Social network analysis based approach to group decision making problem with fuzzy preference relations

Leveraging social network trust relationships among experts to reach consensus has become a popular topic in linguistic group decision making (GDM). However, in linguistic contexts, it is commonly accepted that words mean different things for different people, which indicates the necessity of modeling experts’ personalized individual semantics (PISs). Moreover, experts sometimes may show non-cooperative behaviors during the consensus reaching process (CRP) due to their own interests. As a result, this paper focuses on developing a consensus reaching algorithm with non-cooperative behavior management for PIS-based social network GDM problems. First, linguistic preference relations are transformed into fuzzy preference relations by the PIS model, and then social network analysis techniques are used to obtain experts’ weight vector. Afterwards, we propose a feedback adjustment mechanism to improve experts’ adjustment willingness in CPRs, in which the trust relationships and the PISs of experts are utilized to generate adjustment advice for experts. Furthermore, a non-cooperative behavior management mechanism which dynamically adjusts the trust degrees in social network is devised. Followed by this, a numerical example is provided to demonstrate the proposed algorithm. Finally, detailed simulation results are presented to analyze the influence of different parameters on CRPs and illustrate the validity of the proposed algorithm.

Consistency and consensus study for group decision making (GDM) with fuzzy preference relations (FPRs) are necessary, which have a decisive impact on the quality of decision results. Consistency can prevent self-contradictory judgments, and consensus limits the deviation among individual judgments within the preset threshold. Because acceptably multiplicative consistency still cannot avoid self-contradictory judgments, this paper considers the ordinal consistency and acceptably multiplicative consistency simultaneously. In view of these aspects, we introduce a new optimal model-based method for GDM with FPRs. As minimum total adjustment and minimum individual adjustment may be inconsistent, we introduce the Solidarity consistency and consensus adjustment mechanism (So-CCAM) to allocate the minimum total adjustment, which fully considers the cooperation and solidarity among decision makers. When the So-CCAM does not exhibit stability, we present the nucleolus consistency and consensus adjustment mechanism (Nu-CCAM), which is the best element in the core according to lexicographical order. After that, an algorithm is offered. To demonstrate the application and efficiency of the new method, an illustrative example is offered, and a comparison is carried out. It offers the first optimization models-based method for GDM with FPRs in view of ordinal consistency, acceptably multiplicative consistency, and consensus analysis. Further, it provides two rational consistency and consensus adjustment mechanisms by cooperative game theory.

The ordinal consistency of a fuzzy preference relation

The breaking of additively reciprocal property of fuzzy preference relations and its implication to decision making under uncertainty

The paper addresses the group decision‐making (GDM) problem involving hesitant‐intuitionistic fuzzy preference relations (HIFPRs), focusing on additive consistency and group consensus analysis. Firstly, to preserve the original decision information effectively, the consistency improvement algorithm is designed to identify decision information requiring modification and subsequently execute a localized adjustment process. Secondly, an algorithm is developed to derive HIFPRs that satisfy both acceptable consistency and group consensus simultaneously. Additionally, an approach for calculating decision‐maker weights is proposed. Finally, to address GDM challenges within hesitant‐intuitionistic fuzzy scenarios, a novel decision method is presented. The validity of the proposed GDM approach is demonstrated through a case study involving the selection of outstanding students for exchange learning. Comparative analysis illustrates the method’s rationale and advantages.

Multi criteria decision making (MCDM) and group decision making (GDM) are two well practiced approaches to solve decision making problems. Group decision making combines opinions from Decision makers (DMs) into a coherent group decision. The aim of this paper is to develop a new fuzzy group decision making algorithm and apply it to a real-world groundwater development problem to select the most preferred alternative. Five decision makers (DMs) are selected to express their judgments about relative importance of 3 criteria (water pumped, cost, and risk) and have given their preferences as ordinal preferences, utility values, fuzzy preference relations and multiplicative preference relations. Decision makers' opinions are aggregated based on a fuzzy group decision making. Ordered weighted averaging (OWA) operator has been used to aggregate decision makers' opinions. Consensus measure is also defined to evaluate convergence between group and individual opinions. The outcomes of the proposed algorithm show that proposed fuzzy group decision making process is a relevant approach to aggregate the individual expert's opinion and is a desirable tool to reach consensus among DMs.

