Selecting a model for generating OWA operator weights in MAGDM problems by maximum entropy membership function

In this paper, we consider the assignment of ordered weighted averaging (OWA) operator weights to solve Multi-attributes Group Decision Making (MAGDM) problems with linguistic preference information. Since there are many available programming models that can be used for determining valid weights for attributes, choosing an appropriate OWA model for practical analysis is one of the key issues in MAGDM. Therefore, this research develops a novel approach that uses the prior OWA weights vectors and identifies an appropriate model by means of maximum entropy membership function from the a priori chosen OWA models to rank and/or evaluate alternatives. To develop the approach, we derive an analytic form for the maximum entropy membership function using the principle of maximum entropy and the Lagrange multipliers method. Then we present a bank branch example to demonstrate the applicability of our method.