Group Decision-making Problems Research Articles

A novel fuzzy TOPSIS method based on T-spherical fuzzy Aczel–Alsina power Heronian mean operators with applications in pharmaceutical enterprises’ selection

One of the most significant and complete approaches to accommodate greater uncertainty than current fuzzy structures is the T-Spherical Fuzzy Set (TSPFS). The primary benefit of TSPFS is that current fuzzy structures are special cases of it. Firstly, some novel TSPF power Heronian mean (TSPFPHM) operators are initiated based on Aczel–Alsina operational laws. These aggregation operators (AOs) have the capacity to eliminate the impact of uncomfortable data and can simultaneously consider the relationships between any two input arguments. Secondly, some elementary properties and core cases with respect to parameters are investigated and found that some of the existing AOs are special cases of the newly initiated aggregation operators. Thirdly, based on these AOs and Aczel–Alsina operational laws a newly advanced technique for order of preference by similarity to ideal solution (TOPSIS)-based method for dealing with multi-attribute group decision-making (MAGDM) problems in a T-Spherical fuzzy framework is established, where the weights of both the decision makers (DMs) and the criteria are completely unknowable. Finally, an illustrative example is provided to evaluate and choose the pharmaceutical firms with the capacity for high-quality, sustainable development in the TSPF environment to demonstrate the usefulness and efficacy. After that, the comparison analysis with other techniques is utilized to demonstrate the coherence and superiority of the recommended approach.

Configuration optimization of product-service system design requirements based on hesitant information axiom

Product-service system (PSS) has attracted attention of manufacturers to shift from product-providing to solution-providing, which is a marketable set of products and services. The existing researches emphasize the fulfillment of individualized customer requirements through different PSS configurations. The PSS planning phase is of high importance in generating conceptual schemes, which translates customer requirements (CRs) to design requirements (DRs). In this paper, a systematic decision-making approach based on QFD is put forward aiming to configure the PSS design requirements (DRs). To address the uncertainty and hesitancy in QFD modeling, a hesitant fuzzy linguistic term sets (HFLTSs) is applied to elicit the experts’ linguistic preferences in evaluating the importance of CRs and the relationships between CRs and DRs. To dealing with the group decision-making problems concerning the HFLTSs, the min-upper operator and the max-lower operator assemble the experts’ evaluation results into a linguistic interval, and then the numerical results can be obtained by using the 2-tuple linguistic representation model and the interval preference degree computation. A non-linear 0-1 programming model is proposed to select the target DRs’ specifications for maximizing customer satisfaction under cost constraint. In order to objectively determine the satisfaction degree of each optional specification of DR, the information axiom is introduced to construct the objective function via information content computation. To deal with the qualitative DRs, HFLTSs and information axiom are combined and hesitant information axiom (HIA) is proposed. Finally, a DRs optimization model is established using HIA and the imprecision method. A case study is carried out to demonstrate the effectiveness of the optimal PSS planning approach developed.

An extended ITARA-TOPSIS method for multi-criteria group decision-making problems based on R-number

With the continuous improvement and development of various decision-making methods, it has led to the widespread use of fuzzy sets and fuzzy numbers. At the same time, the application of decision-making methods in different fuzzy environments has been very effective in addressing the deficiencies in existing research. At present, triangular fuzzy numbers have been widely used in the evaluation aspects of various decision making methods, and the proposed R-number effectively solve the uncertainty involving problems related to future events, but the existing research based on the TOPSIS method in the R-number environment has not yet been clearly applied to the triangular fuzzy number environment, and the indifference threshold-based attribute ratio analysis (ITARA) method in the fuzzy environment has yet to be extended. Therefore, this paper proposes a fuzzy indifference threshold-based attribute ratio analysis (FITARA) method based on triangular fuzzy numbers for solving the problem of determining attribute weights in the multi-attribute decision-making process. Secondly, the various risks of the decision environment and the impact on future events are considered and R-number are used to solve this puzzle. In addition, the incorporation of risk perception factors in the context of the existing RTOPSIS method considering multiple risk factors and the use of Manhattan distances to optimize the large number of operations in the process of the method resulted in the development of the FITARA-RTOPSIS model. Finally, the proposed FITARA-RTOPSIS method is applied to the problem of siting emergency supplies storage depots, and the effectiveness of the proposed method is verified through comparative analysis.

