## 4 Simulation Results  
This section introduced an industrial case from an engine manufacturing company in Xi'an. In the case company, FMS was used to manage and coordinate key equipment and automatize the production. Engine components were processed on different machines in the given processing sequence. A material handling system was used to transport these components between machines. However, the machining environment changed dynamically due to the external and internal fluctuations, and the optimal process plan and schedule became less efficient or even infeasible. Thus, the case company needed production-logistic collaboration methods and tools to solve the problems. Based on the investigation of the case company, a simulation experiment was conducted in the laboratory by using CPN Tools to validate the performance and applicability of the proposed method. In the simulation experiment, 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. \(p_{ij}\) denotes the processing time of the operation that job \(j\) is processed on machine \(i\), \(i=1,2,3,4,\ j=1,2,3\). These jobs would be processed by a list of machines in a given order. The volume of each job was given in Table 4 because the volume capacity of AGVs was considered as a constraint in the simulation experiment. A feasible schedule plan is shown Figure 4. The initial time of the schedule was 0. The gray segment represented the variable waiting time between two continuous operations of the same job. In addition, two AGVs were added in the simulation experiment in order to investigate the collaborative relationship between production and logistics in a job shop. Thus, the aforementioned job shop consisted of the following main components, namely a warehouse, four production cells, and two AGVs. The warehouse had an in-buffer and an out-buffer. Each production cell had a machine, an in-buffer, and an out-buffer. Machines and AGVs installed with sensors had the capability of perceiving status information in real time and making decisions based on the knowledge base and real-time information. Other manufacturing resources, such as in-buffers, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Jobs** & **Machine Sequence** & **Processing Times** & **Volume** \\ \hline
1 & 1, 2, 3 & \(p_{11}=10,\ p_{21}=8,\ p_{31}=4\) & 1 \\
2 & 2, 1, 4, 3 & \(p_{22}=8,\ p_{12}=3,\ p_{42}=5,\ p_{32}=6\) & 2 \\
3 & 1, 2, 4 & \(p_{13}=4,\ p_{23}=7,\ p_{43}=3\) & 1.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The instance of the job shop problem. Figure 4: The feasible schedule plan. out-buffers, pallets, and WIP were attached RFID tags to provide real-time location information. The workers had staff cards with embedded RFID tags to provide individual information. For simplicity of understanding but without losing generality of principle, some initial and basic information of manufacturing resources were given as follows. The average power of each machine when processing was 15, while the average power of each machine when idle was 4. AGV1 and AGV2 had the same initial location (0, 4.5). The average power of each AGV was 7. The speed of each AGV was 5 and the maximum space of each AGV was 3. The production and logistics began at initial time 0. Firstly, raw materials were transported to smart machines by smart AGVs. Then, the machine processing started and smart machines actively published logistics tasks. Next, smart AGVs actively requested the logistics tasks. The layout of the shop floor is shown in Figure 5. The black spots in the layout represented the location of in-buffers or out-buffers of the warehouse or production cells. Figure 5: Simulation experiment of the case scenario. CPN: colored Petri nets; RFID: radio frequency identification. As shown in Figure 5, a simulation experiment was conducted based on the case scenario. In the experiment, a personal computer, fifteen antennas, four RFID readers, and nine RFID tags were used.