Research On Optimal Utilization Problem Of Urban Rail Transit Train Stocks

In recent years, urban rail transit develops rapidly in China. While the increasing number of cities with operating rail transit lines, the demand of urban rail transit train stocks is increasing as well. At the same time, in order to improve the service quality and reduce the train stock quantity, the operation departments make flexible train operation plans according to the characteristic of passenger flow. Therefore, there is important practical significance to make reasonable train stocks utilization plans that meet the transportation demand and reduce the operation costs. Based on the development reality of the advanced experience at home and abroad, this paper takes a series of related research on optimization problem of urban rail transit train stocks utilization planning. The main works accomplished in the paper include:(1) Analysis of the influence factors of urban rail transit train stocks optimal utilization problem. Based on the comprehension of urban rail transit train stocks utilization planning, the paper analyses the influence factors of train stocks utilization efficiency from many aspects like train operation plan, Train Diagram, train reentrant mode, vehicle use mode, vehicle maintenance regulation, depot maintenance ability, etc. This part is the foundation of the modal and algorithm research on the urban rail transit train stocks optimal utilization problem.(2) Establishment of the optimal utilization modal for the urban rail transit train stocks. The train stocks utilization planning optimization problem is transferred into Travelling Salesman Problem (TSP) on the train stocks using network. First, without the maintenance constraint, the basic train stocks utilization planning modal is established while considering the train stocks connecting time minimized as the objective function. Then, considering the constraint of train stocks maintenance regulation and depot maintenance ability, the optimization modal objective function is minimizing both the vehicle connecting time and the maintenance waiting time.(3) Greedy genetic algorithm research and design on solving the train stocks utilization planning optimization modal. Use the real matrix encoding mode to encode the train running tasks by different train stocks. Try to find better initial solution by prejudging whether the current train stock can complete the next expected running connection. The greedy algorithm is lead into the genetic algorithm to improve the solving speed.(4) Case design and result analysis. Taking an urban rail transit line with long and short routing in H as example, the train stocks optimal utilization plan of two cases is solved by MATLAB:Without considering the maintenance constraint and considering the constraint of train stocks maintenance regulation and depot maintenance ability. Prove the effectiveness of the modal and algorithm. Analyses the influence to train stocks utilization efficiency by train stocks maintenance regulation and depot maintenance ability, and provide reference for depot maintenance capability set.