Linguistic Distribution Assessments Research Articles

An adaptive consensus model for multi-criteria sorting under linguistic distribution group decision making considering decision-makers’ attitudes

Group multiple criteria sorting (MCS) has become a trend in dealing with a variety of practical problems. During the process of managing group MCS, it is critical to reduce conflicts among decision-makers (DMs). Given the key role of DMs’ attitudes in affecting consensus level, this study aims to propose a novel consensus-based approach to solve group MCS problems considering DMs’ attitudes with flexible expression linguistic distribution assessments (LDAs) that can capture massive DMs’ qualitative preferences. To achieve this goal, first, a minimum adjustment-based optimization model is built to guide individuals in revising their preferences, and a maximum assignment interval-based optimization model is constructed to derive consistent and possible assignments of each alternative while maintaining the accuracy levels of the original assignments. An attitudinal consensus index is then defined to measure the group consensus level, by which group DMs’ attitudes can be well considered in MCS problems. A sophisticated adaptive feedback adjustment mechanism is also developed and inserted into the consensus model, which provides support for consensus-reaching based on the advantages of both types of adaptive feedback adjustment mechanism strategies. Afterwards, to generate more straightforward and scientific assignment solutions, this study proposes a minimum information loss-based optimization model to identify the final categories of each alternative. Finally, an illustrative example for evaluating livable cities, followed by sensitivity and comparative analyses, is presented to demonstrate the applicability and advantages of the proposed MCS approach.

Enhancing reliability of failure modes and effects analysis dealing with linguistic distribution assessments: A consistency based approach

Failure modes and effects analysis is a proactive reliability-management engineering technique, it has been applied to improve reliability of processes, products, and services. Traditional failure modes and effects analysis method manifests three deficiencies with regard to form of risk assessments, determination of risk factor weights, and exploitation of failure modes’ risk levels. Although numerous studies have contributed to surmount these deficiencies, existing failure modes and effects analysis methods still face such issues. Moreover, a novel drawback has raised when failure modes and effects analysis participants adopt linguistic terms as risk assessments of failure modes, the reliability of linguistic distribution assessments as well as failure modes and effects analysis groups (participants with similar knowledge background are previously classified into a failure modes and effects analysis group) is overlooked. Therefore, this paper introduces the transformation of linguistic distribution assessments into fuzzy preference relations, as more effective modeling for investigating consistency, by using the newly defined possibility degrees of linguistic distribution assessments will be applied to devise four algorithms for enhancing reliability of assessments. Hence, the three deficiencies of traditional failure modes and effects analysis method are solved by means of a novel failure modes and effects analysis framework. Finally, a real example with respect to ranking failure modes of worm wheel grinding machine is developed to verify the performance and advantages of the proposed method, in which ten failure modes are ranked and the one with the highest risk level is selected, and consistency of risk assessments is improved to be acceptable.

Canonical triangular interval type-2 fuzzy linguistic distribution assessment EDAS approach with its application to production supplier evaluation and ranking

The evaluation and ranking of suppliers is a critical challenge because it profoundly influences the profitability and competitive positions of manufacturers and producers. This paper presents a canonical triangular interval type-2 fuzzy linguistic distribution assessment based on the distance from average solution technique (CTriIT2F-LDA-EDAS), which is a novel method for evaluating and ranking production suppliers using linguistic distribution assessment (LDA) techniques. The CTriIT2F-LDA uses a canonical triangular interval type-2 fuzzy number (CTriIT2FN) to simplify the trapezium (or trapezoidal) interval type-2 fuzzy number (TrIT2FN) typically used in LDA, thereby making it less intricate and more readily adaptable due to its resemblance to the triangular fuzzy number. This paper presents calculation methods for both the CTriIT2FN and LDA, which involve a de-fuzzification function, distance measurements, and entropy measurements. Moreover, the proposed method was implemented to assess production suppliers involved in the production of electric vehicle DC charging stations for the State Grid Corporation of China. A sensitivity analysis that used both expert and criterion weights was performed. Subsequently, a comparative analysis with existing methods was conducted to validate the versatility and efficacy of the method. The results emphasize the critical factors that influence supplier competitiveness, such as environmental protection training, emissions, safety standards, inventory capacity, and minimum order requirements. Consequently, there is now a pressing need for policies focused on fostering environmental training and expanding both production and inventory management capabilities. The evaluation presented here is used to confirm the adaptability and efficiency of the CTriIT2F-LDA-EDAS method.

