Assessing Integrated TOPSIS Model with Exponential Intuitionistic Entropy Measure: A Case Study

Abstract

Solving multi-criteria decision-making (MCDM) problems requires assigning weights to the problem’s criteria, the determination of criteria weights could be subjectively or objectively. Many studies emphasized the effectiveness of using objective techniques to derive criteria weights, like using entropy as a measure of fuzziness of the fuzzy sets to detect criteria weights. Despite that, still this subject under study and debate between researchers. This is due to the importance and effect of the criteria weights on the final results. The proposed MCDM method integrates the TOPSIS approach with the intuitionistic fuzzy entropy measure in exponential form, aiming to have an MCDM method that is simple to be implemented and compensate the need to determining the criteria weights. In this paper, the process of the new MCDM method is introduced, with two practical examples to demonstrate the simplicity of the proposed method, and to prove its effectiveness without the need to determine the attribute weights. At the end of each example, a comparison table is provided to benchmark the generated result from the new method with the results from other comparable methods.