A Multiobjective Genetic Algorithm Based Dynamic Bus Vehicle Scheduling Approach

Hongyi Shi, Chunlu Wang, Xingquan Zuo, Xinchao Zhao

Abstract: Bus vehicle scheduling is very vital for bus companies to reduce operation cost and guarantee quality of service. Urban roads are easily blocked due to bad weather, such that it is significant to study the bus vehicle scheduling problem under traffic congestion caused by bad weather. In this paper, a dynamic bus vehicle scheduling approach is proposed, which consists of two parts: (1) generate a set of candidate vehicle blocks once the road is blocked; (2) adopt the non-dominated sorting genetic algorithm combined with a departure time adjusting process to select a subset of vehicle blocks from the candidate blocks set to form a vehicle scheduling scheme. Experiments show that our approach can significantly improve quality of service compared to the manual vehicle scheduling scheme.