Intuitionistic Uncertain Linguistic Variables Research Articles

A multi-aspect coordination HDRED site selection framework under multi-type heterogeneous environments

Site selection is a critical link in hot dry rock exploration and development (HDRED). However, some problems such as incoordination of the qualitative and quantitative criteria, inaccurate information descriptions, information loss or distortion, incomplete consideration of the coordinating influence of the subjective-objective aspects, and criteria’s complex interactions exist in the extant site selection methods. To overcome those deficiencies, an innovative multi-aspect coordination framework under heterogeneous environments is proposed to select the optimal HDRED site. First, a comprehensive HDRED site selection criteria system is established from the perspective of sustainability. Second, multi-type heterogeneous information of crisp numbers, interval numbers, triangular intuitionistic fuzzy numbers, and interval-valued intuitionistic uncertain linguistic variables is adopted to accurately describe criteria, which not only can achieve varied criteria coordination, but improves the collection and processing of criteria information. Third, the heterogeneous entropy-DEMATEL model is proposed to more reasonably determine weights. Next, the heterogeneous TOPSIS is constructed to rank alternatives while avoiding information distortion or loss. Finally, a case from China is considered for comparative analysis and sensitivity analysis. The results indicate that the proposed framework can effectively promote the large-scale development of hot dry rock and has strong practical and popularizing value.

Multi-attribute group decision-making method based on multi-granulation weights and three-way decisions

With the increasing complexity of decision-making problems and environment, the integration and fusion of three-way decisions, rough set and multi-attribute group decision-making (MAGDM) have become a major trend in the field of decision analysis. Although many researchers have presented various MAGDM methods under different environments, there are still some imperfections, such as the weight information is not comprehensive or flexible enough, the decision results lack interpretability, and the impact of risk attitude is not fully taken into account. In order to overcome the above shortcomings and improve the scientificity and rationality of decision-making, a novel data-driven MAGDM method under interval-valued intuitionistic uncertain linguistic environment is established based on the idea of multi-granulation and three-way decisions. Our contributions can be identified as follows: (1) The multi-granulation weight mining and fusion methods for experts and attributes are proposed, respectively; (2) The coarse-granulation grading information based on three-way decisions is developed to enhance the interpretability and reference value of decision results; (3) The expected value with risk attitude factor is defined to compare interval-valued intuitionistic uncertain linguistic variables (IVIULVs) and then is used to grade and rank alternatives under different risk attitudes. To illustrate the feasibility and practicality of the proposed method, a case of logistics supplier selection in e-commerce enterprises is demonstrated. Furthermore, the advantages and characteristics of the proposed method are highlighted via detailed comparison and thorough analysis.

Interval-Valued Intuitionistic Uncertain Linguistic Multi-attribute Decision-Making Method for Plant Location Selection with Partitioned Hamy Mean

Plant location selection (PLS) is a typical and complex multi-attribute group decision-making (MAGDM) problem, which requires consideration of several alternatives and multiple attributes. However, existing research results on PLS lack sufficient consideration of complex relationship patterns between attributes, which often leads to inaccurate decision results. Thus, in this paper, a new MAGDM technique is proposed to solve the problem of PLS, in which complex interrelationship structures exist between multi-inputs and attribute values are represented as interval-valued intuitionistic uncertain linguistic variables (IVIULVs). First, to solve the shortcomings of the existing operations, some new operational rules are redefined for IVIULVs based on linguistic scale functions and Archimedean T-conorms and T-norms. These new rules of operation hold the closedness, and are flexible for semantic transformation processing. Then, to more accurately reflect the partition structure and the complex interrelationship pattern among multi-input arguments, we extend the traditional Hamy mean and propose the partitioned Hamy mean (PHAM), which can eliminate the impact of unrelated attributes on the results and meet the semantic conversion needs of different decision makers. Furthermore, we develop two new fuzzy linguistic aggregation operators: interval-valued intuitionistic uncertain linguistic PHAM (IVIULPHAM) and its weighted version (IVIULWPHAM). Finally, a novel MAGDM technique (IVIUL-MAGDM) based on the IVIULWPHAM operator is proposed to solve the problem of PLS. Several examples of PLS are also illustrated to show the validity and advantages of the IVIUL-MAGDM method by comparing with the other existing MAGDM methods.

