## 1 Introduction
[11] compared a variety of hybrid heuristic methods for vehicle scheduling, finding that the heuristic method based on column generation had the best solution quality yet with a long solution time, while the heuristic method based on large-scale neighborhood search was fast and of good quality. Among the soft computing method applied to the vehicle scheduling, VAMPIRES [12, 13] was one of the most successful early heuristic methods, having the similar main ideas to 2-opt algorithms. It had successfully solved hundreds of actual public transport vehicle scheduling problems, and later was replaced by the the BOOST object-oriented software system. [PERSON] and [PERSON] [14] reported the application of the system in a British bus company. In the past ten years, more researches focused on the realistic constraints or characteristics, resulting in the emergence of a series of hyper-heuristic and hybrid approaches. For example, [PERSON] [15] developed a vehicle scheduling method based on the tabu search by applying the 2-opt algorithm. [PERSON] et al. [16] proposed six types of meta-heuristic methods to solve vehicle scheduling problems with multiple vehicle types and continuous driving time constraints. Since the 1990s, the focus of the problem research has been on solving the exact solution of the problem. Some scholars had proposed the precise branch-bound method and the precise column generation method. In order to reduce the scale of the problem, the time-space network was introduced into the multi-yard vehicle operating organization. [PERSON] et al. [17] applied the concept of the time-space network to the multi-station vehicle operating organization problem for the first time and explained the method of cutting empty-drive arcs in the network. In addition, it introduced the method and steps of reducing the network scale, proposed the multi-commodity network flow model of the multi-type multi-yard bus driving plan scheduling problem based on the time-space network, and solved an example problem with a large-scale vehicle operation organization by using the standard mathematical optimization software CPLEX. [PERSON] et al. [18] provided a multi-commodity network flow model based on the time-space network and proposed a stochastic programming algorithm to solve the model. It was of great significance to simplify the problem, establish the model and solve the problem. [PERSON] et al. [19] analyzed the connotation of bus regional dispatching and distribution planning problem, constructed a model of bus regional dispatching and distribution planning based on the space-time network, and verified the model and algorithm through an example. [PERSON] et al. [20] transformed the planning problem of the electric bus into a directed network. [PERSON] et al. [21] and others put forward the idea of two-stage heuristics to solve the problem of multi-station traffic planning for the first time. [PERSON] et al. [22] took the minimum number of vehicles required as the optimization objective and used the heuristic algorithm to solve the problem in stages. [PERSON], [PERSON] et al. [23] considered decision variables describing the connection between the vehicles and assigning vehicles to each station. [PERSON] [24] solved the problem of the multi-station traffic planning based on the iterative local search algorithm, and analyzed 30 cases. At present, the research on the bus driving region dispatching is not very mature. [PERSON] et al. [25] established a mixed integer programming model with time windows, aiming at the minimum number of vehicles, vehicle waiting time and empty driving time, taking into account factors such as yard capacity, allowable vehicle refueling and task reliability of each vehicle. On the basis of describing the vehicle scheduling problem with time windows, multiple yard and multiple vehicle types, [PERSON] [26] established a mathematical model and proposed a simulated annealing genetic algorithm to solve the problem.