Multi-granularity Linguistic Information Research Articles

A Gaussian interval type-2 fuzzy characterization method based on heterogeneous big data and its application in forest ecological assessment

The under-forest economy is a new agricultural and forestry production mode. It can improve the utilization efficiency of forest land and make full use of forest resources, forest space, and forest ecological environment through different forms of industries such as understory planting and breeding. The under-forest economy has important ecological and economic benefits, significant to rural revitalization and industrial structure adjustment. It has been widely discussed by people in recent years. The forest ecological security level is a core factor to be considered in selecting sites for the under-forest economy. In practice, some forest ecological security evaluation indexes usually show heterogeneous forms. To comprehensively evaluate the level of forest ecological security, this study constructs a new multi-indicator fuzzy evaluation method based on heterogeneous big data. Firstly, a new linguistic scale function is defined to realize the consistent conversion of multi-granularity linguistic information. A Gaussian interval type-2 fuzzy aggregation formula for heterogeneous large-scale data is proposed. This formula is used to describe the differences in evaluation information between individuals caused by different knowledge backgrounds of evaluators. Then, the Gaussian interval type-2 fuzzy cross-entropy is defined, and it is verified to satisfy the excellent properties. This cross-entropy is applied to determine the objective weights of evaluation indexes. Considering the risk-averse psychology of experts and combining the prospect theory, the MULTIMOORA method in the Gaussian interval type-2 fuzzy environment is proposed. Finally, the applicability of the constructed Gaussian interval type-2 fuzzy comprehensive evaluation method (GIT2FCE) is verified through a case study conducted on the site selection of the under-forest economic industry.

Shadowed AHP for Multi-Criteria Supplier Selection

Numerous techniques of multi-criteria decision-making (MCDM) have been proposed in a variety of business domains. One of the well-known methods is the Analytical Hierarchical Process (AHP). Various uncertain numbers are commonly used to represent preference values in AHP problems. In the case of multi-granularity linguistic information, several methods have been proposed to address this type of AHP problem. This paper introduces a novel method to solve this problem using shadowed fuzzy numbers (SFNs). These numbers are characterized by approximating different types of fuzzy numbers and preserving their uncertainty properties. The new Shadowed AHP method is proposed to handle preference values which are represented by multi-types of uncertain numbers. The new approach converts multi-granular preference values into unified model of shadowed fuzzy numbers and utilizes their properties. A new ranking approach is introduced to order the results of aggregation preferences. The new approach is applied to solve a supplier selection problem in which multi-granular information are used. The features of the new approach are significant for decision-making applications.

The consistent fuzzy suitability assessment of forest land resources with multi-source heterogeneous data.

In view of the suitability assessment of forest land resources, a consistent fuzzy assessment method with heterogeneous information is proposed. Firstly, some formulas for transforming large-scale real data and interval data into fuzzy numbers are provided. To derive the unified representation of multi-granularity linguistic assessment information, a fuzzy quantitative transformation for multi-granularity uncertain linguistic information is proposed. The proofs of the desirable properties and some normalized formulas for the trapezoidal fuzzy numbers are presented simultaneously. Next, the objective weight of each assessment indicator is further determined by calculating the Jaccard-Cosine similarity between the trapezoidal fuzzy numbers. Moreover, the trapezoidal fuzzy numbers corresponding to the comprehensive assessment values of each alternative are obtained. The alternatives are effectively ranked according to the distance from the centroid of the trapezoidal fuzzy number to the origin. Finally, based on the proposed consistent fuzzy assessment method, the suitability assessment of forest land resources is achieved under a multi-source heterogeneous data setting.

Research on Customer Requirement Prioritization of Engineering Products Based on Group Multi-Granularity Linguistic Information

Considering the ambiguity and uncertainty of information in the construction of an engineering product qualityhouse, a method of determining customer requirements priority based on group multi-granularity linguistic information isproposed. This method starts with experts giving subjective decision information on customer requirements. Secondly, thepriority of each demand can be obtained through consistency transformation and sequential optimization technology of theapproximate ideal scheme. The effectiveness and practicability of the method is proven with an application example.

