能蚁群算法解决公交区域调度问题研究

针对多条运营线路的公交区域调度问题，给出了人员调度问题的改进模型，模型的目标是在满足工作时间、跨度时间、换班要求等相关约束的条件下使人员完成任务的间隔时间最小。论文对已有蚁群算法解决车辆路径优化问题的算法进行了改进。对算法中相应的转移规则和轨迹更新规则进行了重新设定，改进了算法转移策略和信息素更新策略。给出了算法的实现步骤。通过仿真，对模型的正确性进行了验证。证明了改进蚁群算法解决公交调度问题的高效性和较强的适用性。

Intelligent Ant Colony Algorithm for Transit Scheduling Problem

In order to dealing with transit scheduling problem with several lines, an improved transit driver scheduling model was presented. Its objective was to schedule drivers in such a way that the deadheading was minimized while the operational constraints such as work time and spread time were satisfied. An ant colony algorithm (ACA) was presented to solve transit scheduling problem based on principle of ACA used to solve vehicle routing problem (VRP). Improvement on route construction rule and Pheromone updating rule was achieved on the basis of former algorithm. An example was analyzed to demonstrate the correctness of the application of this algorithm. It is proved that ACA is efficient and robust in solving transit scheduling problem.