Three-way Group Decisions Research Articles

Three-way group decisions using evidence theory under hesitant fuzzy linguistic environment

In the actual decision-making process, there will be situations where decision-makers with hesitant attitudes have difficulties in evaluating alternatives numerically, and hesitant fuzzy linguistic term sets can provide decision-makers with an effective way to describe hesitancy in linguistic terms. In multi-attribute group decision-making, each decision maker typically holds different preferences. If the variation in decision makers’ assessment weights across evaluations of each attribute for every alternative is not adequately accounted for, it can result in a problem of coarse-grained calculations, leading to information loss. Additionally, the three-way decision model faces significant challenges in information fusion within the context of the hesitant fuzzy linguistic environment. Therefore, we propose a new three-way decision-making model under the hesitant fuzzy linguistic environment. The model obtains the confidence of different decision makers in attribute evaluations through the fusion of D-S evidence theory, and can perform more fine-grained fusion calculations on the evaluation information of different decision makers. In addition, the model considers the cost function of each alternative in different decision-making actions under hesitant fuzzy linguistic environment, calculates the two thresholds of each alternative in the three-way decision model, and derives the decision rules. The effectiveness of the model is verified through a numerical example and two comparative experiments, therefore, the model can be applied in intelligent classification or recommendation systems of hesitant fuzzy linguistic information systems.

Three-way group decisions with evidential reasoning in incomplete hesitant fuzzy information systems for liver disease diagnosis

Three-way group decisions with evidential reasoning in incomplete hesitant fuzzy information systems for liver disease diagnosis

Exploring three-way group decisions with consensus evolution network for software ecosystem hierarchical criteria health rating

The appropriate rating of software ecosystem health is to the benefit of its sustainable development. The rating method based on an expert panel with specialised knowledge is reliable for rating software ecosystem health. Complexity often causes obstacles for expert panels to give appropriate ratings. Facing complexity, hierarchical criteria system provides evaluation guides with multiple levels and multiple perspectives. However, at present, effective group decision-making under hierarchical criteria system remains a challenge. Thus, this article systematically investigates consensus reaching process and rating method under hierarchical criteria system of software ecosystem health, which are two important components of group decision-making. To master the consensus situation of all criteria and consensus relationships of pair-wise experts under hierarchical criteria system, we construct hierarchical consensus evolution network. Then, we further propose a novel consensus degree measurement and design a consensus reaching process with pair-wise adjustment by utilising hierarchical characteristics. For rating software ecosystem health efficiently, there are usually three ratings including health, sub-health and ill-health. Three-way decisions exactly provide semantic interpretation for these three ratings. Meanwhile, three-way decisions can reduce the risk of incorrect ratings. Considering these advantages, we develop three-way group decisions with hierarchical consensus evolution network to comprehensively rate software ecosystem health under hierarchical criteria system. Finally, take the health rating of the software ecosystem on GitHub as an example, we develop a series of experiment analysis to verify the effectiveness of the proposed method.

Three-Way Group Decisions with Incomplete Spherical Fuzzy Information for Treating Parkinson’s Disease Using IoMT Devices

As Internet of Things (IoT) is extensively employed in diverse realistic areas, it is vital to effectively and timely analyze the data collected by IoT devices. To cope with this problem, by integrating adjustable MG SF probabilistic rough sets (PRSs) with the TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) method, this paper explores a three-way multi-attribute group decision-making (MAGDM) approach in the context of multigranulation (MG) spherical fuzzy (SF) incomplete information systems (IISs) and further applies the presented method to the analysis of leg muscle data obtained from Internet of Medical Things (IoMT) devices for Parkinson’s patients. First, the concept of MG SF IISs is established, and the completion method is provided. Then, adjustable MG SF PRSs are proposed for information fusion. Afterwards, considering the bounded rationality of decision-makers (DMs), a new three-way MAGDM method is designed by fusing adjustable MG SF PRSs with the TODIM method. Finally, in the context of IoMT-based detecting abnormal knee joints in Parkinson’s patients, the applicability and validity of the presented method are eventually verified.

Consensus reaching with the externality effect of social network for three-way group decisions

Three-way group decisions provide an efficient method to settle complex and high risk decision-making problems. To obtain reasonable decision results that satisfy different backgrounds and knowledge of decision makers, it is necessary to design a proper consensus reaching process (CRP) for loss functions of decision-theoretic rough sets (DTRSs). Unlike existing researches, this paper not only extends the group relationship among decision makers to the social network, but also considers the externality of social trust network in group decision making. In light of this idea, we design a new CRP with the externality of social network for three-way group decisions. In the CRP, the adjustment of a decision maker who is persuaded by the moderator can influence other decision makers to accordingly adjust evaluations. Thus, by using the linkage externality influence among decision makers, we establish a two-stage mixed 0–1 linear optimization consensus model for the determination of loss functions of DTRSs. Then, based on Bayesian decision procedure, we construct a complete decision procedure for three-way group decisions with social network. Finally, we apply our proposed method to assess desert locust invasion areas and verify its validity.

