Group Decision-making Model Research Articles

Connections between Campos-Bolanos and Murofushi–Sugeno Representations of a Fuzzy Measure

Nonadditivity of a fuzzy measure, as an indicator of defectiveness, makes a fuzzy mea-sure less useful in applications compared to additive, probabilistic measures. In order to neutralize this indicator of defectiveness to some degree, it is important to study the representations of fuzzy measures, including, in particular, additive, probabilistic representations. In this paper, we discuss a couple of probability representations of a fuzzy measure: the Campos-Bolanos representation (CBR) and the Murofushi–Sugeno representation (MSR). The CBR is mainly represented by the Associated Probability Class (APC). The APC is well studied and the aspects of its use can be found in many interesting studies. This is especially true for the environment of interactive attributes in their identification and multi-attribute group decision-making (MAGDM) models, related to the attributes’ Shapley values and interaction indexes. The MSR is a less-used tool in practice today. The main motivation of the research presented here was to explore the connections between these two representations, which will help increase the usability of the MSR in practice in the future. In the MSR, we constructed the nonequivalent representation class (NERC) of a fuzzy measure. This probabilistic new representation is somewhat similar to the APC in the CBR environment. The proposition on the existence of the MSR induced by the CBR was proven. The presented formula of the APC by the NERC was obtained. The duality property of fuzzy measures for the CBR is well studied with respect to fuzzy measures—Choquet second-order dual capacities. Significant properties were proven for the representation of a monotone expectation (ME) under the NERC conditions: as is known, the necessary and sufficient conditions for the existence of the second-order Choquet dual capacities are proven in the terms of the APC of a CBR and ME. After establishing the links between the APC of a CBR and the NERC of a MSR, we proved the same in the case of the MSR. A recursive connection formula between the interaction indexes, Shapley values, and the probability distribution of the NERC of a two-order additive fuzzy measure was obtained in the environment of a general MAGDM. A new distance concept was introduced for all fuzzy measures’ classes defined in finite sets in terms of the NERC. The distance between two fuzzy measures was defined as the distance between their NERCs. This distance is equivalent to the distance defined on the same class under the conditions of the APC of a CBR. The correctness proposition on the extension of the distance between fuzzy measures for the NERC was preserved: distances between any two fuzzy measures and between their dual fuzzy measures also coincided in the CBR as the MSR. After parameterization, the calculation formula of the new distance was obtained. An illustrative example was considered in order to easily present the obtained results. The connection schemes between the CBR and MSR and the sequential scheme of key facts and results obtained are presented at the end of this work.

PREFERENCE AGGREGATION MODEL CONSTRUCTION OF GROUP MANAGEMENT DECISION-MAKING BASED ON RELATIVE ENTROPY AND NORM OPERATOR

Due to the complexity of the problem in the actual group management decision, the ambiguity of the information of all parties and the uncertainty of the decision environment, it is often difficult for decision-makers to describe the decision information by quantitative methods, but to give uncertain linguistic variables. Based on the analysis of previous research results, this paper proposes a group management decision-making aggregation model based on relative entropy and norm operator. First, the model uses the operation of [Formula: see text]-norm and new [Formula: see text]-norm operator to make the fuzzy set satisfy the ranking rate and contradiction rate. Then, based on the reliability of relative entropy, the attribute weights of different expert decisions are obtained, and the optimal assembly model of the corresponding group decision is established, respectively. The optimal solution and decision algorithm are given for each optimization model. The last example of the algorithm proves the validity and feasibility of the model, and can effectively avoid the distortion and distortion in the process of group management decision preference aggregation.

An Integrated 3-Phase Group Decision-Making Model for Supplier Selection in a Supply Chain Network

An Integrated 3-Phase Group Decision-Making Model for Supplier Selection in a Supply Chain Network

Distance education tool selection using novel spherical fuzzy AHP EDAS.

COVID 19 pandemic, which entered our lives suddenly, caused the change of our classical education system and forced higher education institutions to switch to distance education quickly. During this time, the need for a method that can comprehensively and scientifically evaluate the alternatives of videoconferencing tools to be used in distance education has emerged. This paper proposes a novel hybrid multi-criteria group decision-making (MCGDM) model by integrating the analytic hierarchy process (AHP) and the evaluation based on distance from average solution (EDAS) methodologies. The proposed model is developed with spherical fuzzy (SF) sets, which enable decision-makers (DMs) express their membership, non-membership and hesitancy degrees independently, and in a large three-dimensional spherical space. The applicability of the developed spherical fuzzy AHP EDAS is illustrated through a problem of selecting a videoconferencing tool for distance education. For this purpose, three DMs evaluate five popular videoconferencing tools, namely Zoom, Google Meet, Cisco WebEx, Skype and Microsoft with respect to six criteria, which are expanded with 32 related sub-criteria from the literature to more comprehensively handle the problem. The implications, sensitivity and comparative analyses, limitations and future research avenue are also given within the study.

