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Study on Interval Intuitionistic Fuzzy Multi-Attribute Group Decision Making Method based on Choquet Integral

Study on Interval Intuitionistic Fuzzy Multi-Attribute Group Decision Making Method based on Choquet Integral

In this paper, a method based on Choquet integral is proposed to solve the interval intuitionistic fuzzy multiple attribute group decision making problems. Firstly, some concepts about interval intuitionistic fuzzy measure are defined, through the strict mathematical reasoning to prove the measure we proposed satisfying the axiomatic system of fuzzy measure. Then, on the basis of fuzzy measure and game theory, we propose two models to determine fuzzy measure based on interval intuitionistic fuzzy entropy and weight information matrix. By calculating the Shapely value to determine expert weights, we establish a linear programming model based on relative entropy to determine the fuzzy measure of attribute weights to reflect the interactive characteristics among the criteria, using the Choquet integral to aggregate the decision-making information. Finally, we give the process of decision making in details.
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Dynamic multi-attribute decision making model based on triangular intuitionistic fuzzy numbers

Dynamic multi-attribute decision making model based on triangular intuitionistic fuzzy numbers

In many decision areas, such as multi-period investment and personnel dynamic examination, the decision information is usually collected at different periods. Thus, it is necessary to develop some dynamic decision making models to deal with these multi-period and multi-attribute decision making prob- lems (also known as Dynamic Multi-Attribute, Decision Mak- ing (DMADM) problems [ 12 ]). Recently, research on DMADM problems has received some attention [ 12–15 ]. Xu [ 12 ] devel- oped a multi-period and multi-attribute decision making model based on a simple additive weighting method. Lin et al. [ 13 ] pro- posed a dynamic multi-attribute decision making model, where the attribute values are firstly aggregated into an overall evalu- ation value at each period, then all evaluation values are aggre- gated into an overall score of all alternatives. Xu and Yager [ 14 ] investigated a dynamic multi-attribute decision making prob- lem where the decision information takes the form of the inter- val uncertain information. Wei [ 15 ] developed two aggregation operators to solve a dynamic multi-attribute decision making problem where the decision information also takes the form of the interval uncertain information. All existing research focuses on DMADM problems where the decision information takes the form of a real number or interval uncertain information. Nev- ertheless, in many practical cases, the available decision infor- mation is usually difficult to judge precisely; instead, they can be easily characterized by some fuzzy linguistic terms, such as ‘‘good’’, ‘‘poor’’ and so on. In addition, triangular intuitionis- tic fuzzy numbers in the Intuitionistic Fuzzy sets (IFs) can not only deal with vagueness information, but also express more
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Supplier selection with multi-criteria group decision making based on interval-valued intuitionistic fuzzy sets (case study on a project-based company)

Supplier selection with multi-criteria group decision making based on interval-valued intuitionistic fuzzy sets (case study on a project-based company)

Supplier selection can be considered as a complicated multi criteria decision-making problem. In this paper the problem of supplier selection is studied in the presence of conflicting evaluations and insufficient information about the criteria and different attitudes of decision makers towards the risk. Most of fuzzy approaches used in multi- criteria group decision making (MCGDM) are non-intuitionistic, which significantly restricts their application areas. Because of considering belongingness and non- belongingness of the issue in a same time, intuitionistic fuzzy sets can better encounter with a real supplier selection problem. Also to deal with different attitudes of decision makers toward the risk, the proposed approach in this paper employs a new decision function to participate this factor in decision process. In order to integrate fuzzy information, interval-valued intuitionistic fuzzy ordered weighted aggregation (IIFOWA) is applied to aggregate the obtained preferences. The influence of unfair arguments in final results can be reduced by assigning low weights to the “optimistic” or “pessimistic” discretions. Ranking process is based on the two indices, weighted score function and weighted accuracy function. To demonstrate the efficiency of the proposed approach, it is implemented to supplier selection in a project-based company.
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Advancements in Multi-Criteria Decision Making Based on Interval-Valued Intuitionistic Fuzzy Set

