On priority weights and consistency for incomplete hesitant fuzzy preference relations

Zhang, Zhen; Kou, Xinyue; Yu, Wenyu; Guo, Chonghui

doi:10.1016/j.knosys.2017.12.010

Cited by 115 publications

(45 citation statements)

References 46 publications

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“…Furthermore, application of the hesitant fuzzy sets [57] to the multicriteria decision-making is rapidly growing owing to its capability to interpret vague information efficiently. Some studies (Zhang et al [58,59], Rodríguez et al [60], Yu et al [61]) have proposed hesitant fuzzy preference relation and methods for accounting its consistency. Incorporation of hesitant fuzzy preference relation as preference elicitation approach can be a significant extension of the proposed work.…”

Section: Resultsmentioning

confidence: 99%

Assessment of Healthcare Waste Treatment Alternatives Using an Integrated Decision Support Framework

Hinduja¹,

Pandey²

2019

IJCIS

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Health-care waste management HCW treatment technology selection Intuitionistic fuzzy sets DEMATEL ANP AHP ID:p0065 Healthcare waste (HCW) management has become a major environmental and public-health concern especially in developing countries, and therefore, it has been receiving increasing attention from both industrial practitioners and researcher in recent years. Selection of the optimal treatment technology for HCW is regarded as an intricate multi-criteria decision-making problem involving conflicting and intertwined qualitative as well as quantitative evaluative criteria. To address this decision problem, we develop an integrated decision support framework based on decision-making trial and evaluation laboratory (DEMATEL), intuitionistic fuzzy ANP, and intuitionistic fuzzy AHP. The DEMATEL method determines the influences of main factors and criteria and produces a network relationship map while the ANP method calculates the degree of interrelationship among evaluative criteria and obtains their relative weights. The AHP method assesses the HCW treatment alternatives over evaluative criteria. The experts' opinions are collected in form of intuitionistic fuzzy preference relations as they are effective in capturing uncertainty and hesitancy involved in decision-makers' judgment. We also develop a priority method to derive nonfuzzy weights from the intuitionistic fuzzy preference relations. To validate the feasibility of the proposed approach, a case study is carried out on the selection of optimum HCW treatment technology for Chhattisgarh, India. The analyses of the results indicate that the proposed integrated multi-criteria decision making (MCDM) framework effectively handles the issue of HCW treatment technology selection in uncertain environments. A B S T R A C T

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Section: Resultsmentioning

confidence: 99%

Assessment of Healthcare Waste Treatment Alternatives Using an Integrated Decision Support Framework

Hinduja¹,

Pandey²

2019

IJCIS

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“…Further studies will extend the proposed method to the interval‐valued Pythagorean fuzzy set, soft set, q ‐rung orthopair fuzzy sets, and other uncertain and fuzzy environment …”

Section: Discussionmentioning

confidence: 99%

Pythagorean fuzzy interaction power Bonferroni mean aggregation operators in multiple attribute decision making

