An MCDM model based on KL-AHP and TOPSIS and its application to weapon system evaluation

Abstract

In this paper, we propose a multiple criteria decision-making (MCDM) model based on the proposed KL-AHP and TOPSIS methods, where the KL-AHP method combines analytic hierarchy process (AHP) and Kullback-Leibler divergence (KLD) to determine the relative weights of decision criteria, and the technique for order performance by similarity to ideal solution (TOPSIS) is used to obtain the final rankings of the feasible alternatives. It is reasonable that both subjective judgment and objective information hidden in practical data should be taken into account in decision-making. Therefore, we use AHP to introduce the subjective judgment of decision makers and apply KLD to extract the information of practical data. Then, integrating the subjective judgment and objective information, a new method for determining the relative weights of decision criteria, called KL-AHP (Kullback, Leibler, and AHP), is proposed. For the rankings of the feasible alternatives, the rational and understandable TOPSIS method is employed. Finally, we construct a numerical example for evaluating fighter planes to illustrate our proposed model.