Advancements in Multi-Criteria Decision Making Based on Interval-Valued Intuitionistic Fuzzy Set

Guiwu Wei and Gang Lan[16] proposed a modified grey relational analysis (GRA) method and used the traditional GRA method for calculating steps for solving interval-valued intuitionistic fuzzy multiple attribute decision-making problems with known weight information. The degree of grey relation between every alternative and positive ideal solution and negative ideal solution are calculated. Then, according to the concept of the GRA, a relative relational degree is defined to determine the ranking order of all alternatives by calculating the degree of grey relation to both the positive-ideal solution (PIS) and negative-ideal solution (NIS) simultaneously. Wei Yang and Yongfeng Pang [17] also worked on GRA method with IVIFS to solve MCDM problems.
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Study on the Development of Decision Making Using Intuitionistic Fuzzy Set (IFS) and Interval Valued Intuitionistic Fuzzy Set (IVIFS)

Study on the Development of Decision Making Using Intuitionistic Fuzzy Set (IFS) and Interval Valued Intuitionistic Fuzzy Set (IVIFS)

__________________________________________________________________________________________ Abstract: Out of several higher order fuzzy sets [1], the concept of an intuitionistic fuzzy set (IFS) [2] introduced by Atanassov has been found to be highly useful to deal with vagueness and imprecision. IFS theory has been extensively applied to areas like Artificial Intelligence, networking, Soft decision making, Programming logic, operational research etc. One the promising role of IFS has been emerged in Decision making problems specially group decision making and multi-attribute decision making. In some real-life situations, decision makers may not be able to accurately express their view for the problem as they may not possess a precise or sufficient level of knowledge of the problem or the decision makers are unable to discriminate explicitly the degree to which one alternative are better than others in such cases, the decision maker may provide their preferences for alternatives to a certain degree, but it is possible that they are not so sure about it [3]. Thus, it is very suitable to express the decision maker preference values with the use of fuzzy/intuitionistic fuzzy values rather than exact numerical values or linguistic variables [4]. To satisfy the need of decision making problem with imprecision and uncertainty many researchers have been concentrated on IFS theory. In this paper we reviewed the development of different approaches for solving decision making problem using IFS theory and a brief introduction on the role of Interval Valued IF sets (IVIFS) [5] in multiattribute decision making.
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Supplier Selection Problem Based on Interval Intuitionistic Fuzzy Multiattribute Group Decision Making

Supplier Selection Problem Based on Interval Intuitionistic Fuzzy Multiattribute Group Decision Making

With the development of economy and society, the competition between enter- prises is no longer the unilateral competition between price and quality, but the competition between supply chains. The supplier is at the source of the supply chain and plays a very core role in the whole supply chain. Choosing the right supplier is a good foundation for enterprise development. The evaluation and selection of suppliers is not the individual behavior of purchasers, but actually a complex multi-attribute group decision-making problem. Generally, there are not only quantitative indicators in the index system, such as product price and market share, but also qualitative indicators, such as product research and de- velopment capacity, after-sales maintenance level, etc., and the relationship be- How to cite this paper: Song, D.Y. and
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An approach to interval-valued intuitionistic fuzzy decision making based on induced generalized symmetrical Choquet Shapley operator

An approach to interval-valued intuitionistic fuzzy decision making based on induced generalized symmetrical Choquet Shapley operator

In Section 2, some basic concepts are briey re- viewed, including Interval-Valued Intuitionistic Fuzzy Sets (IVIFSs), fuzzy measures, the Choquet integral, two Choquet integral operators, and the generalized Shapley function. Meanwhile, it analyzes the lim- itations of the previous operations on IVIFSs. In Section 3, the IG-SIVIFCS operator is dened, and several important cases are investigated. Furthermore, some desirable properties are studied. In Section 4, several distance measure-based models for determining the optimal fuzzy measure on the attribute set are established, and an approach to interval-valued intu- itionistic fuzzy multi-attribute decision-making with incomplete weighting information is developed that considers the interactions. In Section 5, an illustrative example is provided to show the concrete application
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MULTI-CRITERIA GROUP DECISION-MAKING USING AN EXTENDED EDAS METHOD WITH INTERVAL TYPE-2 FUZZY SETS

