LAUSR

In recent years, PROMETHEE II has emerged as one of the most efficient and successfully applied outranking method in multi-criteria decision making (MCDM) problems. Conventional PROMETHEE II method presumes that the weights of the criteria are known beforehand, and this is rather a stringent assumption. Moreover, the number of preference indices to be evaluated in the method grows enormously with an increase in the number of alternatives to be ranked. In this work, we look at these concerns and propose a methodology inspired by PROMETHEE II that identifies the best criteria first and then, the preference indices are computed with respect to these criteria only. The algorithm is customized in such a way that it curtails the computational time complexity of PROMETHEE II to a great extent. Here, a large number of alternatives-criteria evaluation matrices are simulated with random numeric data to illustrate the proposed method and evaluate its performance. Statistical analysis on the stability of the rankings and the conformity measure proves that the proposed approach may be preferred over PROMETHEE II and TOPSIS methods. Rank correlation coefficients are evaluated, and they indicate the efficacy of the proposed method.