Three-Way Decisions with Intuitionistic Uncertain Linguistic Decision-Theoretic Rough Sets Based on Generalized Maclaurin Symmetric Mean Operators

As a typical model of three-way decisions (3WDs), decision-theoretic rough set (DTRS) has received extensive attention from researchers in the decision-making fields. Intuitionistic uncertain linguistic variables (IULVs) combine the advantages of intuitionistic fuzzy sets (IFSs) and uncertain linguistic variables (ULVs), IULV is more flexible in dealing with uncertain information in decision-making process, and provides a novel means for obtaining loss function (LF) of DTRSs. To get more comprehensive results, a new 3WD model is proposed to solve the multi-attribute group decision-making (MAGDM) problem. First, we gave the LF of DTRSs with IULVs, combined the IULVs and the generalized Maclaurin symmetric mean (GMSM), and proposed the IULGMSM and WIULGMSM operators to aggregate decision information; further, we proposed an intuitionistic uncertain linguistic DTRS model. Then, a method for deducing a new DTRS model is constructed, which can give the corresponding semantic interpretation of the decision results of each alternative. Finally, an example is applied to elaborate the proposed method in detail, and the effects of different conditional probabilities on decision results are discussed.

Generalized Hamacher Aggregation Operators for Intuitionistic Uncertain Linguistic Sets: Multiple Attribute Group Decision Making Methods

In this paper, we consider multiple attribute group decision making (MAGDM) problems in which the attribute values take the form of intuitionistic uncertain linguistic variables. Based on Hamacher operations, we developed several Hamacher aggregation operators, which generalize the arithmetic aggregation operators and geometric aggregation operators, and extend the algebraic aggregation operators and Einstein aggregation operators. A number of special cases for the two operators with respect to the parameters are discussed in detail. Also, we developed an intuitionistic uncertain linguistic generalized Hamacher hybrid weighted average operator to reflect the importance degrees of both the given intuitionistic uncertain linguistic variables and their ordered positions. Based on the generalized Hamacher aggregation operator, we propose a method for MAGDM for intuitionistic uncertain linguistic sets. Finally, a numerical example and comparative analysis with related decision making methods are provided to illustrate the practicality and feasibility of the proposed method.

Group decision making method based on hybrid aggregation operator for intuitionistic uncertain linguistic variables

Group decision making method based on hybrid aggregation operator for intuitionistic uncertain linguistic variables

Multi-Attribute Group Decision Making Based on Intuitionistic Uncertain Linguistic Hamy Mean Operators With Linguistic Scale Functions and Its Application to Health-Care Waste Treatment Technology Selection

How to select an appropriate and effective health-care waste treatment technology (HCW-TT) is an especially important task in the management of health-care waste (HCW), which can be regarded as a typical multi-attribute group decision making (MAGDM) problem. In the selection of HCW-TT, the expression of evaluation information given by decision makers (DMs) under uncertain decision-making environment and the reflection of interrelationships among multi-attributes are two critical issues. In response, a new MAGDM technique is presented for the selection of HCW-TT based on the intuitionistic uncertain linguistic Hamy mean with linguistic scale functions (LSFs). First, considering the lack of closeness and flexibility of existing operations, some new operations of intuitionistic uncertain linguistic variables (IULVs) are redefined by combining with LSFs. New expected value and accuracy function are also presented to compare IULVs. Then, based on the new operations of IULVs, the intuitionistic uncertain linguistic Hamy mean and its weighted version (IULWHAM) are proposed to aggregate IULVs. The proposed operators can simultaneously model the interrelationship among multi-inputs and handle the semantic translation requirements of different DMs. Meanwhile, several attractive properties and special cases of these two operators are studied and analyzed. Subsequently, a MAGDM method (IUL-MAGDM) is presented based on the proposed IULWHAM. Finally, a numerical example is given to demonstrate the proposed IUL-MAGDM method, and results indicate that the proposed IUL-MAGDM can effectively and flexibly handle the selection of HCW management by comparing with other IUL-MAGDM methods.