Robust Minimum Cost Consensus Model for Multicriteria Decision-Making under Uncertain Circumstances

Recently, the consensus model of group decision-making in uncertain circumstances has received extensive attention. Existing models focus on either minimum cost (maintain the total budget) or maximum utility (improve satisfaction). Based on the minimum cost consensus model, a new multicriteria robust minimum cost consensus model with utility preference is proposed in this paper. Firstly, considering the inherent uncertainty of input data, the unit adjustment cost of experts is described under three robust scenarios. Subsequently, a cost consensus model that expresses the views of decision-makers in a variety of uncertain preference forms such as utility function and Gaussian distribution is proposed. Finally, through the application in emergency decision-making, the cost model and the utility model were compared and analyzed to verify the effectiveness and superiority of the proposed model.

An integrated qualitative group decision-making method for assessing health-care waste treatment technologies based on linguistic terms with weakened hedges

The assessment of the treatment technologies is a vital point for health care waste (HCW) management. The assessment process is regarded as a multi-attribute group decision-making problem. Considering individual knowledge backgrounds and psychological preferences, and various uncertainty factors which are difficult to describe numerical and precisely, experts often portray their individual assessments by employing qualitative expressions. Linguistic term with weakened hedge (LTWH) is an effective tool to quantify the qualitative information. Additionally, the Bonferroni mean (BM) can capture the interrelationship between input data and the combined compromise solution (CoCoSo) method could produce reliable outcomes. In this paper, we extend the BM operator into the LTWH environment and propose the linguistic term with weakened hedge BM (LTWHBM) operator and the weighted version, i.e., LTWHWBM, to depict the indeterminacy in the HCW management and cope with assessment of the treatment technology problems based on the developed novel operational rules of LTWHs. Various properties and special cases of these operators are investigated in detail. Furthermore, we design a LTWHWBM based CoCoSo group decision-making model, herein the LTWHWBM operator is employed to integrate individual experts’ preferences and the extend CoCoSo method is utilized to yield the ranking of alternatives. Ultimately, a case study concerning the assessment of the HCW treatment technologies is performed. The feasibility and favorable characteristics of the established model are justified by the comparisons with several prevailing methodologies and sensitivity analyses.

Granular computing and optimization model-based method for large-scale group decision-making and its application

In large-scale group decision-making process, some decision makers hesitate among several linguistic terms and cannot compare some alternatives, so they often express evaluation information with incomplete hesitant fuzzy linguistic preference relations. How to obtain suitable large-scale group decision-making results from incomplete preference information is an important and interesting issue to concern about. After analyzing the existing researches, we find that: i) the premise that complete preference relation is perfectly consistent is too strict, ii) deleting all incomplete linguistic preference relations that cannot be fully completed will lose valid assessment information, iii) semantics given by decision makers are greatly possible to be changed during the consistency improving process. In order to solve these issues, this work proposes a novel method based on Granular computing and optimization model for large-scale group decision-making, considering the original consistency of incomplete hesitant fuzzy linguistic preference relation and improving its consistency without changing semantics during the completion process. An illustrative example and simulation experiments demonstrate the rationality and advantages of the proposed method: i) semantics are not changed during the consistency improving process, ii) completion process does not significantly alter the inherent quality of information, iii) complete preference relations are globally consistent, iv) final large-scale group decision-making result is acquired by fusing complete preference relations with different weights.

An Influence Network-Based Consensus Model for Large-Scale Group Decision Making with Linguistic Information

The vast majority of the existing social network-based group decision-making models require extra information such as trust/distrust, influence and so on. However, in practical decision-making process, it is difficult to get additional information apart from opinions of decision makers. For large-scale group decision making (LSGDM) problem in which decision makers articulate their preferences in the form of comparative linguistic expressions, this paper proposes a consensus model based on an influence network which is inferred directly from preference information. First, a modified agglomerative hierarchical clustering algorithm is developed to detect subgroups in LSGDM problem with flexible linguistic information. Meanwhile, a measure method of group consensus level is proposed and the optimal clustering level can be determined. Second, according to the preference information of group members, influence network is constructed by determining intra-cluster and inter-cluster influence relationships. Third, a two-stage feedback mechanism guided by influence network is established for the consensus reaching process, which adopts cluster adjustment strategy and individual adjustment strategy depending on the different levels of group consensus. The proposed mechanism can not only effectively improve the efficiency of consensus reaching of LSGDM, but also take individual preference adjustment into account. Finally, the feasibility and effectiveness of the proposed method are verified by the case of intelligent environmental protection project location decision.

