### Related Work  
Before the widespread adoption of TSN, other deterministic protocols like the Time-Triggered Protocol (TTP) were foundational in safety-critical systems. TTP utilizes a static schedule known as a Message Descriptor List (MEDL), which is conceptually analogous to the GCL in TAS, to orchestrate network communication [13]. Building on this legacy, the IEEE 802.1 [2] Time-Sensitive Networking (TSN) task group, established in 2012, standardizes a suite of protocols to enable deterministic communication over standard Ethernet. These protocols build upon foundational standards like IEEE 802.3 for Ethernet [14] and IEEE 802.1Q for bridging [2]. Key TSN components that form the infrastructure for deterministic scheduling include mechanisms for precise time synchronization (IEEE 802.1 AS [15]) and centralized network configuration (IEEE 802.1 Qcc [16]). The application of TSN is extensive, representing a key technology for future communication paradigms such as the 5G-enabled Industrial IoT [17]. The generation of Gate Control Lists (GCLs) for the Time-Aware Shaper (TAS) is a well-known NP-complete problem [12], and a comprehensive overview of solution methodologies can be found in recent surveys [18]. Various approaches have been explored, including:  
* Integer Linear Programming (ILP): This method formulates the scheduling problem using linear inequalities with an objective function to evaluate solution quality. Widely used ILP solvers include CPLEX [19] and Gurobi [20]. * Satisfiability Modulo Theories (SMT): SMT solvers, such as Z3 [21], are employed to find solutions for problems described by first-order logic formulas, combining Boolean satisfiability with theories like linear arithmetic. * Heuristic Algorithms: When finding optimal solutions is computationally infeasible due to problem complexity, heuristic algorithms are used to find suitable, near-optimal solutions within a reasonable time frame. These can range from general metaheuristics to problem-specific strategies [22]. * Deep Learning: The capability of deep learning models to handle complex decision making, predict network dynamics, and learn from extensive datasets makes them a promising tool for TSN scheduling problems, with recent work exploring approaches like reinforcement learning [23]. * Tabu Search: This metaheuristic method systematically explores the solution space, maintaining a \"tabu list\" of recently visited solutions or moves to prevent cycling and guide the search out of local optima [24]. It is important to note that the aforementioned approaches primarily address the offline scheduling problem, where the complete set of traffic requirements is known beforehand. A complementary research area focuses on online scheduling, where scheduling decisions are made dynamically as traffic arrives. Online methods are particularly relevant for handling unforeseen or aperiodic data flows, which are also present in automotive applications. A notable example is the work by [PERSON] et al. on a deadline-aware online scheduler specifically for automotive TSN flows [25]. The research presented in this paper firmly belongs to the offline scheduling paradigm, aiming to generate a highly optimized GCL based on a known set of time-triggered streams to guarantee their deterministic performance. While precise algorithms like ILP and SMT can determine optimal GCLs, their computational time often grows exponentially with the number of traffic flows and network complexity, rendering them impractical for large-scale, dynamic in-vehicle networks where quick GCL recalculation might be necessary. Approximate methods, including various heuristics and machine learning techniques, aim to reduce this computation time but may do so at the cost of sacrificing some determinism in terms of achieving the absolute lowest latency and jitter.