Additive consistent interval-valued Atanassov intuitionistic fuzzy preference relation and likelihood comparison algorithm based group decision making

A group decision-making model based on incomplete comparative expressions with hesitant linguistic terms

A fuzzy multi-criteria decision-making model by associating technique for order preference by similarity to ideal solution with relative preference relation

Netnograghy as a new approach in social networks and Internet communications analyzes customers' suggestions and opinions in the form of comments. Making use utilization of quantitative techniques in order to analyze, detect the meaning and recognize the signals of mainly incomplete opinions of customers is vital for managers. In this paper, firstly by the aid of grounded theory method, the meaning and important criterions of user comments in mobile shopping sites have been recognized and then these criterions have been graded based on decision making by experts and fuzzy preference relations with incomplete information. Analyze of users' comments proved six parameters: price, beauty, process speed, screen quality, battery as well as camera are significant, and among them, based on fuzzy relations with incomplete approach, beauty with value of 0.27563 is of utmost importance for users

On the basis of geometric consistency (GCI) of multiplicative preference relations and the transformation relation between multiplicative preference relations and fuzzy preference relation, inconsistency measure of fuzzy preference relation and the thresholds associated with it are studied. A new satisfactory consistency concept of fuzzy preference relation is defined. For a fuzzy preference relation, which does not possess satisfactory consistency, an improving consistency approach is provided. Then, programming models for estimating interval priority weights from fuzzy preference relations is established. Finally, an example is given to illustrate the validity and practicality of the proposed methods.

Considering the influence of undesirable outputs in environmental assessments of the practical production process, this paper proposes a DEA cross-efficiency method and fuzzy preference relation based on semi-disposability of undesirable outputs for environmental assessments, which combines self-evaluation with peer-evaluation and avoids potentially unrealistic weighting scheme. We first develop a new input-oriented DEA model based on semi-disposability of undesirable outputs. Then, the corresponding DEA cross-efficiency method is proposed to evaluate the homogeneous decision-making units (DMUs) and the additive fuzzy preference relation is constructed using the DEA cross-efficiency scores. An output-oriented DEA model is adopted to derive the priority vector from the constructed additive fuzzy preference relation. Furthermore, we summarize the specific procedures for environmental assessments based on DEA cross-efficiency and fuzzy preference relation. Finally, the practical example of industrial environment assessment of the Yangtze River Economic Belt is provided to show the applicability and effectiveness of the proposed method.

Group decision making with preference information on alternatives is an interesting and important research topic which has been receiving more and more attention in recent years. The purpose of this paper is to investigate multiple-attribute group decision-making (MAGDM) problems with distinct uncertain preference structures. We develop some linear-programming models for dealing with the MAGDM problems, where the information about attribute weights is incomplete, and the decision makers have their preferences on alternatives. The provided preference information can be represented in the following three distinct uncertain preference structures: 1) interval utility values; 2) interval fuzzy preference relations; and 3) interval multiplicative preference relations. We first establish some linear-programming models based on decision matrix and each of the distinct uncertain preference structures and, then, develop some linear-programming models to integrate all three structures of subjective uncertain preference information provided by the decision makers and the objective information depicted in the decision matrix. Furthermore, we propose a simple and straightforward approach in ranking and selecting the given alternatives. It is worth pointing out that the developed models can also be used to deal with the situations where the three distinct uncertain preference structures are reduced to the traditional ones, i.e., utility values, fuzzy preference relations, and multiplicative preference relations. Finally, we use a practical example to illustrate in detail the calculation process of the developed approach.

User community detection via embedding of social network structure and temporal content

This paper reflects the results of research analyzing models of multi-attribute decision-making based on fuzzy preference relations. Questions of constructing the corresponding multi-attribute models to deal with quantitative information concomitantly with qualitative information based on experts’ knowledge are considered. Human preferences may be represented within the fuzzy preference relations and by applying diverse other preference formats. Considering this, so-called transformation functions reduce any preference format to fuzzy preference relations. This paper’s results can be applied independently or as part of a general approach to solving a wide class of problems with fuzzy coefficients, as well as within the framework of a general scheme of multi-criteria decision-making under conditions of uncertainty. The considered techniques for fuzzy preference modeling are directed at assessing, comparing, choosing, prioritizing, and/or ordering alternatives. These techniques have served to develop a computing system for multi-attribute decision-making. It has been implemented in the C# programming language, utilizing the “.NET” framework. The computing system allows one to represent decision-makers’ preferences in one of five preference formats. These formats and quantitative estimates are reduced to nonreciprocal fuzzy preference relations, providing homogeneous preference information for decision procedures. This paper’s results have a general character and were applied to analyze power engineering problems.