Group Decision-Making Model of Renal Cancer Surgery Options Using Entropy Fuzzy Element Aczel-Alsina Weighted Aggregation Operators under the Environment of Fuzzy Multi-Sets

Since existing selection methods of surgical treatment schemes of renal cancer patients mainly depend on physicians’ clinical experience and judgments, the surgical treatment options of renal cancer patients lack their scientifical and reasonable information expression and group decision-making model for renal cancer patients. Fuzzy multi-sets (FMSs) have a number of properties, which make them suitable for expressing the uncertain information of medical diagnoses and treatments in group decision-making (GDM) problems. To choose the most appropriate surgical treatment scheme for a patient with localized renal cell carcinoma (RCC) (T1 stage kidney tumor), this article needs to develop an effective GDM model based on the fuzzy multivalued evaluation information of the renal cancer patients. First, we propose a conversion method of transforming FMSs into entropy fuzzy sets (EFSs) based on the mean and Shannon entropy of a fuzzy sequence in FMS to reasonably simplify the information expression and operations of FMSs and define the score function of an entropy fuzzy element (EFE) for ranking EFEs. Second, we present the Aczel-Alsina t-norm and t-conorm operations of EFEs and the EFE Aczel-Alsina weighted arithmetic averaging (EFEAAWAA) and EFE Aczel-Alsina weighted geometric averaging (EFEAAWGA) operators. Third, we develop a multicriteria GDM model of renal cancer surgery options in the setting of FMSs. Finally, the proposed GDM model is applied to two clinical cases of renal cancer patients to choose the best surgical treatment scheme for a renal cancer patient in the setting of FMSs. The selected results of two clinical cases verify the efficiency and rationality of the proposed GDM model in the setting of FMSs.

Adaptive multi-criteria group decision-making model based on consistency and consensus with intuitionistic reciprocal preference relations: a case study in energy storage technology selection

Aim: This study aims to present a multi-criteria group decision-making model with intuitionistic reciprocal preference relations that incorporates a new feedback mechanims adapting its operation to the decision-makers' consistency. Methods: A new adaptive feedback mechanism is developed within a multi-criteria group decision-making model. Assuming different kinds of decision-makers classified according to their consistency, this feedback mechanism customizes the recommendations given by adopting three strategies to identify the problematic assessments that the decision-makers should change if they want to collaborate to increase the consensus. Results: The performance of the proposed model is shown in a case study for the selection of an energy storage technology. It shows that the decision-makers providing assessments with low consistency must modify more assessments, allowing the most consistent decision-makers to be the leaders of the negotiation and persuade the others to adjust their assessments to improve the agreement achieved. Conclusion: In this study, a novel multi-criteria group decision-making model is developed. It includes an adaptive feedback mechanism adjusting the advice provided by considering the decision-makers' consistency. It allows that the assessments of the most consistent decision-makers never be strongly modified during the decision process.

Hesitant fuzzy-based integrated multi-criteria group decision-making model for supplier selection

Hesitant fuzzy-based integrated multi-criteria group decision-making model for supplier selection

A Novel Integrated Fuzzy-Rough MCDM Model for Evaluation of Companies for Transport of Dangerous Goods

The organization and execution of the transport of dangerous goods is conditioned by a series of legal, technical, technological, safety, and engineering requirements, which must be met. In this way, a complex system is created which has a large number of participants and in which optimization should be performed at each stage from all the above aspects. The main goal of this paper is to create a novel Fuzzy-Rough MCDM (Multiple-Criteria Decision-Making) for the evaluation of companies engaged in the transport of dangerous goods. A group decision-making model was created to evaluate 11 transport companies based on nine legal, technical, technological criteria. The improved fuzzy stepwise weight assessment ratio analysis (IMF SWARA) method was used to calculate the criterion weights, while transport companies were ranked based on Rough Measurement Alternatives and Ranking according to the COmpromise Solution (R-MARCOS). The integration of these methods into a single model that combines two theories of uncertainty, fuzzy and rough, was performed for the first time in this study, which represents a significant contribution. The results show that the most significant criteria are as follows: dangerous goods are classified and permitted under ADR (Agreement Concerning the International Carriage of Dangerous Goods by Road), the prescribed documents are in the transport unit, and the equipment is in the transport unit. When it comes to the evaluation results of companies, it can be noticed that A1 and A4 show the best performance, while A8 and A9 are in the last position. In order to test the stability of the model developed, sensitivity analysis, comparative analysis, and the influence of the dynamic formation of the initial matrix were created.

