Bus vehicle scheduling is very vital for bus companies to reduce operation cost and guarantee quality of service. Many big cities face the problem of traffic congestion, which leads to the planed vehicle scheduling scheme becoming infeasible. It is significant to study bus vehicle scheduling approaches under uncertain environments, such as traffic congestion. In this paper, a bus vehicle scheduling approach is proposed to handle the traffic congestion. It consists of three phases: firstly, a set of candidate vehicle blocks is generated once traffic congestion happens. Secondly, a non-dominated sorting genetic algorithm is adopted to select a subset of vehicle blocks from the set of candidate blocks to generate a set of Non-dominated solutions. Finally, a departure time adjustment procedure is applied to the Non-dominated solutions to further improve the quality of solutions. Experiments on a real-world bus line show that the proposed approach is able to dynamically generate scheduling schemes and significantly improve the quality of service compared to the comparative approaches.

公交车辆调度对于公交公司降低运营成本和保证服务质量至关重要。许多大城市面临交通拥堵的问题，导致计划的车辆调度方案变得不可行。研究在不确定的环境（例如交通拥堵）下的公交车辆调度方法具有重要意义。本文提出了一种公交车辆调度方法来处理交通拥堵。它包括三个阶段：首先，一旦交通拥堵发生，就会生成一组候选车辆区块。其次，采用非支配排序遗传算法从候选块集中选择车辆子集的子集，以生成一组非支配解。最后，对非主导解决方案应用出发时间调整程序，以进一步提高解决方案的质量。在现实世界的公交线路上进行的实验表明，与比较方法相比，该方法能够动态生成调度方案并显着提高服务质量。