MULTI-CRITERIA GROUP DECISION-MAKING USING AN EXTENDED EDAS METHOD WITH INTERVAL TYPE-2 FUZZY SETS

Multi-criteria decision-making (MCDM) methods are very useful in the real-world decision-making problems. We are usually confronted with the decision-making process in an uncertain environment, and the fuzzy set theory is an effi cient tool to handle this uncertainty. Interval type-2 fuzzy sets are one of the extensions of the fuzzy sets which are very fl exible to model an uncertain environment. This study is related to MCDM problems within the context of interval type-2 fuzzy sets (IT2FSs). The evaluation based on distance from average solution (EDAS) method is a new and effi cient MCDM method, and assessment of alternatives in this method is based on the distance of them from average solution with respect to all criteria. In the EDAS method, each alternative has positive and negative distances which are used to determine the appraisal score of it. In this research, we present an extended EDAS method, which is named EDAS-IT2FSs, for dealing with multi- criteria group decision-making problems with interval type-2 fuzzy sets. Basic concepts of interval type-2 fuzzy sets and the arithmetic operations of trapezoidal IT2FSs are used to develop the extended EDAS method. A numerical example of multi-criteria subcontractor evaluation problem is used to illustrate the process of using the extended EDAS method. The example involves eight subcontractors that need to be evaluated with respect to seven criteria. A comparison and a sensitivity analysis based on different sets of criteria weights are also performed to show the validity of the proposed method. The results of these analyses show the effi ciency and stability of the extended EDAS method.
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A Novel TOPSIS MABAC Method for Multi attribute Decision Making with Interval Neutrosophic Set

A Novel TOPSIS MABAC Method for Multi attribute Decision Making with Interval Neutrosophic Set

some decision problems. Then some aggregation operators based on these were proposed by Xu [5-6] and some methods for MADM with IFS were proposed in [7-8]. Furthermore, Atanassov and Gargov [4,9] extended the membership function and non-membership function to interval numbers and proposed interval-value intuitionistic fuzzy set (IVIFS). But IFS and IVIFS can only deal with incomplete information, but not uncertain and inconsistent information.

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Extended TOPSIS method for multi-criteria group decision-making problems under cubic intuitionistic fuzzy environment

Extended TOPSIS method for multi-criteria group decision-making problems under cubic intuitionistic fuzzy environment

However, apart from that Technique for Or- der Preference with respect to the Similarity to the Ideal Solution (TOPSIS), developed by Hwang and Yoon [17], there is a well-known Multi-Criteria Decision-Making (MCDM) method. The aim of this method is to choose the best alternative whose distance from its positive ideal solution is the shortest. After their existence, numerous attempts are made by the researchers to apply the TOPSIS method under the fuzzy and IFS environment. For instance, Szmidt and Kacprzyk [18] dened the concept of distance measure between the IFSs. Hung and Yang [19] presented the similarity measures between the two dierent IFSs based on Hausdor distance. Boran et al. [20] applied the TOPSIS method to solve the problem of human resource personnel selection. Dugenci [21] presented a distance measure for IVIF set and their application to MCDM with incomplete weight information. Garg [22] presented a generalized improved score function for IVIFSs and their TOPSIS-based method for solving the DM problems. Mohammadi et al. [23] presented a gray relational analysis and TOPSIS approach to solving the DM problems. Garg et al. [24] presented a generalized entropy measure of order and degree under the IFS environment and applied it to solve the DM problems. Biswas and Kumar [25] presented an integrated TOPSIS approach for solving the DM prob- lems with IVIFS environment. Vommi [26] presented a TOPSIS method using statistical distances to solve DM problems. Singh and Garg [27] developed the distance measures between the type-2 IFS. Li [28] presented a nonlinear programming methodology-based TOPSIS method for solving Multi-Attribute Decision Making (MADM) problems under IVIFS environment. Garg and Arora [29] extended the Li [28] approach to the interval-valued intuitionistic fuzzy soft set environ- ment. Lu and Ye [30] developed logarithm similarity measures to solve the problems under interval-valued fuzzy set environment. Garg and Kumar [31] presented new similarity measures for IFSs based on the connec- tion number of the set pair analysis. Askarifar et al. [32] presented an approach to studying the framework of Iran's seashores using TOPSIS and best-worst MCDM methods. In [33,34], the authors developed a group DM method under IVIF environment by integrating extended TOPSIS and linear programming methods. Kumar and Garg [35,36] presented the TOPSIS ap- proach for solving DM problems by using connection number of the set pair analysis theory.
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Some Generalized Einstein Aggregation Operators Based on the Interval-Valued Intuitionistic Fuzzy Numbers and Their Application to Group Decision Making