2019

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The power Bonferroni mean (PBM) operator can relieve the influence of unreasonable aggregation values and also capture the interrelationship among the input arguments, which is an important generalization of power average operator and Bonferroni mean operator, and Pythagorean fuzzy set is an effective mathematical method to handle imprecise and uncertain information. In this paper, we extend PBM operator to integrate Pythagorean fuzzy numbers (PFNs) based on the interaction operational laws of PFNs, and propose Pythagorean fuzzy interaction PBM operator and weighted Pythagorean fuzzy interaction PBM operator. These new Pythagorean fuzzy interaction PBM operators can capture the interactions between the membership and nonmembership function of PFNs and retain the main merits of the PBM operator. Then, we analyze some desirable properties and particular cases of the presented operators. Further, a new multiple attribute decision making method based on the proposed method has been presented. Finally, a numerical example concerning the evaluation of online payment service providers is provided to illustrate the validity and merits of the new method by comparing it with the existing methods. K E Y W O R D S interaction operational laws, multiple attribute decision making, PBM operator, PFIPBM operator, Pythagorean fuzzy set 1 | INTRODUCTIONMultiple attribute decision making (MADM) is an important research topic in decision science, which can be described as to select the best alternative from a set of feasible alternatives according to several attributes. Due to the increasing complexity and uncertainty in modern decision making activities, some influencing factors result in decision makers to often have difficulty in assigning crisp values or fuzzy values 1 described only by membership degree for each alternative with respect to each attribute. More recently, as an effective generalization of intuitionistic fuzzy sets (IFSs), 2 the concept of Pythagorean fuzzy sets (PFSs) was developed by Yager. 3,4 The PFSs expressed fuzzy information by using a membership degree and a nonmembership degree, and satisfied the condition that the square sum of its membership degree and nonmembership degree is less than or equal to 1. Obviously, PFSs are more powerful than fuzzy sets (FSs) and IFSs to describe some complex fuzzy information. [5][6][7][8] In recent years, as a novel evaluation, PFSs not only have been extensively studied in theory, but also have achieved a great number of achievements in applications, such as information measures, [9][10][11][12][13][14][15] basic operations, [16][17][18]11,13,[19][20][21][22][23][24][25][26][27][28] Especially the information aggregation operators, as an important role in MADM, have been proposed to aggregate Pythagorean fuzzy numbers (PFNs). For instance, Ma and Xu 29 developed the Pythagorean fuzzy weighted averaging (PFWA) operator and Pythagorean fuzzy weighted geometric (PFWG) operator, and also proposed symmetric Pythagorean fuzzy weighted averaging (SPFWA) operator and symmetric ...

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“…Atanassov and Gargov's [3] concept of interval-valued intuitionistic fuzzy sets (IVFS), which are characterized by a membership and a nonmembership functions, permits to process hesitancy information in decision making problems [19], [20], [22], [33], [34], [50], [56].…”

Section: Preliminariesmentioning

confidence: 99%

An Attitudinal Trust Recommendation Mechanism to Balance Consensus and Harmony in Group Decision Making

Chiclana

et al. 2019

IEEE Trans. Fuzzy Syst.

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This article puts forward a trust based framework for building a recommendation mechanism for consensus in group decision making with interval-valued intuitionistic fuzzy information. To do that, it first presents an attitudinal trust model where experts assign trust weights to others considering the concept of attitude of the group. This approach allows for the implementation of the group attitude in a continuous scale ranging from a pessimistic attitude to an indifferent attitude. Thus, it can express the continuous trust status, and consequently it generalizes the traditional simplified trust model: 'trusting' and 'distrusting'. In particular, three typical policies are defined as: 'extreme trust policy', 'bounded trust policy' and 'indifferent trust policy'. Secondly, the attitudinal trust induced recommendation mechanism is established by a reasonable rule: the closer the experts, the higher their trust degree. This can guarantee that the consensus level of the inconsistent expert is increased after adopting the recommended advices. In addition to group consensus, experts envisage to keep their original opinions as much as possible. A harmony degree (HD) is defined to determine the extent of the difference between an original opinion and the corresponding revised opinion after adopting the recommended advices. Combining the HD index and the consensus index, a sensitivity analysis with attitudinal parameter is proposed to verify the rationality of the proposed attitudinal trust recommendation mechanism. In practice this will facilitate the inconsistent experts to achieve a balance between consensus degree and harmony degree by selecting an appropriate attitudinal parameter.

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On priority weights and consistency for incomplete hesitant fuzzy preference relations

Cited by 115 publications

References 46 publications

Assessment of Healthcare Waste Treatment Alternatives Using an Integrated Decision Support Framework

Assessment of Healthcare Waste Treatment Alternatives Using an Integrated Decision Support Framework

Pythagorean fuzzy interaction power Bonferroni mean aggregation operators in multiple attribute decision making

An Attitudinal Trust Recommendation Mechanism to Balance Consensus and Harmony in Group Decision Making

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