A hybrid model by clustering and evolving fuzzy rules for sales decision supports in printed circuit board industry

This research develops a hybrid model by integrating Self Organization Map (SOM) neural network, Genetic Algorithms (GA) and Fuzzy Rule Base (FRB) to forecast the future sales of a printed circuit board factory. This hybrid model encompasses two novel concepts: (1) clustering an FRB into different clusters, thus the interaction between fuzzy rules is reduced and a more accurate prediction model can be established, and (2) evolving an FRB by optimizing the number of fuzzy terms of the input and output variables, thus the prediction accuracy of the FRB is further improved. Numerical data of various affecting factors and actual demand of the past 5 years of the printed circuit board (PCB) factory are collected and inputted into the hybrid model for future monthly sales forecasting. Experimental results show the effectiveness of the hybrid model when comparing it with other approaches. However, the theoretical development of the validity of clustering an FRB into sub clusters remains to be proven.