A consensus model considers managing manipulative and overconfident behaviours in large-scale group decision-making

When dealing with large-scale group decision-making problems, the central emphasis lies in the objective and rational acquisition of a collective opinion acceptable to the majority of decision makers. Manipulative and overconfident behaviours are two common behaviours that make the decision results deviate from the objective facts in the decision-making process. To manage manipulative and overconfident behaviours in decision-making, this paper investigated a novel consensus model based on social networks. A novel efficient clustering model is first proposed in the model, in which the subgroups' combined cohesion is considered. Furthermore, for the manipulative behaviour of decision makers, we proposed an improved method for the identification and management based on trust relationships. In the consensus reaching process, we proposed a new identification mechanism to promote consensus reaching effectively. In the feedback mechanism, the social network DeGroot model is employed to adjust the opinions of decision makers. Moreover, a management approach is proposed for the overconfident behaviour of decision makers in the social network DeGroot model. Lastly, the feasibility and applicability of the proposed model are verified by an illustrative example. Simulation experiments and comparative analysis demonstrate the effectiveness of the model in facilitating consensus reaching.

Exploring T-spherical fuzzy sets for enhanced evaluation of vocal music classroom teaching

Vocal music is a relatively complex skill and skill course, which not only plays an important role in music education in universities, but also cultivates students’ musical level and artistic cultivation. In the process of teaching vocal music in universities, how to improve the quality of teaching has become an important task and goal for teachers. The vocal music classroom teaching quality evaluation is a classical multiple-attribute group decision-making (MAGDM) problem. In this paper, some novel Dice similarity measures (DSM) and some generalized Dice similarity measures (GDSM) are proposed under T-spherical fuzzy sets (T-SFSs). Then, the weighted generalized Dice similarity measures (WGDSM) is used to solve the MAGDM with T-SFSs. Finally, an illustrative example for vocal music classroom teaching quality evaluation is given to demonstrate the efficiency of the GDSMs.

Group decision-making method based on advanced aggregation operators with entropy and divergence measures under 2-tuple linguistic Pythagorean fuzzy environment

2-tuple linguistic Pythagorean fuzzy values (2TLPFVs) are flexible and more efficient tools to represent fuzzy linguistic information in complex real-world situations than 2-tuple linguistic values (2TLVs) and 2-tuple linguistic intuitionistic fuzzy values (2TLIFVs). This paper introduces a new score value function for 2TLPFVs and discusses its major properties. Two new aggregation operators (AOs) are presented, namely, the advanced 2-tuple linguistic Pythagorean fuzzy weighted average (Adv-2TLPFWA) operator and the advanced 2-tuple linguistic Pythagorean fuzzy weighted geometric (Adv-2TLPFWG) operator, which aggregate 2-tuple linguistic Pythagorean fuzzy information based on novel operational laws of 2TLPFVs. The work also exhibits several properties of these AOs in detail. Furthermore, the article discusses entropy and divergence measures under the 2-tuple linguistic Pythagorean fuzzy information environment. Finally, a new multiple attribute group decision-making (MAGDM) method is constructed to solve group decision-making problems in the 2-tuple linguistic Pythagorean fuzzy context. A real-life group decision-making problem of selecting the best green supplier for fruits and vegetables is investigated to verify the applicability and validity of the proposed approach. The sensitivity investigation and comparative analysis are conducted to illustrate the advantages of the proposed method.