Some Generalized Einstein Aggregation Operators Based on the Interval-Valued Intuitionistic Fuzzy Numbers and Their Application to Group Decision Making

In this paper, we explored some generalized Einstein aggregation operators based on IVIFNs and applied them to the multi-attribute group decision making problems where attribute values are the IVIFNs. Firstly, Interval-Valued Intuitionistic Fuzzy General- ized Einstein Weighted Averaging (IVIFGEWA) oper- ator, Interval-Valued Intuitionistic Fuzzy Generalized Einstein Ordered Weighted Averaging (IVIFGEOWA) operator, and Interval-Valued Intuitionistic Fuzzy Gen- eralized Einstein Hybrid Weighted Averaging (IV- IFGEHWA) operator were proposed. Some of their general properties such as idempotency, commutativ- ity, monotonicity, and boundedness, were studied, and some special cases of them were analyzed. Further- more, a method to multi-criteria group decision making based on these operators was developed, and the operational processes were illustrated in detail. Finally, an illustrative example was given to show the decision steps of the proposed method and to demonstrate their eectiveness. In further research, it is necessary and signicant to give the applications of these operators to the other domains such as pattern recognition, fuzzy cluster analysis, uncertain programming, etc.
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A projection-based approach to intuitionistic fuzzy group decision making

A projection-based approach to intuitionistic fuzzy group decision making

Compared with the Euclidean distance or the Hamming distance, projection measurement is a com- prehensive consideration. It has been widely used in many decision making problems. For example, using projection measure instead of distance measure, Yue and Jia [48] introduced a GDM model with hybrid intuitionistic fuzzy information. Xu and Hu [49] established two projection models for GDM problems. Wei [50] proposed an MADM method based on the projection technique, in which the attribute values were characterized by IFNs. Zheng et al. [51] developed an application of improved grey projection method. Yue [52] proposed a GDM method based on the projection measurement. Xu and Liu [53] described a GDM approach based on a projection method in uncertain fuzzy environment. Xu and Da [54] modeled an uncertain MADM method. Yue [55] suggested a GDM method based on a projection method. Yue and Jia [56] proposed a direct projection-based GDM methodology with crisp values and interval data.
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Application of Fuzzy Based VIKOR Approach for Multi-Attribute Group Decision Making (MAGDM): A Case Study in Supplier Selection

Application of Fuzzy Based VIKOR Approach for Multi-Attribute Group Decision Making (MAGDM): A Case Study in Supplier Selection

Abstract. In today’s competitive global markets, selection of a potential supplier plays an important role to cut production costs as well as material costs of the company. This leads to successful survival and sustainability in a competitive marketplace. Therefore, evaluation and selection of an appropriate supplier has become an important part of supply chain management. The nature of the supplier selection process is a complex multi-attribute group decision making (MAGDM) problem which deals with both quantitative and qualitative factors may be conflicting in nature as well as contain incomplete and uncertain information. In order to solve such a kind of MAGDM problems, the development of an effective supplier selection model is evidently desirable. In this paper, an application of the VIKOR method combined with fuzzy logic has been used to solve supplier selection problems with confliting and non-commensurable (different units) criteria, assuming that compromising is acceptable for conflict resolution. The decision maker wants a solution, which must be closest to the ideal, and the alternatives are evaluated according to all established criteria. Linguistic values are used to assess the ratings and weights for the conflicting factors. These linguistic ratings can be expressed in triangular fuzzy numbers. Then, a hierarchy MAGDM model based on fuzzy sets theory and the VIKOR method has been proposed to deal with the supplier selection problems in the supply chain system. A case study has been illustrated as an application of the proposed model.
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An Extended TOPSIS Method for Multiple Attribute Group Decision Making Based on Generalized Interval-valued Trapezoidal Fuzzy Numbers