A multi-attribute group decision model based on unbalanced and multi-granular linguistic information: An application to assess entrepreneurial competencies in secondary schools

Advances in multi-attribute group decision making require the development of structures flexible enough to deal with unbalanced and multi-granular linguistic information. New distances between linguistic terms are needed to aggregate opinions and measure consensus among decision makers with different profiles. In this paper, firstly, based on the lattice structure of hesitant fuzzy linguistic terms sets, a perceptual-based distance able to capture differences between unbalanced linguistic assessments is developed. Secondly, a projected algebraic structure is defined to deal with multi-perceptual group decision-making contexts where each decision maker has its own qualitative reasoning approach. To this end, a methodology to aggregate unbalanced linguistic information based on different perceptual maps is developed. This methodology can also deal with different multi-granularity linguistic environments. Finally, through an illustrative example based on real data provided by the Andorra Government in a pilot test, the proposed framework is applied to classify and rank a set of secondary students according to their degree of entrepreneurial competency.

Two-sided matching decision making with multi-granular hesitant fuzzy linguistic term sets and incomplete criteria weight information

Two-sided matching decision making (TSMDM) problems exist widely in human being’s daily life. For practical TSMDM problems, matching objects with different culture and knowledge backgrounds usually tend to provide linguistic assessments using different linguistic term sets (i.e., multi-granular linguistic information). Moreover, for TSMDM problems with high uncertainty, it is possible that matching objects may have some hesitancy and thus provide hesitant fuzzy linguistic term sets (HFLTSs). To model these situations, an approach to TSMDM with multi-granular HFLTSs is developed in the paper. In the proposed approach, some optimization models are first constructed to determine criteria weights for matching objects who do not provide clear criteria weight vectors. Afterwards, each matching object’s hesitant fuzzy linguistic decision matrix is aggregated to obtain his/her collective assessments over matching objects on the other side, which are denoted by multi-granular linguistic distribution assessments. These multi-granular linguistic distribution assessments are unified to obtain matching objects’ satisfaction degrees. Furthermore, an optimization model which aims to maximize the overall satisfaction degree of matching objects by considering the stable matching condition is then established and solved to determine the matching between matching objects. Eventually, an example for the matching of green building technology supply and demand is provided to demonstrate the characteristics of the proposed approach. Compared with previous studies, the proposed approach allows matching objects to provide linguistic assessments flexibly and can deal with the situations when incomplete criteria weight information is provided.

Fuzzy TOPSIS-EW Method with Multi-Granularity Linguistic Assessment Information for Emergency Logistics Performance Evaluation

The operation of emergency logistics plays a prominent role in reducing the consequences of disasters. Based on the establishment of a comprehensive evaluation system with the whole period of the disaster cycle that covers emergency preparation, response, and recovery, this paper proposes a fuzzy-symmetrical Technique for Order Preference by Similarity to Ideal Solution-Entropy Weight (TOPSIS-EW) method with multi-granularity linguistic assessment (MGLA) information to evaluate the performance of emergency logistics. Furthermore, the proposed evaluation method is employed to evaluate the performance of emergency logistics in Wenchuan earthquake, five worst-hit regions (i.e., Wenchuan County, Beichuan County, Qingchuan County, Mianzhu City, Shifang City) were ranked as V, III, I, II, IV, respectively. Finally, the effectiveness and reliability of the method are verified by comparison with the other two related methods and a sensitivity analysis. Based on the comprehensive evaluation results, some specific managerial suggestions are proposed to improve the emergency logistics capacity.

Research on Teleconsultation service quality based on multi-granularity linguistic information: the perspective of regional doctors

BackgroundDue to the increasing complexity in socioeconomic environments and the ambiguity in human cognition, decision makers prefer to give linguistic cognitive information with different granularities according to their own preferences. Consequently, to consider the uncertainty and preferences in the evaluation process, a method based on Multi-Granularity Linguistic Information (MGLI) for evaluating teleconsultation service quality is proposed, which provides a new research direction for scientific evaluation and improvement of teleconsultation service quality.MethodsFirstly, this paper explored a service quality evaluation system from the perspective of regional doctors. And then considering the uncertainty and preferences of decision makers, MGLI was used to optimize the index system according to the similarity degree between the linguistic evaluation information and a given linguistic term set. Finally, the empirical research was conducted using Henan Province Telemedicine Center of China (HTCC) as an example to identify the direction for improving the service quality in teleconsultation.ResultsThis study found that the number of consulting rooms, attitude of operators, consultation duration, charges, and attitude of experts are the key factors affecting the quality of teleconsultation service.ConclusionsSuggestions for improving the quality of teleconsultation service are put forward in terms of optimizing the allocation of consulting rooms, improving regional doctors’ experience and standardizing charging standards, which provides a new direction for improving the quality of teleconsultation service.