A three-parameter interval number group decision-making method based on kernel and greyness degree

A decision method based on kernel and greyness degree is proposed for group decision-making problems in which attribute values are represented by three-parameter interval grey numbers, whose kernel and greyness degree are modified. The prospect value of each program’s evaluation information given by decision-makers is solved by using prospect theory. Finally, the reasonable decision-makers weight should make the total weighted prospect value of the scheme the maximum, the deviation between the evaluation information of each decision-maker, the mean value of the evaluation information the minimum, and the greyness degree deviation value of the information which is given by decision makers the minimum. The weight vector of the decision-makers is obtained by solving this model, and the comprehensive prospect value of the solution is used to rank the alternatives. Finally, taking a commercial aircraft parts supplier selection as an example, the rationality of the method is verified.

A novel integrated approach based on best–worst and VIKOR methods for green supplier selection under multi-granularity extended probabilistic linguistic environment

With consideration for the extensive resources consumption and environmental degradation being on the rise today, implementing green development strategy to pursue both socioeconomic growth and the coordinated of environment sustainability, has become an increasingly important issue in modern enterprise supply chain operations management. Hence, the appropriate green supplier selection (GSS), viewed as a core issue in green supply chain management (GSCM), requires continuous research in this field to obtain a complete perception on GSS practices. It can be regarded as a multi-attribute group decision-making (MAGDM) problem that involves many conflict and unmeasurable evaluation criteria. In view of the superiority of multi-granularity extended probabilistic linguistic term sets (MGEPLTSs) in modeling such issues on potential ambiguity, complexity and uncertainty in actual GSS practices, we propose a novel integrated MAGDM methodology for GSS problems, by integrating the BWM (best–worst method) with the VIKOR (VIšekriterijumsko KOmpromisno Rangiranje) technique under the MGEPLTSs environment. First, by introducing the multi-granularity and probabilistic linguistic term sets, the MGEPLTSs are proposed to represent and quantify the decision information of GSCM practitioners. Then, the BWM is introduced to the MGEPLTSs environment to compute the weights of decision-making panels and evaluation attributes in GSS problems, by building the fuzzy mathematical programming model, respectively. Finally, we extend a multi-granularity extended probabilistic linguistic VIKOR method to calculate the compromise measure of alternatives considering the group utility maximization and the individual regret minimization, thereby achieving the full ranking of alternatives. A GSS case is conducted to illustrate the feasibility of the proposed approach, and the sensitivity analysis and comparative analysis with other similar approaches are presented to demonstrate its effectiveness and advantages.

A reputation-based trust evaluation model in group decision-making framework

In group decision-making (GDM) problems, experts need to communicate and adjust their opinions in order to achieve consensus on the final decision-making output. Since experts may have conflicting opinions, trust can be critical and an important reference to use in the decision-making process when some experts are required to modify opinions. Recently, decision-making models based on trust and reputation have been investigated intensively. However, these research works usually rely on the constructed social trust network and honesty and fairness of the trust ratings from experts are taken for granted. The objective of this study is to develop a reputation-based trust model for GDM framework to obtain the trust relationship among experts from their direct interaction and word of mouth. First, the paper defines a trust credibility measure to filter out malicious experts before trust assessment, and designs direct trust feedback based on the interaction quality. Then, based on this direct trust feedback, the global reputation model is proposed according to the synthetical performance of received and provided trust feedback, which encourages long-term good behaviour and guarantees trustworthy communications and interactions. The reputation-based trust and direct trust feedback together build trust relationship among experts. Finally, a simulation experimental analysis of the proposed trust and reputation models is carried out to verify their effectiveness in trust and reputation establishment among the experts, even under the presence of malicious ones.

Conflict management-based consensus reaching process considering conflict relationship clustering in large-scale group decision-making problems

In large-scale group decision-making (LSGDM) events, conflicts among decision makers (DMs) usually occur, causing serious damage to the decision-making process. Accurate conflict detection and timely management in LSGDM can improve the efficiency of the consensus reaching process (CRP) and reduce the overall conflict degree. This paper presents a conflict management-based consensus reaching process (CM-CRP) to achieve centralized and efficient management of DMs. In CM-CRP, to further manage the conflict relationships among DMs, a new clustering algorithm is considered where DMs with conflicting opinions are clustered into a subgroup. To improve the accuracy of the management and precisely the portray behaviors of DMs, for a multi-attributes LSGDM event, the confidence of DMs at the attribute level is captured. Further, the dynamic opinion weight operator is proposed in CM-CRP combining the confidence level and conflict degree of DMs, which enables a more accurate measurement of DMs’ receptivity to others’ opinions. Comparative experiments prove that the proposed CM-CRP outperforms the existing CRP, and several simulations investigate the effect of different factors on the CM-CRP.