An Extended TOPSIS Method for Multiple Attribute Group Decision Making Based on Generalized Interval-valued Trapezoidal Fuzzy Numbers

Multiple attribute decision making (MADM) is an important part of modern decision science. It has been extensively applied to various areas, such as society, economics, management, military and engineering technology. For example, the investment decision- making, project evaluation, the economic evaluation, the personnel evaluation etc. Since the object things are fuzzy, uncertainty and human thinking is ambiguous, the majority of the multi-attribute decision-making is uncertain and ambiguous, which is called the fuzzy multiple attribute decision-making (FMADM). Since Bellmanhe and Zadeh [1] initially proposed the basic model of fuzzy decision making based on the theory of fuzzy mathematics, FMADM has been receiving more and more attentions. Many achievements have been made on FMADM problems [2-5,7-21].
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A Method for Multi-attribute Group Decision Making with Triangular Intuitionistic Fuzzy Numbers and Application to Trustworthy Service Selection

A Method for Multi-attribute Group Decision Making with Triangular Intuitionistic Fuzzy Numbers and Application to Trustworthy Service Selection

Abstract. The trustworthy service selection is a typical Multi-Attribute Group Decision- Making (MAGDM) problem. The aim of this paper is to develop a novel method for MAGDM with Triangular Intuitionistic Fuzzy Numbers (TIFNs) and apply it to the trustworthy service selection problem. Firstly, we dene the mean-index, variance-index, and standard deviation of TIFN. Moreover, a new distance measure of TIFNs is proposed, and the corresponding proofs are given. Based on these concepts of mean-index and standard deviation, a ranking method for TIFNs is developed considering the risk preference of Decision Maker (DM). Further, according to the crisp relative closeness coecient matrix with respect to the normalized TIFNs decision matrix, we use entropy measure to obtain attribute weights. The DMs' weights are calculated by the similarity between the individual and the average decisions. Then, a decision procedure is described to solve the MAGDM under triangular intuitionistic fuzzy environment. Finally, a real trustworthy service selection example is analyzed to verify the practicality and eectiveness of the developed method.
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A Case Study of Multi Attribute Decision Making Problem for Solving Intuitionistic Fuzzy Soft Matrix in Medical Diagnosis

A Case Study of Multi Attribute Decision Making Problem for Solving Intuitionistic Fuzzy Soft Matrix in Medical Diagnosis

In the fuzzy set theory [15] there were no scopes to think about the hesitation in the membership degree, which arise in various real life situations. To overcome these situations Atanassov [1] introduced theory of intuitionistic fuzzy set in 1986 as a generalization of fuzzy set.Most of the problems in engineering, medical science, economics, environments etc have various uncertainties. Molodtsov[12] initiated the concept of soft set theory as a mathematical tool for dealing with uncertainties. Research works on soft set theory are progressing rapidly. Maji et al.[8] defined several operations on soft set theory. Combining soft sets with fuzzy sets and intuitionistic fuzzy sets, Feng et al.[7] and Maji et al.[9,10] defined fuzzy soft sets and intuitionistic fuzzy soft sets which are rich potentials for solving decision making problems.Matrices play an important role in the broad area of science and engineering. The classical matrix theory cannot solve the problems involving various types of uncertainties. In [14] Yang et al, initiated a matrix representation of a fuzzy soft set and applied it in certain decision making problems. The concept of fuzzy soft matrix theory was studied by Borah et al. in [2]. In [5], Chetia et al. and in [13] Rajarajeswari et al. defined intuitionistic fuzzy soft matrix.Again it is well known that the matrices are important tools to model/study different mathematical problems specially in linear algebra. Due to huge applications of imprecise data in the above mentioned areas, hence are motivated to study the different matrices containing these data. Soft set is also one of the interesting and popular subject, where different types of decision making problem can be solved. So attempt has been made to study the decision making problem by using intuitionistic fuzzy soft aggregation operator. Das and Kar [6] proposed an algorithmic approach for group decision making based on IF soft set. The authors [6] have used cardinality of IF soft set as a novel concept for assigning confident weight to the set of experts. Cagman and Enginogh[3 ,4] pioneered the concept of soft matrix to represent a soft set. Mao et al.[11] presented the concept of intuitionistic fuzzy soft matrix(IFSM) and applied it in group decision making problem.
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An Extended LINMAP Method for Multi-Attribute Group Decision Making under Interval-Valued Intuitionistic Fuzzy Environment

