## 6 Conclusions and Future Work  
This study investigates the scheduling problem of ship panel block assembly lines, aiming to minimize the assembly time of panel blocks by determining the sequence of incoming blocks. To address this problem, we propose an end-to-end scheduling method based on DRL. Initially, we establish an MDP model that conforms to the scheduling environment, creatively employing disjunctive graphs to capture the state of the current node and designing a reward function. In order to enhance the learning of information contained in the disjunctive graphs, we introduce a policy function based on GIN and train it using PPO algorithms. We compare our proposed model with heuristic algorithms LPT and NEH, metaheuristic algorithms GA and TS, as well as reinforcement learning algorithms DDQN and PPO without disjunctive graph state representation. Experimental results demonstrate that our algorithm surpasses other baseline methods in terms of both model performance and computational time considerations. Moreover, our model exhibits strong generalization ability when handling larger-scale instances. In the practical production of panel blocks, our approach enables shipyards to save significant time, and as the demand for panel blocks increases with the development of large-scale vessels, our model can effectively respond to these requirements.  
However, it should be noted that our proposed DRL method, being an optimization approach, does not guarantee the discovery of a globally optimal solution. Due to limitations in available data, we were unable to test our model on larger-scale instances. Additionally, in order to simplify the problem, we have made simplifications to the actual process of panel block assembly in the assembly line. However, in real-world scenarios, scheduling issues can become more complex, and in the future, we aim to further investigate how to handle unforeseen events that may arise during the actual assembly process, such as workstation machine failures or the introduction of urgent tasks. To better address the scheduling problem in the panel block assembly line, we will enhance the robustness of our model and validate its scalability by testing on larger-scale instances. Furthermore, we will continue exploring improved heuristic and metaheuristic algorithms while conducting additional experiments to verify the consistency of our model across different datasets.  
Conceptualization, T.Z. and L.L.; data curation, T.Z.; formal analysis, T.Z. and L.L.; funding acquisition, L.L.; investigation, T.Z.; methodology, T.Z. and L.L.; project administration, T.Z.; resources, T.Z.; software, T.Z.; supervision, T.Z. and L.L.; validation, T.Z. and L.L.; visualization, T.Z., L.L., Y.H., Z.F., and S.J.; writing--original draft, T.Z., L.L., Y.H., Z.F., and S.J.; writing--review and editing, T.Z., L.L., Y.H., Z.F., and S.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities. (2023-vb-016).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.