## 4 Simulation Results
Antennas were connected to readers and readers were connected to the computer. The RFID tags were attached to different manufacturing objects, such as machines, AGVs, and WIP. Antennas were used to collect real-time status information of these manufacturing objects from attached RFID tags. Computational experiments were conducted by R-3.3.0 for Windows (64-bit) in a PC with a quad-core AMD A10-5750M processor at 2.5 GHz and 8 GB DDR3-1333 RAM. R-3.3.0 for Windows (64-bit) is a software environment for statistical computing and graphics. Referring to the manufacturing process, the TCPN model was built up in CPN Tools, as shown in Figure 5. The TCPN model started running at the same time when the production and logistics were executed according to the planned time. Firstly, real-time status information of machines, AGVs, and WIP were transmitted to the PC through RFID reader ports. Secondly, based on the collected information, the objective functions were implemented by R-3.3.0 for Windows (64-bit) in the PC and the results were stored in Standard ML (SML) files. Thirdly, every time the cycle of the TCPN model started, the information in the SML files was updated. By loading SML files, the status of colored tokens was tuned accordingly. Using the proposed method, the simulation result is shown in Table 5. T0001 denotes the first published logistics task, W denotes the warehouse, M1 denotes the machine1, A001 denotes the AGV1, and similarly for the others. The total waiting time was 26.4. The makespan was 30.3. The electricity consumption of machines was 894.8, while the electricity consumption of AGVs was 98.7. Thus, the total electricity consumption was 993.5. The efficiency of the proposed model was verified and the result showed that the computing time was less than 0.01 s, which was reasonable for implementing the proposed method in the real manufacturing environment. A comparative study between an event-driven method [18] and the proposed TCPN based self-adaptive collaboration method was presented. Since real-life data were not given in the paper used for the comparative study, an instance of job shop problems from [25] was used as a feasible schedule plan, including four machines and three jobs, as shown in Table 4. Based on the same benchmark, the objective functions were implemented by R-3.3.0 for Windows (64-bit) in the PC with the consideration of three KPIs including waiting time, makespan, and electricity consumption. The result of the comparative study is shown in Table 6. The result demonstrated that the TCPN-based self-adaptive collaboration method outperformed the event-driven method in reductions of waiting time, makespan, and electricity consumption. \begin{table}
\begin{tabular}{c c c} \hline \hline
**KPI** & **Event-Driven Method** & **Self-Adaptive Collaboration Method** \\ \hline Total waiting time & 37.1 & 26.4 \\ Makespan & 36.3 & 30.3 \\ Total electricity consumption & 1035.3 & 993.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The result of the comparative study. KPI: key performance indicators. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Task** & **Start Point** & **Destination** & **Job** & **Priority** & **AGV** & **Remaining** & & **Waiting Time** & & **Computing** \\
**ID** & **(X, Y)** & **(X, Y)** & **ID** & **ID** & **Space** & **After** & **Transport** & **Before** & **Time (s)** \\ \hline T0001 & W(0, 4.5) & M1(2, 5, 4.5) & 1 & 0-t & A001 & 2 & 0 & 0.5 & 0 & \textless{}0.01 \\ T0002 & W(0, 4.5) & M2(2, 5, 2) & 2 & 0-t & A002 & 1 & 0 & 1 & 0 & \textless{}0.01 \\ T0003 & W(0, 4.