Consensus-Based Multicriteria Group Preference Analysis Model With Multigranular Linguistic Distribution Information

In this article, we deal with an improved fusion method for multigranular linguistic distribution assessments, a flexible and practical expression, in a multiple criteria group decision-making problem, which permits an accurate transformation between two linguistic labels without the loss of information. The importance degree of decision makers is allocated on the basis of individual consistency elicited from evaluation assessments represented by linguistic distributions, which is described with the aid of the proposed dominance degree of linguistic distribution. To intuitively reflect the group consensus level, a new measure, Jaccard consensus of linguistic distribution, is introduced. Based on this, a consensus-based multicriteria group preference analysis model with multigranular linguistic distribution information is established within a unified framework, which is developed by means of additive multiattribute value utility theory. Furthermore, a corresponding algorithm is matched to obtain a representative value function for decision analysis and an illustrative example is developed to illustrate the effectiveness of the proposed model.

Aplicación de un enfoque multigranular basado en el modelo lingüístico difuso 2-tupla para la evaluación de indicadores de política foresta

Introduction: The need for quality indicators is well recognized by users and proponents of public policy evaluation. Indicators recurrently include qualitative attributes for which there are few studies assessing the level of compliance. Objective: To apply a multigranular approach, based on the 2-tuple fuzzy linguistic model, to evaluate 13 indicators of the National Forestry Program, established in the system of social policy indicators derived from the National Development Plan 2012-2018 of Mexico. Materials and methods: The method uses the 2-tuple fuzzy linguistic representation model and an extension called extended linguistic hierarchies, designed to solve problems with multigranular linguistic information. The indicators’ level of compliance was evaluated based on four criteria: clarity, relevance, monitoring, and adequacy. Results and discussion: The structure defined in evaluating social policy indicators corresponds appropriately to that used with the 2-tuple fuzzy linguistic model. The evaluation resulted in a sorted list in which the indicator “Rate of change of timber forest production” had the best rating with a “very high” level of compliance; 10 other indicators had the “high” level of compliance, and the remaining two indicators were rated with “moderate” compliance. Conclusions: The 2-tuple fuzzy linguistic model allowed the appropriate evaluation of the level of compliance with the desirable attributes of indicators

Group multigranular linguistic QFD for prioritizing service designs with combined weighting method

AbstractQualification for the importance degree of service designs (SDs) is an essential stage in promoting accommodation performances to satisfy customers and gain market shares. Although various methods have been applied to derive the priorities of SDs, they do not effectively deal with the vagueness of information and the heterogeneity of decision makers (DMs). This study presents an improved quality function deployment (QFD) for prioritizing SDs. Multigranular unbalanced linguistic term sets are used to capture evaluators' ratings to cope with vague information. The multigranular linguistic information is unified by using a novel uniform method. This kind of information then is represented as linguistic distribution assessments using the concept of random preferences based on possibility interpretations of weight information. A consensus‐based mathematical programming model is established to determine the weights of DMs. Moreover, a combined structure that combines maximizing deviation and best worst methods is used to derive basic weights of SDs. Finally, an illustrative example of island accommodation management in Weizhou Island is provided to demonstrate the applicability and advantages of the proposed QFD framework.

An Aspiration-Based Approach for Qualitative Decision-Making With Complex Linguistic Expressions

Decisions are usually made based on not only the performances of alternatives but also are implied by how the performances satisfy the decision makers’ aspiration levels. This paper presents a linguistic aspiration-based solution to qualitative decision-making (QDM) where the aspiration levels and performances can be expressed by complex linguistic expressions (CLEs) such as hesitant fuzzy linguistic term sets and linguistic terms with weakened hedges. The proposed approach can deal with complex problems, which involve multi-criteria, multi-groups of experts, and multi-granular linguistic information. Based on the conventional aspiration-based approaches, the value function is defined by the probability of a CLE achieving its linguistic aspiration level. The performance of the proposed QDM approach is then demonstrated by solving the problem regarding the provider evaluation and selection. The proposed approach extends the range of available natural linguistic expressions that can be considered and used by experts and emphasizes the role of linguistic aspiration levels in QDM.

