Vendor selection using fuzzy C means algorithm and analytic hierarchy process

Abstract

Vendor selection is a strategic issue in supply chain management for any organization to identify the right supplier. Such selection in most cases is based on the analysis of some specific criteria. Most of the researches so far concentrate on multi-criteria decision-making analysis. Though many approaches have been proposed, analytic hierarchy process (AHP) is the most well known as it can deal with a very complex criteria structure. In AHP, the selected criteria are ranked and organized in a hierarchical order from generic to specific to formulate the problem. Though this order of ranking is acceptably logical, it incurs a huge computational complexity when a large number of alternatives are considered as the selection criteria. Moreover, the AHP may generate wrong selection due to computational error. To address these limitations, a novel model namely vendor selection using fuzzy c-means algorithm and analytic hierarchy process (VFA) is presented in this paper by integrating the fuzzy c-means clustering (FCM) algorithm with analytic hierarchy process (AHP). The outcome of the proposed VFA algorithm is compared with the basic AHP algorithm and VFA outperforms the basic AHP and reduces the computational complexity of AHP by a factor of 7.