## 7 Conclusions  
As a result of conducting this research, it is proposed that this work be dedicated to analyzing and optimizing the shipbuilding automated collaborative order-picking (SACOP) scheduling in flexible multi-level picking systems. Precisely, the key role of AGVs in \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**AAQ**} & **MADPSO** & **TPPM** & **RMFSOM** & \multirow{2}{*}{**MADPSO/TPPM**} & \multirow{2}{*}{**MADPSO/RMFSOM**} \\ \cline{2-5}  & **AROS** & **AROS** & & & \\ \hline
4 & 395.68 & 1380.7 & 441.97 & 71.34\% & 10.44\% \\
8 & 387.96 & 1132 & 439.86 & 65.73\% & 10.89\% \\
12 & 384.7 & 1055 & 433.05 & 63.54\% & 11.92\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Calculate the average value of AROS in Table 5 according to AAQ. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**AAQ**} & **AROS** & **UAQ** \\ \hline
4 & 467.6 & 2 \\
6 & 454.22 & 3 \\
8 & 461.39 & 2 \\
10 & 460 & 2 \\
12 & 479.92 & 3 \\
14 & 483.62 & 3 \\
16 & 487.91 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Supplementary experiments result in different AAQ values when fix PQ equals 12 and WQ equals 2. SACOP activities was noticed and a multi-AGV-driven pallet-picking scheduling optimization (MADPSO) method to transform the SACOP problem into the modified multi-AGV scheduling task problem (MMATSP) was proposed. On this basis, a mathematical model was established to describe the multi-objective optimization process of using the MADPSO method to solve the SACOP problem, in which the optimization objectives, constraints, and the interaction strategy between devices after being converted to the MMATSP were analyzed in detail. Furthermore, an improved NSGA-III algorithm was designed to implement the MADPSO method, including the design of a double-layer coding method, a simulation decoding operator based on a queuing service model, a hybrid crossover and mutation strategy, the additional TOPSIS decision support operator, and so on. Finally, through a series of comparative experiments, the effectiveness of the MADPSO method in solving SACOP problems and the performance advantage of MADPSO compared with traditional pallet-picking mode (TPPM) and other existing research that may be used to solve SACOP problems are verified. By using the MADPSO method, the difficult multi-level flexible order-picking optimization problem with complex coupling relationships can be transformed into a more easily solvable multi-AGV parallel tasks scheduling optimization problem. Furthermore, the queuing service model is used to solve the temporal relationship problem in parallel scheduling, thereby transforming the SACOP problem into a common combinatorial optimization problem that can be solved by multiple heuristic algorithms. This means that our work can address the challenges associated with scheduling optimization in flexible multi-level picking systems. In addition, the MADPSO method proposed in this paper has the following advantages. Firstly, the MADPSO method can solve multi-objective optimization problems with more than two objective functions, which makes it highly scalable in practical applications and can increase optimization objectives according to actual needs. The SACOP problem discussed in this article involves three objective functions, and when another objective needs to be added, the adjustment can be simply completed by expanding the reference hyperplane and adjusting the TOPSIS operator. Secondly, the MADPSO method fully considers the performance requirements of jumping out of local optima and has a strong global search ability. Therefore, this method still has good optimization ability when solving problems involving nonconvex/nonlinear objective functions and constraints. Finally, the MADPSO method has a certain solving ability for problems with uncertain parameters, and its included queuing service model can be used to transform the original picking scheduling scheme into a feasible solution in the event of sudden parameter changes.