Informatica

In this paper, by unifying the dual roles of order-inducing variables, a PF weighted induced generalized weighted averaging (PFWIGOWA) operator is presented to facilitate the PF information. The key feature of the proposed operator is that it can improve the existing aggregation operators by the dual roles of its order-inducing variables. In addition, the PFWIGOWA’s desirable properties and different families are also discussed. Furthermore, an approach based on the developed operator is presented for solving multi-attribute group decision making (MAGDM) problems with PF information. Finally, the usefulness of the proposed method is illustrated in a research and development (R&D) projects selection problem.

A new method is proposed to solve the interactive group decision making problem in which the preference information takes the form of intuitionistic fuzzy preference relations. Firstly, we aggregate all individual intuitionistic fuzzy preference relations into a collective one. Then, a method to determine the experts’ weights by utilizing the compatibility measures of the individual intuitionistic fuzzy preference relations and the collective one is proposed. Furthermore, a practical interactive procedure is developed, in which the intuitionistic fuzzy association coefficient is used to rank the given alternatives. Finally, this study presents a numerical example to illustrate the availability of the developed approach and compare it to another method.

In this paper, we propose a new aggregation operator under uncertain pure linguistic environment called the induced uncertain pure linguistic hybrid averaging aggregation (IUPLHAA) operator. Some of the main advantages and properties of the new operator are studied. Moreover, in the situations where the given arguments about all the attribute weights, the attribute values and the expert weights are expressed in the form of linguistic labels variables, we develop an approach based on the IUPLHAA operator for multiple attribute group decision making with uncertain pure linguistic environment. Finally, an illustrative example is given to verify the developed approach and to demonstrate its feasibility and practicality.

The aim of this paper is to investigate intuitionistic fuzzy multiple attribute group decision making problems where the attribute values provided by experts are expressed in intuitionistic fuzzy numbers, and the weight information about the experts is to be determined. We present a new method to derive the weights of experts and rank the preference order of alternatives based on projection models. We first derive the weights of the decision makers according to the projection of the individual decision on the ideal decision. The expert has a large weight if his evaluation value is close to the ideal decision, and has a small weight if his evaluation value is far from the ideal decision. Then, based on the weighted projection of the alternatives on the intuitionistic fuzzy ideal solution (IFIS), we develop a straightforward and practical algorithm to rank alternatives. Furthermore, we extend the developed model and algorithm to the multiple attribute group decision making problems with interval-valued intuitionistic fuzzy information. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.

In this paper we develop a new method for 2-tuple linguistic multiple attribute decision making, namely the 2-tuple linguistic generalized ordered weighted averaging distance (2LGOWAD) operator. This operator is an extension of the OWA operator that utilizes generalized means, distance measures and uncertain information represented as 2-tuple linguistic variables. By using 2LGOWAD, it is possible to obtain a wide range of 2-tuple linguistic aggregation distance operators such as the 2-tuple linguistic maximum distance, the 2-tuple linguistic minimum distance, the 2-tuple linguistic normalized Hamming distance (2LNHD), the 2-tuple linguistic weighted Hamming distance (2LWHD), the 2-tuple linguistic normalized Euclidean distance (2LNED), the 2-tuple linguistic weighted Euclidean distance (2LWED), the 2-tuple linguistic ordered weighted averaging distance (2LOWAD) operator and the 2-tuple linguistic Euclidean ordered weighted averaging distance (2LEOWAD) operator. We study some of its main properties, and we further generalize the 2LGOWAD operator using quasi-arithmetic means. The result is the Quasi-2LOWAD operator. Finally we present an application of the developed operators to decision-making regarding the selection of investment strategies.