Three-dimensional dynamic trust-driven consensus model for social network group decision-making with application to sustainable supplier selection in a circular economy

The trust degree between individuals plays an important role in social network group decision-making (SNGDM). The majority of current literature assumes that the trust degree between individuals is constant. However, the trust values among decision-makers (DMs) are subject to change over time. Thus, it is necessary to identify potential dynamic trust that is compatible with the actual SNGDM to support the consensus reaching. For this purpose, this article investigates consensus building that considers dynamic trust among DMs in SNGDM with linguistic distribution assessments (LDAs). Firstly, the three-dimensional trust degree among DMs is constructed from three perspectives: the trust relationship from social networks, the confidence level of DMs, and the similarity of DMs' preferences. Secondly, an optimization model is developed with the objective of maximizing consensus to determine how trust degree is assigned to each perspective, thereby determining the weights of DMs. Then, a double feedback-based consensus mechanism incorporating both opinion evolution and trust evolution is developed. Under the guidance of consensus mechanism, an improved SNGDM approach with LDAs is presented. Finally, we demonstrate our proposed approach through a case study of sustainable supplier selection in a circular economy. Comparative analysis and sensitive analysis verify our approach’s effectiveness.

An optimization method for handling incomplete and conflicting opinions in quality function deployment based on consistency and consensus reaching process

Quality Function Deployment (QFD) is a highly regarded customer-oriented quality management tool, and its key step is to obtain the importance ratings of Customer Requirements (CRs) from customer opinions. However, customers usually provide incomplete and conflicting opinions due to cognitive limitations and differences in backgrounds. To this end, this study integrates an optimization-based consensus reaching process into QFD for dealing with conflicting customer opinions, where customer opinions are modeled with incomplete linguistic distribution assessments. First, a consistency-driven optimization model is designed to convert incomplete linguistic distribution assessments into complete linguistic distribution assessments by maximizing the consistency index of complete linguistic distribution assessments. Then, consensus reaching process based on a two-stage-based consensus model is developed to yield the consensual collective linguistic distribution assessments, which are further utilized to derive the consensual importance ratings of CRs. The first stage of the consensus model aims to minimize the adjustment between the original linguistic distribution assessments and the adjusted linguistic distribution assessments, and the second stage aims to minimize the symbolic distance between the original linguistic distribution assessments and the adjusted linguistic distribution assessments. Finally, a case study of product development for EVCARD in China, a sensitivity analysis, and a comparative analysis are presented to show the effectiveness and feasibility of our proposal.

Consensus reaching with heterogeneous stochastic dominance in the enterprise credit rating under linguistic distribution assessments context

Consensus reaching with heterogeneous stochastic dominance in the enterprise credit rating under linguistic distribution assessments context

A three-way decision model associated with decision-theoretic rough fuzzy sets based on computing with words

The three-way decision model based on linguistic term sets has been extensively investigated since decision makers frequently utilize natural language to evaluate in an actual decision-making process. The existing models require decision makers to select appropriate linguistic terms from a given linguistic term set. However, making such a choice is not always simple, and decision makers occasionally choose words that are related to their own experience. In order to deal with this kind of decision problem, we appeal to the theory of computing with words pioneered by Zadeh and establish a three-way decision model based on computing with words in this paper. The paper focuses on how to deal with more general linguistic information using the theory of computing with words. Initially, using the concept of computing with words, we translate more broad linguistic information into a linguistic distribution assessment on a balanced linguistic term set in order to better analyze linguistic information. The three-way decision based on computing with words is then discussed. Decision-theoretic rough fuzzy sets take into account the ambiguity of the decision target as a generalization of the classical decision-theoretic rough sets. This is what motivated us to develop a three-way decision based on decision-theoretic rough fuzzy sets using computing with words. Additionally, a fabricated example demonstrates that our three-way decision model is more adaptable in processing linguistic information and can handle more general linguistic information provided by decision makers.

Statistics-based method for large-scale group decision-making with incomplete linguistic distribution fuzzy information: Incorporating reliability and entropy

Aiming at large-scale decision-makers (DMs) and the loss of decision sample information in large-scale group decision-making (LSGDM), a novel statistical estimation method incorporating the reliability and entropy of linguistic distribution assessment is proposed. First, classify the large-scale DMs into several subgroups according to their prior decision efficiency distribution. After clustering, collect the five-number summary of the incomplete decision sample information provided by the DMs in the subgroups. Second, estimate the mean, standard deviation, skewness and kurtosis of the decision sample via the Cornish–Fisher expansion. Then utilize the Bayes estimation to address the reliability of the subgroups, thereby obtaining the confidence interval, which is used to develop the interval linguistic distribution preference relation (ILDPR) for the subgroups. Moreover, combine the reliability and the entropy measure constructed by the above four estimators to determine subgroup weights. Furthermore, present the expectation and variance of the ILDPR to sort the alternatives. Finally, demonstrate the feasibility and validity of the proposed LSGDM method based on a case and a comparison.

