The K MPCA hybrid algorithm to selection the technology using clustering and principal component analysis

Choosing the right technology is an important issue faced by manufacturing and industrial companies. This is while the access to new technologies has widened the selection set so that solving the problem of technology selection has become more difficult and complicated despite multiple decision criteria. On the other hand, appropriate technology can create significant competitive advantages for a company in a complex business environment. So far, various methods have been presented to solve the problem of technology selection, each of which has advantages and disadvantages, but none of the proposed methods have all the necessary capabilities. In this article, by using the method of principal components analysis, the combined method of clustering and analysis of principal components along with fuzzy theory, the combined KMPCA algorithm has been developed in solving the technology selection problem. The number of technology selection variables was increased from 6 to 14 variables with a more complete coverage of decision dimensions, and data from 49 currently technologies of Iran's stone industry were collected and tested in the model. The results of this research, while improving the solution, show the reduction of the dimensions of the problem and the reduction of multi-collinear relationships between the data in the technology selection process.