A fuzzy TOPSIS model via chi-square test for information source selection
The Information Source (IS) selection involves various aspects with different requirements under indeterminate conditions. It is such a complicated process pertaining to seeking for the most appropriate solution that how to resolve the constraint resources needs to be congruously considered. This paper proposes a Multi-Criteria Group Decision Making (MCGDM) model, which uniforms the quantitative and qualitative factual value of different attributes with trapezoidal fuzzy numbers. Analytic Hierarchy Process (AHP) and Entropy Weights (EW) are integrated to alleviate the conflicts by experts intuitions and provide the accurate weight vector in this model. Besides, the Euclidean Distance (ED) is substituted by the Value of Chi-Square Test (VCST) to refine the Relative Closeness (RC), which theoretically excluded the potential bias arising from relative importance of the two types of distances, in a revised Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The optimal recommendation compromises in a social decision making way. Finally, the software named Evaluator, which is based on the presented model, is illustrated to show how it can be practically used for IS selection with comparative analysis.
topsis chi-square test trapezoidal fuzzy number analytic hierarchy process entropy weights