Two-rank multi-attribute group decision-making with linguistic distribution assessments: An optimization-based integrated approach

In real life, two-rank multi-attribute decision-making (MADM), in which all alternatives are divided into two preference-ordered categories, is common. In this paper, we investigate two-rank multi-attribute group decision-making (MAGDM) with linguistic distribution assessments (LDAs). A challenge when tackling such two-rank problems is establishing an LDAs-based two-rank model that maintains a balance between classification accuracy and computational complexity. When neither the threshold nor the number of alternatives within each category is specified in advance, determining individual two-rank results and subsequently aggregating the two-rank results for each decision-maker to resolve conflicts within the group is another challenge. Given these, we aim to propose a novel approach for two-rank MAGDM with LDAs. The main innovations and contributions of this paper are as follows. (a) From the perspective of linguistic scale function (LSF)-based cumulative expectations, we present a new LDAs-based distance measure that exhibits several desirable properties. A new score function for comparing LDAs is subsequently proposed using the new distance and the idea of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). (b) Using the minimum intensity of reversed rankings combined with the misclassification ratio of alternatives as an integrated objective function, we construct two 0-1 integer programming models incorporating constraints associated with the centers and priorities of categories to determine the optimal individual and group two-rank results of alternatives, respectively. (c) We apply our method to two-rank MAGDM associated with short video placement platforms. Comparing the proposed approach with other two-rank MAGDM approaches further demonstrates its effectiveness and rationality.

Canonical triangular interval type-2 fuzzy set linguistic distribution assessment TODIM approach: A case study of FMEA for electric vehicles DC charging piles

Linguistic distribution assessment (LDA) plays a prominent role in describing the subjective uncertainties and objective distribution of decision-maker (DM) opinions. Although there have been several studies on LDA, few of these studies have paid attention to the different understandings of language semantics provided by DMs. To address the issue, type-2 (T2) fuzzy sets (T2FSs) are a well-known approach because T2FSs can accurately model the understanding of individuals with linguistic uncertainty. A trapezium (or trapezoidal) interval type-2 fuzzy set (TrIT2FS) is a common application of T2 fuzzy logic. Accordingly, this paper discusses a novel LDA called a canonical triangular interval type-2 fuzzy set (CTriIT2FS)-LDA in a T2 fuzzy environment. The CTriIT2FS-LDA combines a CTriIT2FS simplified from a TrIT2FS with LDA, which can simplify the computation while retaining their advantages. To facilitate the calculation of the CTriIT2FS-LDA, the basic operations of the CTriIT2FSs and the CTriIT2F-LDA, such as the defuzzied (crisp) function, distance measurement, and entropy measurement, are defined. Based on the TODIM approach, a CTriIT2F-LDA-TODIM is developed for FMEA for interval type-2 fuzzy environments. Furthermore, the proposed approach is applied in a practical case for FMEA for the electric vehicle DC charging piles of the State Grid Corporation of China.

Improving consistency based on regret theory: A multi-attribute group decision making method with linguistic distribution assessments

Multi-attribute group decision making (MAGDM) problems widely exist in our daily life. In such problems, multiple decision makers (DMs) are involved and elicit their evaluations to assess a set of possible alternatives. Their complexity usually implies to deal with uncertain contexts and the use of linguistic preference modelling for the elicited assessments has been common. Linguistic distribution assessments (LDAs) applied to MAGDM, involving several linguistic terms associated with their symbolic proportions, have been effective to describe such assessments. However, to ensure a reasonable decision making result, the consistency of LDAs should be investigated and a corresponding MAGDM method proposed. This paper proposes a MAGDM method based on regret theory, not only to take the psychological behaviors of DMs into account, but also to reflect the consistency of assessments. Hence, the additive regret preference relation is firstly defined and then four algorithms are devised to improve the consistency of assessments. On this basis, two weighting determination models are built to calculate the weights of attributes and DMs respectively. Furthermore, alternatives can be ranked according to their overall synthetic values. The proposed MAGDM method is applied to an emergency assistance area selection problem and its result verifies the feasibility and effectiveness of the proposal.

