## 5 Self-Reconfiguration Methods in SSrMS
### Negotiation Model  
The negotiation model can reduce the time and cost of reconfiguration by reconfiguring a part of the manufacturing process. Furthermore, it is possible to realize the system's goal by considering each fractal's goal. [PERSON] et al. [51] suggested a negotiation model for reconfiguring the manufacturing process with many negotiations, as illustrated in Figure 16. The method proposed by [PERSON] et al. [51] consists of three mechanisms: resource-based negotiation, task-based negotiation, and hybrid-type negotiation. The hybrid-type negotiation involves the use of the genetic algorithm (GA) to realize the optimization  
Figure 15: Goal decision model in SSrMS. \begin{table}
\begin{tabular}{l c c} \hline \hline  & **System’s Goal (Profit Value)** & **Difference (\%)** \\ \hline Actual value & 5.61 & \\ Neural network result & 5.38 & 5.28\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Actual and experimental result. process. To execute hybrid-type negotiations, [PERSON] et al. [51] suggested five agent modules: the bid-manager, agent dispatcher, task agent module, negotiation agent module, and resource agent module. The hybrid-type negotiation can simultaneously reassign tasks and resources using the GA. In the hybrid-type negotiation method, the GA derives optimized reallocation. Based on the results of reallocation from the GA, hybrid-type negotiation realizes optimization. The hybrid-type negotiation involves three steps to perform negotiations with the GA as shown in Figures 17 and 18. Steps 1 and 2 of the hybrid-type negotiation process are shown in Figure 17. Step 1 begins with the bid-manager determining the fractal and then joining a negotiation process. The bid-manager decides the candidates for negotiation, and each candidate checks their sub-fractals. If the candidates have sub-fractals, then all the sub-fractals enter the negotiation process. All the fractals also check priority to confirm the negotiation participants once the bid-manager determines all candidates. Fractals with insufficient priority are excluded from the negotiation. Based on step 1, the fractals are determined to participate in the negotiation. In step 2, the final boundary is determined according to the highest-level fractal. The highest-level fractal determines the final boundary of the negotiation. In step 3, the hybrid-type negotiation process is performed, as shown in Figure 18. Hybrid-type negotiation covers two cases: negotiation case 1 and negotiation case 2. Negotiation case 1 refers to the negotiation between multi- and single-agents. Furthermore, negotiation case 2 refers to the negotiation between multi-agents. A single fractal shares the resources or tasks under negotiation case 1. By contrast, under negotiation case 2, multiple fractals assign the resources and tasks using the GA. In order to perform the experiment by GA, the GA was implemented using Java Eclipse to verify the hybrid-type negotiation. The population size was set as 100 units, and the number of iterations was 700. The mutation ratio was set as 0.1. In the GA, a chromosome consists of two parts indicating the fractal ID and the tasks with sequence. A gene in the chromosome has two types: fractals and tasks. A fractal gene has information on unique fractal IDs, which represent each piece of equipment including \(F_{1}\), \(F_{2}\), and \(F_{3}\). A task gene has information on the job and task, which represents the job and task IDs (e.g., \(J_{1}\), \(J_{2}\), \(J_{3}\),... and \(T_{11}\), \(T_{12}\), \(T_{21}\), \(T_{31}\),... ). Furthermore, each task has information regarding the processing time of every other task. The GA has three objectives, which are the minimum total task completion time for each fractal (i.e., \(CF\)), the minimum workload of each fractal (i.e., \(WF\)), and the minimum total workload of fractals (i.e., \(WT\)). The fitness value of the chromosome is decided by the objective function \(f(z)\), which corresponds to \(\min f(z)=(CF+WF+WT)\).