A multi-criteria group decision-making method with fuzzy preference relations based on filtration-and-weighting-based triangular bounded consistency

In multi-criteria group decision-making (MCGDM) problems with incomplete fuzzy preference relations (FPRs), the consistency based on bounded rationality is preferred to depend on the historical preferences of decision makers to generate complete FPR matrices. There are two factors in influencing the effect of historical preferences on complete FPR matrices and further decision makers’ satisfaction of group solutions. One is the effectiveness of historical preferences and the other is their relative weights. On this basis, this paper proposes a new MCGDM method with FPRs based on the filtration-and-weighting-based triangular bounded consistency (FWBTBC), where historical FPRs between three adjacent pairs of alternatives are characterized by triangles and these two factors are considered comprehensively. A triangle filtration process is designed based on the Grubbs criterion to filter the abnormal triangles in the collected ones. A weight determination process is developed, where a distance measure between remaining triangles is constructed to calculate their weights. Based on the combination of the two processes, the FWBTBC of FPRs is created and then the proposed method is presented. The validity of the proposed method is demonstrated by its application in analyzing a drainage tube supplier selection problem for a tertiary hospital located in Hefei, Anhui, China.

Hamy mean operator under complex picture fuzzy environment and its application to disaster management program

Disaster Management Program plays a critical role in coordinating and implementing strategies to address emergencies and disasters, ranging from natural events like hurricanes, earthquakes, and wildfire to human-made incidents such as industrial accidents or terrorist attacks. Simultaneously, it is widely studied as a typical multi-attribute decision-making (MADM) problem. This paper investigates the concept of complex picture fuzzy sets (CPFS), an extension of picture fuzzy sets (PFS), achieved by the inclusion of a phase term. The existence of phase terms expand the scope of CPFS from real line to a complex plane of unit disc and highlight its originality by demonstrating its capacity to handle both vagueness and periodicity simultaneously. In this paper, the complex picture fuzzy Hamy mean operator (CPFHM) and complex picture fuzzy dual Hamy mean operator (CPFDHM) is studied. The reason of selecting complex picture fuzzy Hamy mean operator (CPFHMO) is that it can find interrelationship among multi-input variables. Then the various properties of CPFHM and CPFDHM operator are described in depth. A multi-attributes group decision-making (MAGDM) technique for solving group decision-making problems is proposed based on these operators. The validity of the present technique is demonstrated by analyzing a disaster management problem. Furthermore we check the sensitivity of parameter k and apply the validity test on our proposed technique. Finally, a comprehensive comparison is provided between the proposed model and specific existing approaches, illustrating that the suggested decision model is superior and more advantageous than the existing employed methodologies.

Fermatean fuzzy multi-attribute personnel-position matching group decision-making with unknown weight information

In view of the lack of research on two-sided matching group decision-making and the rare application of Fermatean fuzzy numbers in two-sided matching decision-making, a multi-attribute personnel-position matching group decision-making methodology is proposed. First, the group decision-making problem for multi-attribute personnel-position matching is described. To address this problem, a new algorithm is designed to determine the attribute weights. The group comprehensive Fermatean fuzzy evaluation matrix can be obtained by aggregating the attribute weights, the Fermatean fuzzy evaluation matrices given by the decision-makers and the weights of decision-makers. Moreover, another novel algorithm is designed to calculate the weight of the group consistency attribute. The synthetic Fermatean fuzzy evaluation matrix is obtained by aggregating the group consistency attribute weights and the Fermatean fuzzy evaluation matrices given by the candidates. Moreover, based on the group comprehensive Fermatean fuzzy evaluation matrix and the synthetic Fermatean fuzzy evaluation matrix, a multi-objective two-sided matching model is established to maximize the satisfaction of two-sided agents. The optimal two-sided matching scheme is obtained by the model solution. Finally, the feasibility, effectiveness and innovation of the proposed methodology are verified by an example analysis of personnel-position matching. The main contributions of this paper are listed as follows: (1) Group decision-making of two-sided matching based on Fermatean fuzzy information is explored. (2) A Fermatean fuzzy induced ordered weighted average (FFIOWA) operator that aggregates Fermatean fuzzy numbers is introduced. (3) Two new algorithms for calculating the unknown attribute weights are designed. (4) A satisfaction calculation method considering the accuracy coefficient is proposed. (5) The model for Fermatean fuzzy multi-attribute personnel-position matching group decision-making is developed.

