Optimization of rolling stock scheduling scheme for urban rail transit under reconnection marshalling

With the continuous exploration of the daily passenger flow travel rules of urban rail transit, innovation in transport organization is the key approach to solve the problem of effective matching between passenger flow and transport capacity, and to achieve the system energy saving and maximize the social and economic benefits. The operation mode of reconnection marshalling can effectively improve the matching degree between passenger flow and transport capacity. By analyzing the differences in rolling stock scheduling problems between reconnection marshalling and fixed marshalling, the succession method of the reconnection train based on the “shadow train” was constructed. Taking the rolling stock and train number succession, rolling stock consistency and reconnection marshalling operation as the constraints, and taking the minimum total cost of train number succession and the minimum standard deviation of rolling stock use time as the objective functions, the optimization model of rolling stock scheduling scheme of urban rail transit under reconnection marshalling was constructed. By introducing a nonlinear inertia weight update method and dynamic learning factors, a multi-objective chaos particle swarm optimization (MOCPSO) algorithm was designed. An example of 102 train numbers on an urban rail transit line was taken to verify the effectiveness of the model. The upper limit of train number succession time, the reconnection and unmarshalling operation of rolling stock and the rolling stock storage were discussed and analyzed. The results show that the MOCPSO algorithm can effectively jump out of the local optima by introducing the Logistic chaos optimization strategy. The larger the upper limit of the train number succession time, the more the rolling stock input quantity. It is not appropriate to make the train number succession time too long. In the process of rolling stock scheduling, the number of reconnection and unmarshalling operations should be reduced as much as possible. This method can provide decision-makers with a series of rolling stock scheduling Pareto non-inferior schemes with different operation inputs and different balance of rolling stock scheduling. The approach is helpful to coordinate the utilization of the transportation capacity and reduce the energy consumption of rail transit. © 2024, Central South University Press. All rights reserved.