A Multiple Attribute Group Decision Making Method Based on 2-D Uncertain Linguistic Weighted Heronian Mean Aggregation Operator

2-Dimension uncertain linguistic variables can describe both subjective evaluation result of attributes and reliability of the evaluation results in multiple attribute decision making problems. However, it is difficult to aggregate these evaluation information and give comprehensive results. Heronian mean (HM) has the characteristic of capturing the correlations between aggregated arguments and is extended to solve this problem. The 2-dimension uncertain linguistic weighted HM aggregation( 2DULWHMA) operator is employed in this paper. Firstly, the definition, properties, expectations and the operational laws the 2-dimension uncertain linguistic variables are investigated. Furthermore, the properties of the 2DULWHMA operators, such as commutativity, idempotency and monotonicity, etc. are studied. Some special cases of the generalized parameters in these operators are analyzed. Finally, an example is given to demonstrate the effectiveness and feasibility of the proposed method

A novel approach to multi-attribute group decision-making based on interval-valued intuitionistic fuzzy power Muirhead mean

This paper focuses on multi-attribute group decision-making (MAGDM) course in which attributes are evaluated in terms of interval-valued intuitionistic fuzzy (IVIF) information. More explicitly, this paper introduces new aggregation operators for IVIF information and further proposes a new IVIF MAGDM method. The power average (PA) operator and the Muirhead mean (MM) are two powerful and effective information aggregation technologies. The most attractive advantage of the PA operator is its power to combat the adverse effects of ultra-evaluation values on the information aggregation results. The prominent characteristic of the MM operator is that it is flexible to capture the interrelationship among any numbers of arguments, making it more powerful than Bonferroni mean (BM), Heronian mean (HM), and Maclaurin symmetric mean (MSM). To absorb the virtues of both PA and MM, it is necessary to combine them to aggregate IVIF information and propose IVIF power Muirhead mean (IVIFPMM) operator and the IVIF weighted power Muirhead mean (IVIFWPMM) operator. We investigate their properties to show the strongness and flexibility. Furthermore, a novel approach to MAGDM problems with IVIF decision-making information is introduced. Finally, a numerical example is provided to show the performance of the proposed method

Hesitant fuzzy multi-criteria decision making methods based on Heronian mean

Among several extensions of fuzzy set theory, the concept introduced by Torra and Narukawa (2009) in defining hesitant fuzzy set is interesting and practical. In this paper we introduce and study new methods for dealing with MCDM (multi-criteria decision making) problems under the hesitant fuzzy environment. First, we propose and discuss the notion of hesitant fuzzy Heronian mean operators. By using these operators, we can portray the relationship of the criteria effectively. Then, the numerical examples are provided and comparative analyses with other aggregation operators are not neglected. Furthermore, the weighted forms of the hesitant fuzzy Heronian mean operators are defined for MCDM problem, based on which, new MCDM methods are proposed. The MCDM methods presented in this paper can provide an effective manner to assist the decision maker in making his/her decision. An example about dormitory construction projection selection is given to show the effectiveness of the proposed method. First published online: 04 Nov 201

Intuitionistic fuzzy prioritized operators and their application in multi-criteria group decision making

Intuitionistic fuzzy set is a very useful tool to depict uncertainty. Lots of multi-criteria group decision making methods under intuitionistic fuzzy environment have been developed. Current methods are under the assumption that the criteria and the decision makers are at the same priority level. However, in real group decision making problems, criteria and decision makers have different priority level commonly. In this paper, multi-criteria group decision making problems where there exists a prioritization relationship over the criteria and decision makers are studied. First, the intuitionistic fuzzy prioritized weighted average (IFPWA) and the intuitionistic fuzzy prioritized weighted geometric (IFPWG) operators are proposed. Then, some of their desirable properties are investigated in detail. Furthermore, the procedure of multi-criteria group decision making based on the proposed operators is given under intuitionistic fuzzy environment. Finally, a practical example about talent introduction is provided to illustrate the developed method

Pythagorean fuzzy Muirhead mean operators in multiple attribute decision making for evaluating of emerging technology commercialization

In today’s world, with the advancement of technology, several emerging technologies are coming. Faced with massive emerging technologies which are the component of the technology pool, how to identify the commercial potential of emerging technologies in theory and practice is an important problem. The scientific approach to the selection of these emerging technologies is one of the main objectives of the research. In this paper, we extend Muirhead mean (MM) operator and dual MM (DMM) operator to process the Pythagorean fuzzy numbers (PFNs) and then to solve the multiple attribute decision making (MADM) problems. Firstly, we develop some Pythagorean fuzzy Muirhead mean operators by extending MM and DMM operators to Pythagorean fuzzy information. Then, we prove some properties and discuss some special cases with respect to the parameter vector. Moreover, we present some new methods to deal with MADM problems with the PFNs based on the proposed MM and DMM operators. Finally, we verify the validity and reliability of our methods by using an application example for potential evaluation of emerging technology commercialization, and analyze the advantages of our methods by comparing with other existing method

Fuzzy Techniques for Decision Making 2018

Zadeh's fuzzy set theory incorporates the impreciseness of data and evaluations, by imputting the degrees by which each object belongs to a set. Its success fostered theories that codify the subjectivity, uncertainty, imprecision, or roughness of the evaluations. Their rationale is to produce new flexible methodologies in order to model a variety of concrete decision problems more realistically. This Special Issue garners contributions addressing novel tools, techniques and methodologies for decision making (inclusive of both individual and group, single- or multi-criteria decision making) in the context of these theories. It contains 38 research articles that contribute to a variety of setups that combine fuzziness, hesitancy, roughness, covering sets, and linguistic approaches. Their ranges vary from fundamental or technical to applied approaches