An Interactive Consensus Model for Group Decision-Making with Incomplete Multi-Granular 2-Tuple Fuzzy Linguistic Preference Relations

As a result of the heterogeneity of decision-makers, it is more suitable to use multi-granular linguistic information to express assessing information over alternatives for decision-makers. Decision-makers may only provide partial preference information as the limitation of knowledge over the alternatives. Thus, the incomplete multi-granular 2-tuple fuzzy linguistic preference relations (IMGFLPRs) can be applied to manage the GDM problems in complex environments. In this paper, we propose a novel group decision-making (GDM) model based on mathematical programming with IMGFLPRs. It is more important that the proposed mathematical programming can directly deal with IMGFLPRs and does not need to be converted into a uniform form. By this means, we construct a consensus reaching process based on IMGFLPRs, considering simultaneously dynamic adjustment of experts’ weight. Finally, an emergency plan selection problem is solved by the proposed model, which shows the result of GDM is effectiveness.

A large-scale group decision-making with incomplete multi-granular probabilistic linguistic term sets and its application in sustainable supplier selection

A large amount of stakeholders take part in the decision-making process, usually called a large-scale group decision-making (LGDM) problem. Some stakeholders may only provide partial preference information because of the limitation of knowledge over the alternatives. In this paper, a LGDM model is proposed to handle such problems, in which the incomplete multi-granular linguistic information showcases more appropriateness in respect of multi-stakeholders to represent their assessments. Meanwhile, the proposed model attains the maximum information from all decision makers and avoids an oversimplification for the elicited information in traditional linguistic models. It is more significant that we present three normalising methods for the purpose of securing the complete probabilistic linguistic term sets (PLTSs) based on risk attitudes: optimistic, pessimistic and neutral, respectively. In addition, alternatives are ranked by the extended TOPSIS method. Finally, a sustainable supplier selection is used to validate the effectiveness of the proposed model.

A new integrated fuzzy MCDM approach and its application to wastewater management

This paper proposes a fuzzy multi-criteria group decision making methodology that combines 2-tuple fuzzy linguistic representation model, linguistic hierarchies, Decision Making Trial and Evaluation Laboratory (DEMATEL) method and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The multigranular linguistic information obtained from decision-makers are unified and aggregated employing linguistic hierarchies and 2-tuple fuzzy linguistic representation model. The weights of the criteria are calculated employing DEMATEL method, which enables to consider inner dependencies among criteria. Then, fuzzy TOPSIS method is utilized to rank the alternatives. The developed methodology is able to handle information in a decision making problem with multiple information sources. Furthermore, it enables managers to deal with heterogeneous information without loss of information. The developed methodology is used to determine the most suitable wastewater treatment (WWT) alternative for Istanbul, the largest city of Turkey that is also listed among the world's most crowded cities.

Linguistic terms with weakened hedges: A model for qualitative decision making under uncertainty

When expressing the experts’ opinions in qualitative decision making (QDM), linguistic hedges can be considered to modify the force expressed by a predefined linguistic term. If an expert is not sure to select one term, weakened hedges would be a natural way to express the uncertainty. This is usually implemented by using a hedge to modify the most possible term, like the expression “more or less good”. To model the uncertainty implied by hedges in QDM, this paper presents a novel linguistic representational and computational model in which the linguistic expressions take the form of a weakened hedge and a linguistic term, which is named as linguistic term with weakened hedge (LTWH). The syntax of LTWHs is defined by a set of hedges and a set of linguistic terms. The semantics of a LTWH is determined, objectively, based on the semantics of the term and a similarity measure of the reference domain. Accordingly, the negation, order relations and some basic operations of LTWHs are defined. To illustrate the effectiveness of LTWHs in granular computing, the connection to some multi-granularity linguistic models is exploited and a process for unifying multi-granularity linguistic information is developed. The major contritions of this paper are: (1) The proposed model enables a new manner to express and operate uncertain linguistic information in QDM; (2) it possesses clear syntax and semantics and the computational results are very interpretable; and (3) the proposed solution of multi-granularity linguistic unification maintains the semantics of the original linguistic information.

