Strategic decision-making in uncertain environments

El proceso de toma de decisiones tiene una incidencia relevante en los resultados de las empresas, lo que ha llevado a desarrollar novedosos métodos que permitan evaluar bajo condiciones no controlables elementos subjetivos y racionales. En ese sentido, el objetivo principal de este trabajo estudia los operadores de agregación en la toma de decisiones en entornos inciertos. Se presentan dos metodologías que permiten agregar información, que se llaman operadores OWA y BON-OWA. La aplicación de estos operadores se realiza en la selección de lanzamiento de nuevos productos. La principal ventaja de estos operadores es que permiten capturar la actitud del decisor y la comparación e interrelación continua de la información. Así, se destaca el análisis holístico que ofrecen estos métodos sobre la toma de decisiones en incertidumbre, que permite integrar conceptos de la teoría administrativa y la teoría de la agregación en un caso aplicado, visualizando como la inclusión de la información genera cambios dentro de los rankings de selección de alternativas.Decision-making has a relevant incidence on firms results, which has led to develop novel methods that allow assessing subjective and rational elements under uncontrollable conditions. In this sense, the main aim of this work is to study aggregation operators in decision-making in uncertain environments. Two methodologies allow aggregating information are presented, which are called OWA and BON-OWA operators. The application of these operators is made in the selection of new product release. The main advantage of these operators is that they allow to capture the attitude of the decision maker and the continuous comparison and interrelation of the information. Thus, the holistic analysis offered by these methods on decision making in uncertainty is highlighted, which allows to integrate concepts of administrative and aggregation theories in an applied case, visualizing how the inclusion of information generates changes within the selection of alternatives.Universidad Pablo de Olavid

Dual hesitant fuzzy aggregation operators

Dual hesitant fuzzy sets (DHFSs) is a generalization of fuzzy sets (FSs) and it is typical of membership and non-membership degrees described by some discrete numerical. In this article we chiefly concerned with introducing the aggregation operators for aggregating dual hesitant fuzzy elements (DHFEs), including the dual hesitant fuzzy arithmetic mean and geometric mean. We laid emphasis on discussion of properties of newly introduced operators, and give a numerical example to describe the function of them. Finally, we used the proposed operators to select human resources outsourcing suppliers in a dual hesitant fuzzy environment. First published online: 11 Sep 201

Intuitionistic fuzzy generalized probabilistic ordered weighted averaging operator and its application to group decision making

In this paper, we present the intuitionistic fuzzy generalized probabilistic ordered weighted averaging (IFGPOWA) operator. It is a new aggregation operator that uses generalized means in a unified model between the probability and the OWA operator. The main advantage of this new operator is that it is able to deal with probabilities (objective information) and ordered weighted averages (subjective information) in the same formulation. Moreover, it is also able to deal with uncertain environments that can be assessed with intuitionistic fuzzy numbers. Furthermore, it uses generalized means providing a very general formulation that includes a wide range of situations. We study some of its main properties and particular cases such as the generalized intuitionistic fuzzy ordered weighted averaging (GIFOWA) operator and intuitionistic fuzzy probabilistic ordered weighted averaging (IFPOWA) operator. We end the paper by applying the new operator to a group decision making problem concerning the selection of investments. First published online: 26 Jun 201

A new measure of volatility using induced heavy moving averages

The volatility is a dispersion technique widely used in statistics and economics. This paper presents a new way to calculate volatility by using different extensions of the ordered weighted average (OWA) operator. This approach is called the induced heavy ordered weighted moving average (IHOWMA) volatility. The main advantage of this operator is that the classical volatility formula only takes into account the standard deviation and the average, while with this formulation it is possible to aggregate information according to the decision maker knowledge, expectations and attitude about the future. Some particular cases are also presented when the aggregation information process is applied only on the standard deviation or on the average. An example in three different exchange rates for 2016 are presented, these are for: USD/MXN, EUR/MXN and EUR/USD