Yager considered the problem of maximizing an OWA aggregation of a group of variables that are interrelated and constrained by a collection of linear inequalities and he showed how this problem can be modelled as a mixed integer linear programming problem. In this short communication we show a simple algorithm for exact computation of optimal solutions to a constrained OWA aggregation problem w...

In this paper we introduce the semi-uninorm based ordered weighted averaging (SUOWA) operators, a new class of aggregation functions that, as WOWA operators, simultaneously generalize weighted means and OWA operators. To do this we take into account that weighted means and OWA operators are particular cases of Choquet integral. So, SUOWA operators are Choquet integral-based operators where thei...

The OWA operators are traditionally used in the context of decision making as the means to aggregate the satisfaction of single criteria into a overall preference index. They belong to the class of averages, entailing compensatory properties. Differently, t-norms and t-conorms entail the reinforcement property, since there is a kind of interaction between the degree of criteria satisfaction. A ...

We analyze different types of aggregation operators. We focus on the generalized OWA (GOWA) operator and the Quasi-OWA operator which generalize a wide range of aggregation operators. We also analyze a recent approach that unifies the weighted average and the OWA operator in the same formulation called ordered weighted averaging – weighted averaging (OWAWA) operator. We introduce the fuzzy gene...

One important issue in the theory of Ordered Weighted Averaging (OWA) operators is the determination of the associated weights. One of the first approaches, suggested by O’Hagan, determines a special class of OWA operators having maximal entropy of the OWA weights for a given level of orness; algorithmically it is based on the solution of a constrained optimization problem. In this paper, using...

We develop a new decision making model based on the use of a unified approach between the probability, the weighted average and the ordered weighted averaging (OWA) operator in an uncertain environment that can be assessed with fuzzy numbers (FNs). We present a new aggregation operator called the fuzzy probabilistic ordered weighted averaging – weighted averaging (FPOWAWA) operator. We study it...

OWA operators, introduced by Yager, are very important non linear aggregation functions in both academic studies and a myriad of applications. In this study, we use two dimensional OWA aggregation function into pedagogical evaluation practice, which will involve the preferences and experiences of decision makers and teachers. In addition, we also introduce a long time educational evaluation mod...

In this paper we introduce a class of linear orders for discrete gradual real numbers. Based on the linear orders we propose an OWA operator on the set of discrete gradual numbers and discuss some its properties. This is a first step of our intentions to introduce a class of linear orders, and consequently also OWA operator, for the set of discrete gradual intervals. Because the set of all fuzz...

In practical term any result obtained using an ordered weighted averaging (OWA) operator heavily depends upon the method to determine the weighting vector. Several approaches for obtaining the associated weights have been suggested in the literature, in which none of them took into account the preference of alternatives. This paper presents a method for determining the OWA weights when the pref...

The basic operations for combining real values in the frame of decision analysis are the Weighted Mean (WM) and the Ordered Weighted Averaging operator (OWA). The weighted mean allows the system to compute an aggregate value from the ones coming from several sources, taking into account the reliability of each information source. Alternatively, the OWA operator allows the user to weight the val...