## 5 Conclusions  
This paper proposes a new intelligent scheduling model, PERT-RP-DDPGAO. This model decomposes project plans using PERT technology. After the project plan is decomposed, a resource plan is formed, and the resource plan is trained as an intelligent agent in the DDPG model to achieve automatic coordination of multiple projects and multilevel plans in an enterprise.  
The PERT-RP-DDPGAO model adds a feedback environment to traditional PERT techniques, improving the robustness of traditional algorithms. This paper studies resource planning, which has received little attention in scheduling algorithm research. For the first time, the resource plan is transformed into a resource plan matrix through the matrix formula, and the control action of the resource plan is simulated through matrix operation. After the resource plan is matrived, the DDPG algorithm is used to achieve automatic coordination of the resource plan. After analysis, the results of automatic coordination have practical managerial implications and potential for engineering applications.  
Finally, this paper conducts comparative experiments on the DDPG part of the new algorithm with the DQN algorithm, random search algorithm, and greedy search algorithm, analyzing and verifying that the DDPG algorithm is superior to the other algorithms in  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm Name** & **Response Time (s)** \\ \hline DDPG algorithm & 0.98 \\ DQN algorithm & 1.01 \\ Grey search algorithm & 1.07 \\ Random search algorithm & 1.22 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison table of response times for different algorithms.  
terms of convergence and response speed. The DDPG algorithm has a response time 3.0% lower than that of the DQN algorithm. The algorithm has a response time that is 8.4% shorter than that of the gray search algorithm. The algorithm has a response time reduced by 19.7% compared to that of the random search algorithm.  
The PERT-RP-DDPGAO algorithm considers engineering applications, simplifies the complexity of the production process, and mainly focuses on the core planning parameters of project management. The core input parameters of the algorithm are planned time, time parameters, and resource capability. The planned time and time parameters are derived from the PERT algorithm, while the resource capability is obtained through manual filling. Therefore, the core of the application of the algorithm is for enterprises to have the ability to track product plans. Through practical application, it has been found that enterprises with ERP and MES systems can use this algorithm. However, this algorithm still has the following limitations: Firstly, algorithms require enterprises to have basic information technology capabilities, such as establishing ERP or MES systems that can provide real-time feedback on product progress. Secondly, the algorithm has only been applied to machining devices, and the generalization of the resource planning matrix still needs to be improved. Thirdly, the application of algorithm results also needs to be combined with management processes. The fourth issue is that the algorithm does not involve risk management.