## 5 Self-Reconfiguration Methods in SSrMS  
### Goal Decision Model  
There are three methods for process reconfiguration which are the goal decision model, negotiation model, and sustainability assessment method in SSrMS. [PERSON] and [PERSON] [12] suggested the concept of a goal decision model to generate a system's goal based on environmental needs. The goal decision model falls under the category of goal-orientation technologies, which consist of GGP, GHP, and GBP. However, the reference goal decision model cannot represent all the goals in-depth because the goals for the manufacturing system are complicated owing to the interdependencies among the goals. [PERSON] et al. [49; 50] suggested a goal decision model mechanism based on the goal model concept of [PERSON] and [PERSON] [12]. The goal decision model is a crucial mechanism for determining the system's goal. Furthermore, the proposed model features flexibility in terms of deciding goals. The mechanism of the goal decision model includes five steps, as illustrated in Figure 13. As shown in Figure 13, the goal decision model can determine the system's initial best goal. In order to perform experiments, a neural network (NN) model has been developed for implementing the goal decision model by using Python 3.8. To develop the NN model, the results of NN optimization provided by the Matlab library have been considered. As shown  
Figure 13: Mechanism of goal decision model [49]. in Figure 14, the activation function of the NN is a Gaussian function by data tendency verification from Matlab. Therefore, the NN includes a regression model with a Gaussian activation function. The total number of data was 500 and sizes of data for testing and validation were 50% each. The number of training epochs was 1000. Data used in this study are artificially made, similar to actual company data. This is because actual data have many missing values and it may have a negative impact on training and validation. The NN consists of one input layer (5 nodes), four hidden layers (32 nodes-64 nodes-32 nodes-5 nodes), and one output layer (1 node). As shown in Figure 14, data for the experiment consists of productivity, efficiency, utilization, flexibility, cost, and profit. The values of productivity, efficiency, utilization, flexibility, and cost range from 0 to 1, but that of profit ranges from 0 (i.e., zero profit) to 10 (i.e., maximum profit). As illustrated in Figure 14, each fractal has its own NN to realize its goal, and the result from each fractal can be used as an input value to realize the system's goal. Based on the derived value of the system' goal, the NN can obtain each fractal's optimized parameter value. Based on the NN structure, individual fractals can decide a new goal under the system's goal in order to reset the goal. Furthermore, the manufacturing system can reset the goal or execute partial reconfiguration in the system using the proposed model. However, the signal and process data cannot be easily obtained for experiments. Therefore, the experiment is limited within the dotted box illustrated in Figure 14.  
As mentioned before, the size of data for testing and validation were 50% each, and 1000 training epochs were adopted. After training the NN, the validation mean squared error (MSE) value for prediction was 0.031. Furthermore, the predicted initial solution value from the trained NN was 5.38. When ten is the maximum profit ratio without any constraints, the value of NN result means the best profit ratio under the constraints, such as cost, equipment capability, time, and so on. The basic experimental assumption was that the exact relationship and equation of the data were unknown. However, to verify the trained neural network, the original and experiment results were compared, as illustrated in Table 3. The normalization process can provide unified units for different units, such as percentages,  
Figure 14: Overview of experiment for the goal decision model. time, etc.