Retracted: Novel Distance Measures for Single-Valued Neutrosophic Fuzzy Sets and Their Applications to Multicriteria Group Decision-Making Problem

Retracted: Novel Distance Measures for Single-Valued Neutrosophic Fuzzy Sets and Their Applications to Multicriteria Group Decision-Making Problem

A preference-approval structure-based non-additive three-way group consensus decision-making approach for medical diagnosis

The clinical diagnosis decision-making process of integrated traditional Chinese medicine and Western medicine is essentially a type of group decision-making (GDM) problem, which is transparently characterized by the multiformity of data types and the diversity of knowledge structures among experts. However, most existing GDM methods are designed based on single data types and cannot address GDM problems involving multiple data types and diversified attributes. Moreover, the preference-approval structure provides a simple framework to simulate the preference information regarding an individual’s ranking and approval. But it only considers two categories: approval and disapproval, without accounting for the indecision of individuals. In reality, three-way decision (TWD) is common. Given the deficiencies above, this paper puts forward a novel preference-approval structure-based three-way group consensus decision-making approach for a class of GDM problems with the characteristics of incompleteness, multi-granularity, diversity, and compound. To represent the information in these complex GDM problems, we firstly present the concept of incomplete multi-granularity diversified compound decision systems (IMGDCDSs). Secondly, according to the data characteristics, we construct a non-additive TWD model over the framework of granular computing. On the one hand, using the reference point of each attribute, an acquisition method of relative loss functions is given by considering three kinds of states. On the other hand, we present a new fuzzy measure to calculate non-additive conditional probabilities. The above work enriches the existing TWD theory. Based on the three-way classification and ranking results obtained, this paper subsequently defines a preference-approval structure and establishes a new group consensus decision-making approach. Thereby, the TWD model is integrated into preference-approval structures, which further enriches the GDM theory. More importantly, some existing group consensus methods are special cases of our study. Whereafter, we demonstrate the feasibility of the approach using an illustrative example. Finally, the stability and effectiveness of the approach are verified through an empirical study in the context of medical diagnosis.

A Group Decision-Making Method Based on the Experts’ Behavior During the Debate

Debate is a process consisting in arriving at a reasoned opinion on a proportion in which individuals must be truly capable of defending their own judgments. It has been used within group decision-making (GDM) problems to help experts make better decisions. However, whether experts engage in a vigorous debate, it can result in the use of aggressive language that may diminish consensus, which is the major objective of GDM. To avoid it, we present a novel method for GDM problems that can identify aggressive comments during the debate by incorporating a classifier based on sentiment analysis techniques. According to the information extracted during the debate, two procedures are developed to assign weights to the experts, which are used to introduce two new consensus measures and to make the final decision. Unlike the existing GDM methods, this new one can take advantage of the information extracted during the debate (i.e., experts’ behavior) throughout the decision process, making it in rapport with real-world GDM processes.

