Study On The Optimizing Method Of Railway Train Formation Plan And Train Scheduling Based On MAS

Train Formation Plan is the organization plan of car-flows .Train Scheduling is the train working plan and the promises which railway transportation affords to society. The number and type of train, the starting stating and ending stations and so on, which train formation plan stipulates, are the bases of train scheduling. So, when making up train formation plan and train scheduling, we must pay attention to the coordination and cooperation of them and intensify the combination of car-flows and railway line, in order to improve the qualities of making up train formation plan and train scheduling and realize the whole railway transportation production optimization.This paper, analyzes freight demand, founds a mathematic model of train formation plan and train scheduling and an integrative model form based on MAS, and then puts forward the algorithms. At last, it uses an example to validate availability of this method.The paper mainly consists of the following sections:(1)By analyzing the freight demand and insufficiency in china railway freightage, the paper founds a mathematic model of train formation plan and train scheduling, to expound the theory of coordination and cooperation between them.(2) It founds an integrative model form of train formation plan and train scheduling based on MAS, and endows Agent with avail function, according to the demand and attribute of entity witch it corresponds to. Then it sets up an interaction model based on contract-net, and gives corresponding message.(3) Aiming at allocating working time in section and the arriving time and departing time in station for train, the paper puts forward a dispatching algorithms based on modified"gantt"(4) At last, it uses an example to validate availability of this method..The paper, oriented to freight demand, for the first time, attempts to adopt MAS to solve the coordination and cooperation of train formation plan and train scheduling. It founds the model, put forwards algorithms. Although it is not perfect, it is useful to research and perfect it ulteriorly.