Joint Routing and Scheduling Optimization of In-Vehicle Time-Sensitive Networks Based on Improved Grey Wolf Optimizer

In-vehicle time-sensitive networking (TSN) delivers highly secure, ultralow latency deterministic communication for intelligent connected vehicles (ICVs). To tackle the traffic scheduling problem of in-vehicle TSN, this study establishes in-vehicle network topologies and flow models, abstracts the traffic scheduling problem as a job-shop scheduling problem (JSSP), and formulates a priority-based optimization function capable of various end-to-end (E2E) delay requirements. A joint routing and scheduling optimization strategy based on improved grey wolf optimization (IGWO) is proposed, which incorporates acrlong LF, historical experience learning, and acrlong TS operators to significantly enhance search capabilities and optimization efficiency. This strategy can rapidly solve large-scale in-vehicle network scheduling and generate scheduling results with outstanding delay performance. Dynamic routing that combines load-balanced and shortest path effectively minimizes interference between flows, further reducing E2E delay. Simulation experiments grounded in realistic ICV scenarios demonstrate the effectiveness of the proposed strategy. Furthermore, the simulation results verify the impact of flow period parameters and network topologies on E2E delay, offering guidance for in-vehicle TSN engineering design.