Method of Multiple Attribute Group Decision Making Based on 2-Dimension Interval Type-2 Fuzzy Aggregation Operators with Multi-granularity Linguistic Information

This paper proposes an approach to linguistic multiple attribute group decision-making (MAGDM) problems with 2-dimension multi-granularity linguistic information by adding a subjective imprecise estimation of reliability of the linguistic assessment information for expressing fuzzy information appropriately. To avoid the drawback of traditional transformation functions of multi-granularity linguistic information (MGLI) defined in the basic linguistic term set or 2-tuple linguistic information, in this paper, decision information is fully expressed by using 2-dimension interval type-2 trapezoidal fuzzy number (2DIT2TFN) representation model under multi-granular linguistic contexts. Firstly, the definition, operational laws, and ranking method of 2DIT2TFNs are proposed. Then 2-dimension interval type-2 trapezoidal fuzzy ordered weighted average (2DIT2TFOWA) operator and quasi-2-dimension interval type-2 trapezoidal fuzzy ordered weighted average (quasi-2DIT2TFOWA) operator are put forward. Moreover, we apply new operators to developing approach to MAGDM problem with linguistic term set of different odd cardinalities. Finally, a numerical example related to health management is provided to illustrate the utility and effectiveness of the developed method. We also make comparisons between the methods proposed in this paper and some existing ones to confirm its feasibility and rationality. The main contribution of this paper possesses three points: (1) proposing the 2DIT2TFNs, which can reflect the evaluation on objects more reasonably; (2) developing some new aggregation operators under 2-dimension interval type-2 trapezoidal fuzzy environment; and (3) applying new operators to developing approach to MAGDM problem with MGLI.

Similarity-Based Approach for Group Decision Making with Multi-Granularity Linguistic Information

The aim of this article is to investigate the approach for multi-attribute group decision-making, in which the attribute values take the form of multi-granularity multiplicative linguistic information. Firstly, to process multiple sources of decision information assessed in different multiplicative linguistic label sets, a method for transforming multi-granularity multiplicative linguistic information into multiplicative trapezoidal fuzzy numbers is proposed. Then, a formula for ranking multiplicative trapezoidal fuzzy numbers is given based on geometric mean. Furthermore, the concept of similarity degree between two multiplicative trapezoidal fuzzy numbers is defined. The attribute weights are obtained by solving some optimization models. An effective approach for group decision making with multi-granularity multiplicative linguistic information is developed based on the ordered weighted geometric mean operator and proposed formulae. Finally, a practical example is provided to illustrate the practicality and validity of the proposed method.

Probabilistic linguistic vector-term set and its application in group decision making with multi-granular linguistic information

With the rapid information explosion and sharing, recommender systems (RS) play an auxiliary role in assisting the Internet users to make decision especially in the e-service platform. Normally, the information in this process is related to opinions and preferences, which are usually expressed through a qualitative way such as linguistic evaluation terms (LETs). However, the LETs may come from different sources such as experts, users, etc., which makes the linguistic evaluation scales (LESs) used in this process probably be different due to their different backgrounds and levels of knowledge. The diversity and flexibility of these LESs determine the quality of information, and further affect the effectiveness of a RS. In this paper, we focus on improving the accuracy of the multi-granular linguistic recommender system by supporting customers to find out the most eligible items according their own preferences. We first propose the probabilistic linguistic vector-term sets (PLVTSs) to promote the application of multi-granular linguistic information. Based on the PLVTSs, we then develop a novel algorithm to tackle multi-attribute group decision making (MAGDM) problems with multiple LESs. Furthermore, the effectiveness of the PLVTSs is validated by an illustration of personalized hospital selection-recommender problem. Finally, we point out some possible research directions regrading to the PLVTSs.

A Method for Dealing with Multi-granularity and Multi-scale Linguistic Information based on Modified Proportional 2-tuple

Multi-granularity linguistic information has been studied by researchers in many disciplines, in which the scale of linguistic term sets is usually restricted in domain [0, 1]. In this paper, we consider the multiple attribute group decision making (MAGDM) problems, in which the evaluation of each alternative with respect to each attribute is provided by several experts of the corresponding field. In order to convey the preferences of different experts exactly and to describe the characteristics of different attributes, linguistic term sets with different granularities and different scales have been proposed to express their evaluation values. Moreover, the performance values given by the experts take the form of modified proportional 2-tuples. In this process, the modified proportional 2-tuple, its comparative rules as well as several aggregation operators have been proposed. A new method has been proposed to achieve a basic modified linguistic term set (BMLTS) and a transformation function has been defined to make the performance values uniform. An example is given to illustrate the applicability and flexibility of the method.