Hybrid weighted aggregation operator of cubic fuzzy-consistency elements and their group decision-making model in cubic fuzzy multi-valued setting

The ambiguity and uncertainty of human cognition about actual engineering problems are very challenging and indispensable issues in the information expression and aggregation. However, existing various cubic (hesitant) concepts may not reasonably represent the hybrid information of both an interval-valued fuzzy value and a fuzzy sequence with identical and/or different fuzzy values, which commonly occurs in engineering fields. To express the hybrid information, this paper first proposes the notion of a cubic fuzzy multi-valued set as a new extension of existing cubic (hesitant) notions and defines operational relations of cubic fuzzy multi-valued elements. To obtain reasonable operations between different fuzzy sequence lengths in cubic fuzzy multi-valued elements, cubic fuzzy multi-valued elements are transformed into cubic fuzzy-consistency elements based on the average value and consistency degree/level (complement of standard deviation) of a fuzzy sequence in a cubic fuzzy multi-valued element. Next, we present operations of cubic fuzzy-consistency elements and an expected value of a cubic fuzzy-consistency element for ranking cubic fuzzy-consistency elements. Further, we propose a cubic fuzzy-consistency hybrid weighted arithmetic and geometric averaging operator, and then develop a multi-attribute group decision-making model using the cubic fuzzy-consistency hybrid weighted arithmetic and geometric averaging operator and expected value of cubic fuzzy-consistency elements to solve group decision-making problems under the cubic fuzzy multi-valued environment. To reflect the feasibility and effectiveness of the developed group decision-making model, the developed group decision-making model is utilized in an example on the selection problem of slope design schemes regarding an open pit mine in the cubic fuzzy multi-valued environment. Comparative analysis indicates the flexibility and rationality of the developed group decision-making model.

An ensemble learning method based on deep neural network and group decision making

Ensemble learning (EL) method which has high potential to improve the performance of single image classification model can be constructed in two steps: one is the generation of weak learners; the other is the combination of these learners. In this paper, an ensemble learning method based on deep neural network and group decision making (DNN-GDM-EL) is proposed, which uses deep neural networks (DNNs) to generate individual learners and exploits group decision making (GDM) to combine these learners. DNNs have demonstrated remarkable ability for image classification due to the powerful feature extraction ability. To ensure the diversity and accuracy, many different DNNs are used to generate individual learners. Furthermore, the individual learners are regarded as decision-makers (DMs), the categories are seen as alternatives, and the GDM aims to find an optimal alternative considering various suggestions of DMs. Specifically, a GDM model is established based on Bayesian theory which can reflect the complex relationship among the class of image, prior knowledge and output of DNN, and a GDM method based on TOPSIS is applied to solve this problem. Next, the index matrix consisted of DM's attributes is proposed, and an aggregation method based on 2-additive generalized Shapley AIVIFCA (2AGSAIVIFCA) operator is used to calculate the weights of DMs by fusing these matrixes. Further, state transition algorithm (STA) is applied to obtain the optimal weights of alternative's attributes. The effectiveness and superiority are verified in three public data sets and a real industrial problem by comparing DNN-GDM-EL method with other typical EL methods.

The operational properties of linguistic interval valued q-Rung orthopair fuzzy information and its VIKOR model for multi-attribute group decision making

As a generalization of linguistic q-rung orthopair fuzzy set (Lq-ROFS), linguistic interval valued q-Rung orthopair fuzzy set (LIVq-ROFS) is a new concept to deal with complex and uncertain decision making problems which Lq-ROFS cannot handle. Due to the lack of information in decision making process, decision makers mostly prefer to give their preferences in interval form rather than a crisp number. In this situations, LIVq-ROFS appears up as a useful tool. In this work, we define operational laws of LIVq-ROFS and prove some properties. Furthermore, we propose the conception of the LIVq-ROF weighted averaging operator and give its formula by mathematical induction. To compare two or more linguistic interval valued q-Rung orthopair fuzzy numbers (LIVq-ROFNs), the improved form of score function is also given. Considering the powerfulness of LIVq-ROFSs handling ambiguity and complex uncertainty in practical problems, the key innovation of this paper is to develop the linguistic interval-valued q-rung orthopair fuzzy VIKOR model that is significantly different from the existing VIKOR methodology. The computing steps of this newly created model are briefly presented. Finally, the effectiveness of model is verified by an example and through comparative analysis, the superiority of VIKOR method is further illustrated.