Multigranulation behavioral three-way group decisions under hesitant fuzzy linguistic environment

Bounded rationality of decision-makers and interaction among criteria plays a key role in the actual multi-criteria group decision-making (MCGDM) process. This paper aims to offer a decision approach to a MCGDM problem under hesitant fuzzy linguistic (HFL) uncertainty, by means of behavioral multigranulation decision-theoretic rough set (DTRS), where criteria interaction is handled by Choquet integral, and decision-makers’ psychological preferences are considered by prospect theory. In specific, we firstly define the behavioral DTRS over two universes, where the threshold parameters are obtained by maximizing expected utility, rather than minimizing expected risk. Then, from the perspective of the difference of alternatives, we develop the calculation methods of loss and benefit functions, and provide the determination of the overall threshold parameters by Choquet integral, in consideration of criteria interaction. With these discussions, the behavioral multigranulation DTRS over two universes in the HFL context is proposed, and the two special cases, i.e., optimistic and pessimistic versions are also discussed. Thus, solutions to a MCGDM issue, green supplier selection, by utilizing the proposed behavioral multigranulation HFL DTRS over two universes, are designed, along with key steps and algorithm. Ultimately, an illustrative example is given to elaborate the practicability and feasibility of our presented model.

Three-way group decisions based on multigranulation hesitant fuzzy decision-theoretic rough set over two universes

Three-way group decisions based on multigranulation hesitant fuzzy decision-theoretic rough set over two universes

Three-way group decisions under hesitant fuzzy linguistic environment for green supplier selection

Purpose The purpose of this paper is to propose a three-way group decision-making approach to address the selection of green supplier, by extending decision-theoretic rough set (DTRS) into hesitant fuzzy linguistic (HFL) environment, considering the flexible evaluation expression format of HFL term set (HFLTS) and the idea of minimum expected risk in DTRS. Design/methodology/approach Specifically, the authors first present the calculation method of the conditional probability and discuss the loss functions of DTRS with HFL element (HFLE), along with some associated properties being investigated in detail. Further, three-way group decisions rules can be deduced, followed by the cost of every green supplier candidate. Thus, based on these discussions, a novel green supplier selection DTRS model that combines multi-criteria group decision-making (MCGDM) and HFLTS is designed. Findings A numerical example of green supplier selection, the comparative analysis and associated discussions are conducted to illustrate the applicability and novelty of the presented model. Practical implications The selection of green supplier has played a critically strategic role in sustainable enterprise development due to continuous environmental concerns. This paper offers an insight for companies to select green supplier selection from the perspective of three-way group decisions. Originality/value This paper uses three-way decisions to address green supplier selection in the HFL context, which is considered as a MCGDM issue.

Three-Way Group Decision in a Covering Decision Information System

Three-way decision (3WD) provides a new perspective for solving practical decision-making problems, which is in line with human's cognitive pattern. A covering information system (CIS) is an information system (IS) that consists of multiple coverings in the universe. A CIS with decision attributes which is seen as a covering decision information system (CDIS). This paper proposes three-way group decisions in a CDIS, as well as gives its application on the problem of position competition. First of all, the neighbourhood of every point in a CDIS is defined, and corresponding similarity class of this point is also obtained. Then, because of the uncertainty of risks, loss functions are acquired through group decision-making by means of interval numbers. Next, a method of three-way group decisions in a CDIS is presented. Eventually, the position competition is presented as an example to support our proposed decision-making method.

Three-way group decisions with interval-valued decision-theoretic rough sets based on aggregating inclusion measures

The main purpose of three-way decisions with interval-valued decision-theoretic rough sets is to make decisions by minimizing interval-valued expected loss functions. Since the set of interval-valued expected losses is not a totally ordered set, a new mechanism is presented to make three-way group decisions with interval-valued decision-theoretic rough sets by calculating inclusion measures between two arbitrary interval-valued expected loss functions. Firstly, based on conjunctive and disjunctive semantics of intervals, inclusion measures are proposed based on the partial orders via different semantics, respectively. Secondly, the framework of three-way decisions with interval-valued decision-theoretic rough sets is presented based on inclusion measures of intervals. Thirdly, three-way decisions with interval-valued decision-theoretic rough sets are extended to group decisions. To improve the precision of interval-valued loss evaluation for the final group decisions, we divide interval-valued expected loss functions for each expert into several trivial intervals, then the matrix of inclusion measures for interval-valued expected loss functions between every two experts is obtained by aggregating all the inclusion measures. Fourthly, we employ the methodology of three-way decisions with interval-valued decision-theoretic rough sets and select rules with minimal costs or risks to obtain the optimal decision rules of group decisions. To highlight the performance of our methodology, by using score functions to transform an interval into a real one we introduce another method based on the principle of justifiable granularity to obtain decision rules of group decisions. Finally several data sets are employed to evaluate our methodology.