An overview of intuitionistic linguistic fuzzy information aggregations and applications

Purpose Intuitionistic linguistic fuzzy information (ILFI), characterized by linguistic terms and intuitionistic fuzzy sets (IFSs), can easily express the fuzzy information in the process of muticriteria decision making (MCDM) and muticriteria group decision making (MCGDM) problems. The purpose of this paper is to provide an overview of aggregation operators (AOs) and applications of ILFI. Design/methodology/approach First, some meaningful AOs for ILFI are summarized, and some extended MCDM approaches for intuitionistic uncertain linguistic variables (IULVs), such as extended TOPSIS, extended TODIM, extended VIKOR, are discussed. Then, the authors summarize and analyze the applications about the AOs of IULVs. Findings IULVs, characterized by linguistic terms and IFSs, can more detailed and comprehensively express the criteria values in the process of MCDM and MCGDM. Therefore, lots of researchers pay more and more attention to the MCDM or MCGDM methods with IULVs. Originality/value The authors summarize and analyze the applications about the AOs of IULVs Finally, the authors point out some possible directions for future research.

Some intuitionistic uncertain linguistic Bonferroni mean operators and their application to group decision making

With respect to multi-attribute group decision-making (MAGDM) problems in which the attribute values take the form of intuitionistic uncertain linguistic variables (IULVs), the group decision-making methods based on some Bonferroni mean (BM) aggregation operators were developed. Firstly, we proposed some new operational rules for the IULVs which can overcome the weaknesses of the existing operations. Then, we extended BM operator to the IULVs and developed intuitionistic uncertain linguistic arithmetic Bonferroni mean (IULABM) operator, intuitionistic uncertain linguistic arithmetic weighted Bonferroni mean (IULWABM) operator, intuitionistic uncertain linguistic geometric Bonferroni mean (IULGBM) operator, and intuitionistic uncertain linguistic weighted geometric Bonferroni mean (IULWGBM) operator. At the same time, some desirable properties of the proposed operators, such as idempotency, boundedness, and monotonicity, were studied, and some special cases of these operators were analyzed. Moreover, some approaches based on the developed operators are proposed. Finally, an illustrative example is given to show the steps of the developed methods and to discuss the influences of different parameters on the decision-making results.

A New Procedure to Intuitionistic Uncertain Linguistic Group Decision Making

Intuitionistic uncertain linguistic variables (IULVs) are useful to express the qualitative and quantitative recognitions of decision makers. However, after reviewing the previous operational laws on IULVs, we find there are some limitations. To address these issues, we define several new operations on IULVs and give a new ranking method. To improve the utilization of IULVs, this paper defines two Choquet operators: the intuitionistic uncertain linguistic symmetrical Choquet averaging (IULSCA) operator and the intuitionistic uncertain linguistic symmetrical Choquet geometric mean (IULSCGM) operator, which can address the internal correlations among elements. To globally reflect the interactive characteristics of the importance of elements, two generalized Shapley intuitionistic uncertain linguistic symmetrical Choquet operators are presented. Subsequently, a new distance measure is defined, which is then used to build models to ascertain fuzzy measures on decision maker and criteria sets to address the case where the weighting information is partly known. After that, a new procedure to intuitionistic uncertain linguistic group decision making is developed. Finally, a specific example is offered to illustrate the practicality of the new procedure, and the comparison analysis is also made.