An Extended LINMAP Method for Multi-Attribute Group Decision Making under Interval-Valued Intuitionistic Fuzzy Environment

Deschrijver and Kerre [12] have shown that IFSs are equivalent to IVFSs (also called vague sets [3]) and both can be regarded as L-fuzzy sets in the sense of Goguen [34]. In reality, it may not be easy to identify exact values for the membership and non-membership degrees of an element to a set. In this case, a range of values may be a more appropriate measurement to accommodate the vagueness. As such, Atanassov and Gargov [5] introduce the notion of IVIFS:

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Interval-valued Trapezoidal Intuitionistic Fuzzy Generalized Aggregation Operators and Application to Multi-attribute Group Decision Making

Interval-valued Trapezoidal Intuitionistic Fuzzy Generalized Aggregation Operators and Application to Multi-attribute Group Decision Making

Abstract. An Interval-Valued Trapezoidal Intuitionistic Fuzzy Number (IVTrIFN) is a special case of an Intuitionistic Fuzzy Set (IFS), which is dened on a real number set. From a geometric viewpoint, the expectation and expectant score of an IVTrIFN are dened using the notion of a barycenter, and a new method is developed to rank IVTrIFNs. Hereby, some generalized aggregation operators of IVTrIFNs are dened, including the generalized ordered weighted averaging operator and the generalized hybrid weighted averaging operator, which are employed to solve multi-attribute group decision making problems. Using the weighted average operator of IVTrIFNs, the attribute values of alternatives are integrated into the individual comprehensive ratings, which are further aggregated into the collective one by the generalized hybrid weighted averaging operator of IVTrIFNs. The ranking orders of alternatives are then generated according to the expectation and expectant score of the collective comprehensive ratings of alternatives. A numerical example is examined to demonstrate the applicability and implementation process of the decision method proposed in this paper.
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Model for Multiple Attribute Decision Making with Normal Distribution Interval Number Based on Choquet Integral

Model for Multiple Attribute Decision Making with Normal Distribution Interval Number Based on Choquet Integral

Abstract – In this paper, we use the Choquet integral to propose the normal distribution interval number Choquet ordered averaging operator. The operator not only considers the importance of the elements, but also can reflect the correlations among the elements. It is worth pointing out that most of the existing normal distribution interval numbers averaging operators are special cases of our operator. Finally an illustrative example is given to use the operator in the range of uncertain multi-attribute decision-making. The results show that the method proposed in this paper is feasible.
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Evaluation of cloud services: A fuzzy multi-criteria group decision making method

Evaluation of cloud services: A fuzzy multi-criteria group decision making method

Evaluating the performance of the available Cloud services with respect to multiple, usually conflicting criteria in a specific situation is always challenging due to (a) the availability of multiple Cloud services; (b) the multi-dimensional nature of the decision making problem; (c) the involvement of multiple decision makers; and (d) the presence of subjectivity and imprecision involved in the decision making process [ 34 ]. To overcome these concerns, this paper presents a fuzzy multi-criteria decision making method based on the fusion of several concepts including (a) the TOPSIS method; (b) the Choquet integral operator; and (c) intuitionistic fuzzy numbers.
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