A sentiment analysis-based two-stage consensus model of large-scale group with core-periphery structure

The development of big data and social media has driven large-scale group decision making (LSGDM) to merge with social networks and focus on individual behavioral factors. Following this trend, this paper develops a novel LSGDM consensus model that explores and manages the meso-scale structure among experts using free texts to express their opinions under social network settings. In the proposed approach, firstly the sentiment analysis is adopted to extract preferences over alternatives provided by experts and the preferences are further converted into distributed linguistic preference relation matrices. Then a core-periphery detection method for the social network constructed based on the newly defined distance measure for linguistic distribution assessments is proposed. After that, expert weights are derived by an optimization model that maximizes the expert reliability based on consistency and node centrality. Moreover, considering reference dependence and bounded rationality features of members among the detected network, a prospect theory-based two-stage consensus model is developed to improve group consensus systematically and gradually. Finally, a case study regarding life science investments is provided to illustrate the usefulness of our proposal. The convergence of the proposed model is proven by theoretical and simulation analysis. Comparative analysis reveals the features and advantages of our model.

Adaptive minimum adjustment consensus model for large-scale group decision making under social networks and its application in Integrated Care of Older People

Elderly care has become a vital livelihood issue as the aging population continues to grow. Generally, integrated care of older people (ICOPE) involves doctors, nurses, and family members. The service quality evaluation of integrated care can be modeled by social network large-scale group decision making (SN-LSGDM), and linguistic distribution assessment (LDA) is a powerful means to express evaluation information. However, it lacks the associated research. This paper proposes an adaptive minimum adjustment consensus model for SN-LSGDM with LDA and then applies it to evaluate elderly care modes. Firstly, trust relationships are updated according to the opinion similarity and the trust deviation. Then, a new clustering method based on trust density and opinion similarity is proposed to cluster DMs. Further, the expert and subgroup consensus levels are measured by considering the distance from the collective opinion. Moreover, adaptive consensus models are constructed in terms of the minimum adjustment of subgroups and experts. Finally, a case study of the service quality of ICOPE demonstrates the effectiveness of the new method, and comparison and sensitivity analysis reflect the advantages of the new approach.

Sustainable evaluation of energy storage technologies for wind power generation: A multistage decision support framework under multi-granular unbalanced hesitant fuzzy linguistic environment

Energy storage technology (EST) plays a foundational role for dealing with the intermittency of wind power and accelerating the structural revolution of renewable energy systems. Generally, EST selection is treated as a multiple-criteria group decision-making problem. However, stakeholders are not allowed to express multiple preferences via personalized linguistic distribution assessment and their risk appetites have received less attention in the existing approaches. This study aims at prioritizing ESTs by developing a novel multistage support framework where multi-granular unbalanced hesitant fuzzy linguistic term sets (UHFLTSs) are adopted to depict and quantify stakeholders’ opinions based on personalized semantics and granularities. A sustainable index system is devised in four dimensions (economic, technical, environmental and social) and the extended best-worst method (BWM) under multi-granular UHFLTSs environment is combined with maximum deviation method to determine the hybrid weights of criteria. A novel approach linking multi-granular UHFLTSs with double parameters TOPSIS method integrating risk appetite and optimism preference of stakeholders is further proposed by constructing optimization model, which can simultaneously yield the credible experts’ weights and prioritize the most desirable technology. The application of the proposed framework is demonstrated through an empirical case. Eventually, sensitivity analysis and comparative analysis are implemented to verify the effectiveness and validity of our proposal.

Consensus-driven methodology to managing diversity and complex linguistic ratings in quality function deployment: An optimization-based approach

Quality function deployment (QFD) is a quality management tool that can effectively transform customer requirements (CRs) into engineering characteristics (ECs) of services or products. It is a crucial process to determine the prioritization of ECs from the assessments of the relationship between CRs and ECs in QFD. This study designs several optimization models to derive the consensual prioritization of ECs from the diverse and complex assessments, in which the assessments of QFD team members are modelled by comparative linguistic expressions (CLEs). First, a consensus-driven optimization model (CDOM) is presented to manage CLEs by transforming them into linguistic distribution assessments. Then, a minimum adjustment element consensus model (MAECM) is developed to derive the consensual assessments by minimizing the number of adjustment elements between the members’ original and adjusted assessments. Following this, a technique for order preference by the similarity to ideal solution (TOPSIS) based method is used to derive the prioritization of ECs from the consensual assessments. A case study regarding the improvement of window services in the Bank of JT Shanghai Branch is proposed to illustrate the implementation of the presented method. The comparative analysis shows that the presented method outperforms previous methods in managing CLEs and consensus.

Service quality in football tourism: an evaluation model based on online reviews and data envelopment analysis with linguistic distribution assessments.

