Stochastic Optimization Model And Algorithms For Train Scheduling Problem

In this paper, we discussed two questions:train dispatching under the random traffic and the train operation adjustment under uncertainty time delays. Randomized traffic scheduling problem, consider the high-speed rail traffic in the case of stochastic volatility, rational use of existing technology and equipment to ensure traffic safety and passenger safety conditions, reasonable arrangements for passenger trains through the lane between the interval grouping the number of trains to ensure train operation diagram, complete the transportation and production tasks, improve high-speed railway passenger transport capacity of trains. Summarizing the traffic control experience of home and abroad, gives a flexible way traffic control. Taking into account the fluctuations in passenger flow, we put the traffic flow as a random variable, create a random traffic demand scheduling model and design step by step and mean random demand two algorithms to simulate the model and obtain the appropriate traffic control plan for the railway companies.The train operation adjustment under uncertainty time delays is that the interference during the actual operation diagram due to the random deviation from the plan diagram. Different types of interference caused by different trains at the station make the actual moment with stochastic, as well as the delay time. Based on the scheduling and our high-speed railway passenger train to run under the actual operating conditions, fully into account the level of trains in the railway network, make the train's arrive and depart time in the station as the random variables minimizing the total weighted delay time at all stations, and establishing the stochastic optimization model for the train adjustment under the uncertainty delay time. In the algorithm design process, respectively, known and unknown delay time to give a different solution strategy, the stochastic optimization model to solve the model into a deterministic optimal draw the corresponding train diagram. Numerical examples verify the feasibility of the model and real-time scheduling.