## 5 Computational Experiment
### Discussion  
By comparing our proposed model with six baseline methods in two test cases, our model outperforms other baseline methods in terms of computation results and computational time. Among them, the LPT algorithm demonstrates near real-time computation but exhibits a significant disparity in _makespan_ results compared to other algorithms. Therefore, under specific conditions that prioritize computation time, the LPT algorithm is undoubtedly more suitable. However, when considering the overall performance of the model and computational time, our method holds a distinct advantage. As our model's computational instances scale up from 25 to 125, it continues to exhibit superior performance compared to other algorithms, showcasing its remarkable generalization capabilities when handling larger-scale instances. With the development of larger ships, the demand for panel blocks increases significantly. Thanks to the robust generalization abilities of our model, our approach can effectively tackle this challenge. Additionally, it is noteworthy that our model outperforms the PPO algorithm, which does not utilize disjunctive graph representation of states, in both sets of test experiments. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Problem Instance**} & \multirow{2}{*}{**Size**} & \multicolumn{3}{c}{**Heuristic**} & \multicolumn{3}{c}{**Metaheuristic**} & \multicolumn{3}{c}{**Reinforcement Learning**} & \multirow{2}{*}{**Ours**} \\ \cline{3-3} \cline{5-8}  & & **LPT** & **NEH** & **GA** & **TS** & **DDQN** & **PPO** & \\ \hline Ta010 & \(20\times 5\) & 1213.4 & **1108** & **1108** & **1108** & **1108** & **1108** & **1108** \\ Ta020 & \(20\times 10\) & 1701 & 1689.7 & 1693 & 1691.3 & 1657.7 & 1646.3 & **1640.2** \\ Ta030 & \(20\times 20\) & 2305.2 & 2286.1 & 2278.4 & 2259.8 & 2263.0 & 2256.5 & **2247.4** \\ Ta040 & \(50\times 5\) & 2901.6 & 2884.5 & 2881.6 & 2875 & 2879.1 & 2874.8 & 2861.8 \\ Ta050 & \(50\times 10\) & 3198 & 3175.4 & 3171.5 & 3162.6 & 3168.3 & 3159.5 & **3145.1** \\ Ta060 & \(50\times 20\) & 3971.6 & 3951.8 & 3954 & 3946.5 & 3931.4 & 3903.4 & **3887.4** \\ Ta070 & \(100\times 5\) & 5560 & 5483.2 & 5479.1 & 5451.5 & 5452.5 & 5429.7 & **5396.2** \\ Ta080 & \(100\times 10\) & 6089.7 & 6004 & 5996.4 & 5989.1 & 5983.0 & 5971.8 & **5949** \\ Ta090 & \(100\times 20\) & 6705.9 & 6670.8 & 6658.5 & 6649.4 & 6652.3 & 6649.4 & **6627** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Makespan_ of each algorithm on the common benchmark (h). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Problem Instance**} & \multirow{2}{*}{**Size**} & \multicolumn{3}{c}{**Heuristic**} & \multicolumn{3}{c}{**Metaheuristic**} & \multicolumn{3}{c}{**Reinforcement Learning**} & \multirow{2}{*}{**Ours**} \\ \cline{3-3} \cline{5-8}  & & **LPT** & **NEH** & **GA** & **TS** & **DDQN** & **PPO** & \\ \hline Ta010 & \(20\times 5\) & 0 & 1.73 & 3.75 & 7.14 & 0.70 & 0.79 & 0.72 \\ Ta020 & \(20\times 10\) & 0 & 2.14 & 4.26 & 7.58 & 1.19 & 1.35 & **1.14** \\ Ta030 & \(20\times 20\) & 0 & 2.47 & 5.74 & 9.77 & 1.39 & 1.52 & **1.31** \\In essence, the satisfactory performance of our method can be attributed to the end-to-end model based on DRL efficiently generating feasible scheduling sequences, coupled with the disjunctive graph-based state representation method, which provides a more comprehensive and rational depiction of the current scheduling information. Hence, our model stands as a competitive approach for solving the ship panel block assembly scheduling problem. In practical shipbuilding processes, our method can provide scientific and effective scheduling plans for panel block assembly, reducing construction time for blocks, and facilitating the execution of subsequent work plans, thereby shortening the shipbuilding cycle and lowering costs.