Research on flow scheduling of train communication based on time-sensitive network

The rapid development of intelligent rail transportation equipment is raising higher requirements for the real-time performance of Train Communication Network (TCN) data transmission. Time-Sensitive Network (TSN) has gained widespread attention in train communication due to its advantages of high transmission rates and deterministic latency. Firstly, for addressing the flow scheduling problem in TSN, a TCN topology model supports TSN is designed. It establishes a multi-objective flow scheduling model based on a priority strategy, aiming to minimize average response time and makespan while ensuring a scheduling success rate. Then, to solve the routing selection problem, a joint scheduling algorithm based on multi-level routing selection is adopted, providing feasible scheduling solutions. Finally, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm is introduced to iteratively and selectively optimize the scheduling solutions. This approach resolves the flow ordering problem while achieving optimization for the scheduling model. The feasibility of this scheduling approach is validated through simulations, and the results demonstrate significant advantages of the proposed model and algorithm in terms of scheduling success rate and real-time performance, providing effective solutions for flow scheduling in TCN.