An extended TODIM method for multiple attribute group decision making based on intuitionistic uncertain linguistic variables

The intuitionistic uncertain linguistic variables are the good tools to express the fuzzy information, and the TODIM (an acronym in Portuguese of Interactive and Multicriteria Decision Making) method can consider the bounded rationality of decision makers based on the prospect theory. However, the classical TODIM method can only process the multiple attribute decision making (MADM) problems where the attribute values take the form of crisp numbers. In this paper, we will extend the TODIM method to the multiple attribute group decision making (MAGDM) with intuitionistic uncertain linguistic information. Firstly, the definition, characteristics, expectation, comparison method and distance of intuitionistic uncertain linguistic variables are briefly introduced, and the steps of the classical TODIM method for MADM problems are presented. Then, on the basis of the classical TODIM method, the extended TODIM method is proposed to deal with MAGDM problems with intuitionistic uncertain linguistic variables, and its significant characteristic is that it can fully consider the decision makers' bounded rationality which is a real action in decision making. Finally, an illustrative example is proposed to verify the developed approach.

Some intuitionistic uncertain linguistic Heronian mean operators and their application to group decision making

With respect to multiple attribute group decision making (MAGDM) problems in which both the attribute weights and the expert weights take the form of crisp numbers, and attribute values take the form of intuitionistic uncertain linguistic variables, some new intuitionistic uncertain linguistic Heronian mean operators, such as intuitionistic uncertain linguistic arithmetic Heronian mean (IULAHM) operator, intuitionistic uncertain linguistic weighted arithmetic Heronian mean (IULWAHM) operator, intuitionistic uncertain linguistic geometric Heronian mean (IULGHM) operator, and intuitionistic uncertain linguistic weighted geometric Heronian mean (IULWGHM) operator, are proposed. Furthermore, we have studied some desired properties of these operators and discussed some special cases with respect to the different parameter values in these operators. Moreover, with respect to multiple attribute group decision making (MAGDM) problems in which both the attribute weights and the expert weights take the form of real numbers, attribute values take the form of intuitionistic uncertain linguistic variables, some approaches based on the developed operators are proposed. Finally, an illustrative example has been given to show the steps of the developed methods and to discuss the influences of different parameters on the decision-making results.

Some geometric aggregation operators based on interval intuitionistic uncertain linguistic variables and their application to group decision making

With respect to multiple attribute group decision making (MAGDM) problems in which both the attribute weights and the expert weights take the form of crisp numbers, and attribute values take the form of interval-valued intuitionistic uncertain linguistic variables, some new group decision making analysis methods are developed. Firstly, some operational laws, expected value and accuracy function of interval-valued intuitionistic uncertain linguistic variables are introduced. Then, an interval-valued intuitionistic uncertain linguistic weighted geometric average (IVIULWGA) operator and an interval-valued intuitionistic uncertain linguistic ordered weighted geometric (IVIULOWG) operator have been developed. Furthermore, some desirable properties of the IVIULWGA operator and the IVIULOWG operator, such as commutativity, idempotency and monotonicity, have been studied, and an interval-valued intuitionistic uncertain linguistic hybrid geometric (IVIULHG) operator which generalizes both the IVIULWGA operator and the IVIULOWG operator, was developed. Based on these operators, an approach to multiple attribute group decision making with interval-valued intuitionistic uncertain linguistic information has been proposed. Finally, an illustrative example is given to verify the developed approaches and to demonstrate their practicality and effectiveness.

Methods for aggregating intuitionistic uncertain linguistic variables and their application to group decision making

With respect to multiple attribute group decision making (MAGDM) problems in which the attribute weights and the expert weights take the form of real numbers and the attribute values take the form of intuitionistic uncertain linguistic variables, new group decision making methods have been developed. First, operational laws, expected value definitions, score functions and accuracy functions of intuitionistic uncertain linguistic variables are introduced. Then, an intuitionistic uncertain linguistic weighted geometric average (IULWGA) operator and an intuitionistic uncertain linguistic ordered weighted geometric (IULOWG) operator are developed. Furthermore, some desirable properties of these operators, such as commutativity, idempotency, monotonicity and boundedness, have been studied, and an intuitionistic uncertain linguistic hybrid geometric (IULHG) operator, which generalizes both the IULWGA operator and the IULOWG operator, was developed. Based on these operators, two methods for multiple attribute group decision making problems with intuitionistic uncertain linguistic information have been proposed. Finally, an illustrative example is given to verify the developed approaches and demonstrate their practicality and effectiveness.