A study on exit and entry mechanism and evolution of relationships between decision makers for multistage large-scale group decision-making problems

This study develops a framework for multistage large-scale group decision-making problems. In the proposed framework, we develop an exit and entry mechanism for decision makers (DMs), which can select suitable DMs for subsequent decision-making stages, exclude unqualified DMs, and maintain good trust relationships among DMs. Second, the relationships between DMs are allowed to evolve by building new links and updating trust levels. Third, we propose a new method to generate a recommendation plan for each DM by referencing another DM’s evaluation (reference source) in a specific proportion (modification rate). The probability of a DM accepting its recommendation plan can be calculated by considering two factors (reference source and modification rate). Then, we consider the expectation of incrementing group consensus degree as the objective function to determine the optimal reference source and modification rate for each DM, thereby generating its recommendation plan. Finally, simulation and comparison experiments demonstrate the effectiveness of the proposed methods in improving the efficiency of consensus reaching among DMs.

MABAC method based on cumulative prospect theory for MCGDM with dual probabilistic linguistic term set and applications to sustainable supplier selection

Dual probabilistic linguistic term set (DPLTS) is a new proposed decision-making environment. It uses probabilistic form to represent the appraisal of the alternative from decision makers. There are few methods to deal with DPLTS according to the literature proposed up to now. The purpose of this article is to proposed a new improved Multi-Attribute Border Approximation Area Comparison (MABAC) method extended by cumulative prospect theory (CPT) and combined with DPLTS to address the multi-criteria group decision-making (MCGDM) problem of sustainable supplier selection. In order to make the decision procedure containing more fuzzy information, we also improved the equation of distance between DPLTSs with system of rectangular coordinates. This new improved MABAC method is combined with CPT and it is semi-objective method. Not only in the procedure of calculating distance between alternatives and border approximation area, but also in the procedure of determining the weights of attributes. At the end of this paper, the comparison of this new method with other proposed DPLTS methods, such as Correlation Coefficient Method and DPLTS-TODIM-CRITIC Method, demonstrates the availability and difference.

A probability-based fuzzy algorithm for multi-attribute decision-analysis with application to aviation disaster decision-making

This research addresses the challenges of multi-attribute group decision-making in a hesitant fuzzy environment, where randomness and uncertainty introduce biases into practical making choices. A probability-based double uncertain fuzzy (PDHF) algorithm is proposed to overcome these challenges. The algorithm constructs probability-based double hesitant fuzzy information matrices for each decision expert. The maximum deviation and entropy methods determine objective weights for decision experts and attribute indicators. Combining an improved scoring function and deviation function generates a comprehensive decision evaluation information matrix for decision experts. Subsequently, a decision algorithm that integrates probability-based double hesitant fuzzy sets with the preference ranking organization method (PROM) is employed to obtain the final decision result. As part of a case study, the algorithm is used to evaluate emergency action plans for aviation disaster accidents. Comparing the results with those from the TOPSIS, VIKOR, and PDHFS decision-making algorithms shows that the suggested PDHF algorithm works well and can be trusted for the multi-attribute group decision-making problems in hesitant fuzzy environments.

A two-stage consensus model for large-scale group decision-making considering dynamic social networks

Clustering and the feedback mechanism in a consensus reaching process (CRP) have been regarded as two critical components when solving the large-scale group decision-making (LSGDM) problem. However, the necessity of treating decision makers (DMs) as a dynamic social network (in terms of their trust relationships) in the DM clustering has not been fully investigated. Also, with people's increased democratic awareness, it appears essential to consider the individual's willingness to cooperate when designing the feedback mechanism in the CRP. This study will focus on solving the LSGDM problem considering dynamic social networks and propose a novel two-stage consensus model. First, a hybrid trust network is built to dynamically represent social relationships among DMs (i.e., the hybrid trust network evolves with the CRP). Spectral clustering algorithm is then used to cluster DMs into more trusted sub-groups, thus improving the efficiency of coordinating DMs. Second, a two-stage feedback mechanism is designed to help DMs reach a consensus in a more human-centric manner. To reach a consensus without seriously violating the willingness of DMs, we strike a balance between maximizing the consensus degree and minimizing the adjustment cost and further develop two nonlinear programming models to provide modification suggestions for sub-groups and individuals, respectively. The originality of the proposed consensus model can be summarized as clustering the dynamic hybrid trust network integrated with balancing the decision-making efficiency against the willingness of DMs to cooperate. The feasibility and effectiveness of the developed model are demonstrated through the detailed application study.