## 3 Proposed Blockchain and Digital Twin-Based Heuristic Multi-Cooperation Scheduling Framework
### Blockchain-Based Data Validation
To sum up, this makes it ideal for implementation in live scenes before manufacturing adds DT to complement and protect DT-based cloud environments. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Comparison Item** & **LAN-Fabric** & **WAN-Fabric** \\ \hline TPS & 365.9 & 76.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Throughput of a distance-based different network. As is depicted, the proposed algorithm managed to maintain high and stable throughput for the blockchain network. As shown in Figure 5, the original algorithm recorded a decline of throughput with the increasement of peer nodes in the network; on the other hand, our proposed algorithm shows better throughput than the original algorithm. In our approach, the broadcasting is centered near the base station and starts transmitting information based on physical distance, and the cluster will be divided by the base station. The throughput results in Table 3 show that network nodes across physical regions, network operators, public cloud nodes, limited network bandwidth, and other problems will cause network delay. Data processing based on physical distance can resolve these problems to achieve a higher throughput rate. In addition, the destination of cluster-based data transmission is more apparent in the network routing process. More similar data can be packaged together, so the number of transactions completed per unit of time will increase. This improves the efficiency of our proposed heuristic multi-cooperation scheduling framework for the SM environment. Figure 4: Authenticated time of the proposed Blockchain algorithm. Figure 5: Authenticated time of the proposed Blockchain algorithm. ### Simulation of the PDQN DRL Model in DT

DT has been employed in current production and testing systems in various sectors. It has been emphasized as a deployment approach for establishing completely automated systems and improving semantic interoperability across several domains. In a smart factory, the manager's purpose is to construct an intelligent agent, make plans, and take measures to decrease unexpected downtime and waiting time, enhancing the overall factory processing rate. A typical smart manufacturing system is optimized in this manner. #### 4.3.1 DT's Simulation and Modeling

For our work, we consider a factory installed with cameras in a physical location to obtain manufacturing (factory) conditions by providing footage for a specific range inside the factory. Our model considers the factory's storage house from several places with a central operator and a reserved area (reserved areas with space capacity CR) used to store unprocessed goods. The remaining space in the storage facility is recorded as Cr (we consider this the current state for our model), corresponding to the present time stamp (t). Under such settings, it is clear that for each time step, the current state of our model is set at Cr of range {0 to CR}, and therefore, CR+1 states are available. SMDP model is used to build the discount cost function to estimate the system's processing capacity for warehouse storage. In our study, the Semi-Markov Decision Process (SMDP) model is used as the primary model for examining storage units inside a smart factory. The agent controls the range R(n) for the factory storage activity and capacity and it is required to be checked at each timestamp (t). It is also worth mentioning that the decision system's precision is entirely dependent on the precision of the dispersion of the RFID antenna and resolution of the image-based object detection cameras and system, which determines the number of possible actions. The states (Sn) will be set to [0] when the reserve area is filled, and there is no space left for additional units ready to process or store at the warehouse. Under such circumstances, it is fair for the agent (DRL-model) to select action (A(n)) = 0, thus meaning it terminates obtaining and storage of new units in the defined storage area of capacity CR.