Rough Number—Based Three-Way Group Decisions and Application in Influenza Emergency Management

Group decision-making can effectively deal with complex decision problems in reality and takes important research status in the field of decision-making. In recent years, three-way decision has been a hot topic in the field of uncertain decision-making, so the models of three-way decision under group environment have become a new direction and problem in decision-making research. Based on the subjectivity and uncertainty of the group judgment, this paper adopts the rough number method to objectively and effectively integrate the group information so as to establish rough number—based three-way decision models. First of all, we use rough numbers to transfer loss functions of individuals into a rough number—based gathering loss function, and verify the rough number—based loss function meets the general characteristics of general loss functions. Then from the perspectives of optimism, pessimism, risk preference of decision-makers, and directly operation of rough numbers, we explore the calculation of thresholds and the acquisition method of three-way rules, and then establish rough number—based three-way group decision models. Finally, influenza emergency management is introduced to verify the effectiveness and advancement of the novel method.

The determination of three-way decisions with decision-theoretic rough sets considering the loss function evaluated by multiple experts

One of the main aims of a theory of three-way decisions is to model a cohort of human ways of dealing with problems and how to go about information processing considering applications in designing and implementing intelligent systems. Three-way decision with decision-theoretic rough sets is a new model characterized by the conditional risk, the loss functions and the minimum-risk decision rules. We provide a three-way decisions approach using loss functions evaluated by multiple experts based on the Bayesian decision theory. In this paper, we propose the methodologies of the aggregation of loss functions of decision-theoretic rough sets under group decision-making environment. Then we further compare them with that of the existing papers, which focus on the aggregation of the thresholds. We also construct basic model of three-way group decisions. Finally, an empirical study of evaluating the loss functions validates the reasonability and effectiveness of the proposed models. The contributions of the study are as follows: group decision-making is regarded as a knowledge based, which makes the decision more convincing as compared to single-person methods. Considering the different situations, we mainly design the corresponding aggregation operators of loss functions. Our new approach provides the methodologies of the aggregation of loss functions which in turn are used to compute the thresholds for the determination or construction of the basic rules of three-way group decision-making.

Three-way group decisions based on prospect theory

The three-way decision method is a new developing uncertain decision theory. The method divides a set of decision alternatives into three regions, called the acceptance, rejection, and uncertainty regions constructed from a pair of thresholds. However, seldom researches about three-way decisions take psychological attitudes and preferences of the decision-makers into consideration. In this paper, we propose a novel three-way group decision method, which considers the psychological attitudes from the decision-maker. Firstly, we provide the concept of three-way group decisions. The three-way approximation is constructed by many decision-makers with respect to a single critical value. The objects are still divided into acceptance, rejection, and uncertainty regions, respectively. Then, for the proper region (uncertain region, in general), we propose a decision method, which considers the psychological preferences through fusing prospect theory into three-way group decision methods. The alternatives in the proper region are ranked in accordance with their weighted prospect values. Finally, the optimal choices are made. The decision steps are presented in detail, and the practicality of the proposed methods is illustrated through an example.

Three-way group decisions with decision-theoretic rough sets

On consideration of the effectiveness of group decision making (GDM) in practical complex decision problems, we introduce GDM into three-way decisions with decision-theoretic rough sets (DTRSs) and propose GDM-based three-way decisions. GDM-based three-way decisions extend the range of applications of three-way decisions with DTRSs and provide a novel interpretation of the determination of loss functions. Based on DTRSs, we firstly focus on analyzing the determination for the loss functions under the GDM environment. With the aid of the principle of justifiable granularity, we adopt the important and majority suggestions of experts to measure each loss function, which supports a coherent way of designing information granules in presence of numerics. In this case, the loss functions are determined in the form of interval-valued information granule. By using the interval comparison method, we further deduce the three-way decisions and design a corresponding decision procedure of GDM-based three-way decisions. Then, an example of strategy supply selection is given to elaborate the GDM-based three-way decisions. Finally, we validate the performance of our proposed method by experimental analysis.