A multi-criteria group decision making model for knowledge management system selection based on TOPSIS with multiple distances in fuzzy environment

Abstract

Purpose – The purpose of the paper is to develop a model for the selection of knowledge management system (KMS), in which the assessment criteria are defined and the TOPSIS method with multiple distances in fuzzy environment is proposed. Design/methodology/approach – First, the paper establishes the evaluation criteria from functional, performance and economic aspects. Second, a new TOPSIS method is proposed to deal with the linguistic evaluation information. In the proposed method, in order to eliminate the bias of TOPSIS with single distance, six kinds of distances that are commonly used in TOPSIS including Hamming distance, Euclidean distance, Dp,q distance, Hausdorff distance, L2 distance and vertex distance are extended in fuzzy environment and employed in the TOPSIS to generate six independent pre-rankings. Afterwards these pre-rankings are combined by Condorcet method to generate the final joint ranking. Findings – Since the final ranking is the collective result, the bias in each single pre-ranking is eliminated and the selection is more objective and accurate. The example shows the proposed model is practical. Research limitations/implications – The linguistic preferences are given in the single granularity linguistic information. Practical implications – The proposed model can be applied as a tool for decision makers in the evaluation and selection of KMS. Originality/value – The paper gives an overall evaluation of KMS and proposes the new TOPSIS method with multiple distances in fuzzy environment.