## 1 Introduction
[16] proposed an efficient iterative local search approach hybridized with simulated annealing and variable neighborhood search, demonstrating through large-scale computational experiments that their method achieves high-quality solutions with significantly improved total weighted tardiness (TWT) compared to dispatching rules. [PERSON] et al. [17] systematically reviewed heuristic approaches for multi-objective FJSPs, highlighting their capability to balance competing objectives like makespan minimization and energy efficiency while maintaining near-optimal performance across diverse problem instances. [PERSON] et al. [18] empirically validated the effectiveness of metaheuristics in an FJSP, showing that genetic algorithms outperform Tabu Search and Ant Colony Optimization in both partial and total flexibility scenarios, achieving up to 18% improvement in total completion time for large-scale instances. [PERSON] et al. [19] significantly enhanced scheduling stability by integrating neighborhood search and competitive learning mechanisms into a Hybrid Discrete Particle Swarm Optimization (HDPSO) algorithm, considering transportation time. Similarly, [PERSON] et al. [20] proposed an improvedNSGA-II algorithm (ASA-NSGA-EE) that combines adaptive crossover and mutation with simulated annealing strategies to achieve the collaborative optimization of multi-objective low-carbon scheduling. However, these methods still suffer from high computational complexity and a tendency to become trapped in local optima. Deep reinforcement learning (DRL) has demonstrated significant potential in addressing the FJSP by enabling adaptive decision-making through environmental interactions. Recent studies have explored diverse neural architectures to improve scheduling decisions through environmental interactions [21; 22]. Early efforts focused on standard neural networks for state representation. [PERSON] et al. [23] developed a distributed Q-learning system that dynamically selects machines and dispatching rules yet struggles with high-dimensional decision spaces. [PERSON] et al. [24] integrated SARSA/Q-learning with genetic algorithms through Multi-Layer Perceptron (MLP)-based parameter adjustment, achieving accelerated convergence but limited generalization. [PERSON] et al. [25] proposed a two-stage algorithm based on Convolutional Neural Networks (CNNs), while [PERSON] et al. [26] designed a Deep Q-Network (DQN) with multi-dimensional features and ten dispatching rules. Though effective in specific scenarios, these methods are fundamentally constrained by their reliance on static feature engineering that fails to capture dynamic operation-machine interactions, coupled with rigid state representations that neglect critical topological dependencies. Furthermore, MLP-based architectures exhibit limited scalability when handling problems of varying complexity. To address these issues, recent studies adopted graph-based representations. [PERSON] et al. [27] pioneered a heterogeneous graph neural network (HGNN) that models the FJSP as a Markov Decision Process (MDP), outperforming traditional rules through topological feature learning. [PERSON] et al. [28] extended this with dynamic graph updates in an actor-critic framework, demonstrating robustness in fluctuating environments. For complex constraints, [PERSON] et al. [29] embedded crane transportation features into DQN through graph neural network (GNN)-based state encoding, and [PERSON] et al. [30] combined GNNs with knowledge-based RL for multi-objective optimization. [PERSON] et al. [31] further developed a multi-agent GNN system for textile manufacturing. However, existing GNN approaches remain hindered by static graph structures that cannot adapt to real-time compatibility changes, oversimplified node processing that fails to distinguish between heterogeneous entities like machines and operations, and inflexible attention mechanisms that inadequately model dynamic resource competition patterns.