The emergence of sports tourism has compelled sports managers to rethink the management and improvement of sports facilities. Through service quality analysis, sports managers can identify the strengths and weaknesses of their activities for possible advancement. Hence, this study aims to develop a decision support model based on integrating online reviews and data envelopment analysis to measure the degree of tourist satisfaction and provide benchmarking goals for service improvement. The proposed model employs text mining techniques to discover service quality attributes from text reviews. According to the discovered service quality attributes, we conduct sentiment analysis to reveal the sentiment polarities of the text reviews. Then, we refine the polarities and ratings of online reviews into linguistic distribution assessments. Furthermore, we develop a linguistic distribution output-oriented non-discretionary bestpoint slack-based measure (BP-SBM) to compute the degree of tourist satisfaction and benchmarking goals. The linguistic distribution output-oriented non-discretionary BP-SBM can handle both positive and negative data values, thus overcoming the flaws of the traditional model. Meanwhile, the proposed decision support model investigates how the service-quality attributes interact to provide improvement pathways for an underperforming stadium based on association rule mining. We test the applicability of the proposed decision support model on some Elite stadia in Europe.

A VIKOR-Based Linguistic Multi-Attribute Group Decision-Making Model in a Quantum Decision Scenario

Quantum decision theory has been successfully applied to multi-attribute group decision-making (MAGDM) to model decision-makers’ interference and superposition effects in recent years. Existing quantum models assume that interference effects among decision-makers are symmetric. However, asymmetric interference effects have been ignored. We propose a VIKOR-based linguistic distribution assessments (LDAs) model considering asymmetric interference effects in a quantum decision scenario. Firstly, we combine VIKOR with LDAs to obtain a compromise solution in a fuzzy multi-attribute decision scenario with conflicting attributes. Secondly, an aggregation framework based on quantum probability theory is constructed to explore group preferences considering asymmetric interference effects among decision-makers. Finally, the model is compared with other methods to confirm its validity and stability.

A Quantum Framework for Modeling Interference Effects in Linguistic Distribution Multiple Criteria Group Decision Making

In most existing linguistic distribution multiple criteria group decision-making (MCGDM) models, the sample size of group linguistic distribution assessment (LDA) is often neglected, and the decision makers are viewed to be independent. The probabilistic distribution is closely related to the sample size, and there exist more or fewer interference effects among different individual opinions. Thus, this article develops a quantum framework for modeling interference effects in the linguistic distribution MCGDM process. First, considering the sample size information, we redefine the LDAs and provide a new computational model for them. Second, to integrate a series of LDAs, the linguistic distribution weighted averaging (LDWA) operator is presented. Mathematic proofs reveal that the new LDWA operator can not only ensure the information integrality but also can avoid the conjunctive and disjunctive operations. Third, to explore the interference effects among individual opinions, a quantum framework is constructed. In this process, individual opinions are viewed as various wave functions occurring synchronously. They interfere with each and influence the aggregation result. Finally, an illustrative example of Internet finance soft power evaluation is provided to verify the effectiveness. Sensitivity and comparative analyses are also implemented to assess the stability and validity of our method.

An integrated multi-granular distributed linguistic decision support framework for low-carbon tourism attraction evaluation

ABSTRACT With the increasing awareness about environmental protection, low-carbon tourism (LCT) is viewed as a new form of sustainable development that can provide greater economic, social and environmental benefits. Evaluating tourism attractions is of great significance for operators of tourism attractions to improve the service quality. Meanwhile, tourists can select the most appropriate LCT scenic spots among the alternatives. To address this decision-making problem, this study develops an integrated multi-criteria group decision-making method within the context of multi-granular linguistic distribution assessments. First, the best-worst method is employed to identify the weights of the criteria. Second, an extended relative entropy-based method that combines a proximity entropy weight and a similarity entropy weight is developed to assign weights to decision-makers in terms of each criterion. Third, an improved multi-granular linguistic distribution ORESTE (Organísation, rangement et Synthèse de données relarionnelles, in French) is proposed to prioritize LCT attractions. Finally, an illustrative example of LCT attraction evaluation followed by comparative and sensitivity analyses is presented to verify the applicability and effectiveness of the proposed framework.

Prioritizing Mobile Clinical Decision Support Applications for Personalized Healthcare Using an Integrated Merec-Probid Approach with Linguistic Distribution Assessments

Prioritizing Mobile Clinical Decision Support Applications for Personalized Healthcare Using an Integrated Merec-Probid Approach with Linguistic Distribution Assessments