A comprehensive minimum cost consensus model for large scale group decision making for circular economy measurement

Since the first report on the Circular Economy (CE) appeared in 2013, there has been an explosion of interest in the subject by society and the business world. Thus, a base of academic literature has been developed, seeking the establishment of principles that serve as a theoretical foundation for the concept of CE. Governments demand to know how organizations are evolving in the transition towards the new production model. However, despite the efforts of researchers and companies to develop effective measurement systems, it is not easy to decide which aspects to measure, nor to determine the degree of intensity in which an organization implements the CE model. The measurement proposals combine different methodologies that are costly and time consuming procedures. We propose a comprehensive minimum cost consensus model for large scale group decision making, in which the initial experts’ preferences are automatically adjusted to obtain the measurement and cost of indicators, so that they might agree on the measurements implemented. The main aim of this research is not only to provide a quick, useful and correct method for measuring the CE, but also to show its correctness, advantages and usefulness by comparing its performance with a real case.

A dynamic hybrid trust network-based dual-path feedback consensus model for multi-attribute group decision-making in intuitionistic fuzzy environment

This paper proposes a dual-path feedback consensus model based on dynamic hybrid trust relationships to solve multi-attribute group decision-making problems in intuitionistic fuzzy environment. This model comprises two main parts: (a) the construction of a dynamic hybrid trust network among decision makers (DMs) and (b) the formation of a dual-path feedback mechanism to improve the group consensus. In the first part, a hybrid trust network is constructed by combining DMs’ prior knowledge of each other and the preference similarities between them. Then, the hybrid trust network is dynamically updated iteratively to reflect the changes in the trust relationships in the process of joint decision-making. In the second part, DMs with low consensus degrees are identified and provided with either a preference or weight adjustment path to improve the group consensus. The preference adjustment path is activated for DMs who agree to modify their preferences, and a nonlinear programming model is proposed to help DMs improve consensus degrees while minimizing adjustment cost. The weight adjustment path is activated for DMs who stick to their own opinions and refuse to make changes, and their weights is adjusted accordingly. An illustrative example along with the related sensitivity analysis and comparative study are used to verify the effectiveness of the proposed model.

A DES-based group decision model for group decision making with large-scale alternatives

A DES-based group decision model for group decision making with large-scale alternatives

Supply chain finance platform evaluation based on acceptability analysis

With the rapid development of financial technology, an increasing number of supply chain finance (SCF) service platforms have sprung up to optimize the flows of financial resources, information and materials in the supply chain. The evaluation and selection of SCF platforms have consequently become critical issues not only for platform customers but also for competitors. Although the practices and challenges of SCF have been analyzed in-depth and several studies have investigated the factors that influence SCF platform performance, research on the evaluation of different SCF platforms is scarce. To comprehensively evaluate different types of SCF platforms, this study first identifies critical factors that influence the performance of SCF platforms. A stochastic multicriteria acceptability analysis for group decision-making (SMAA-2) model is then applied to address the SCF platform evaluation problem. Finally, a questionnaire survey is conducted to validate the accuracy of the proposed evaluation model. The empirical results show that the proposed method provides rankings similar to the survey results and thus is accurate and reliable. The evaluation factors and the rankings of SCF platforms can provide references and decision support not only for financial service providers and platform customers but also for regulatory institutions.

TMT group decision method based on system dynamics and game control theory.

The game of group decision-making of top management team is the difficulty of research. It will provide new ideas for the breakthrough of this research to study the evolution process of internal conflict of top management team by using game theory method, to study the model of solving internal behavior conflict of top management team by using cybernetics method, and to analyze the game and control of group decision-making of top management team by using system dynamics method. In this paper, the interaction, decision-making process and control methods of TMT group decision-making are described in detail from the perspective of random game and control. Based on the combination of game and control, TMT group decision-making is studied, and a new theoretical model of TMT group decision-making is proposed.