Fuzzified Choquet Integral and its Applications in MADM: A Review and A New Method

Keikha, Abazar; Nehi, Hassan Mishmast

doi:10.1007/s40815-015-0037-0

Cited by 10 publications

(5 citation statements)

References 37 publications

(54 reference statements)

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“…Such a combination might increase the perceived validity of the results. Finally, using DEMATEL along with a multi-attribute decision-making model fuzzy AHP, fuzzy analytical network process or fuzzy Choquet integral can also be utilized to deal with various relationships between criteria and decide on their relative weights [55][56][57]. …”

Section: Discussionmentioning

confidence: 99%

Critical Success Factors for the Iron and Steel Industry in Turkey: A Fuzzy DEMATEL Approach

Kabak

Ülengin

Çekyay

et al. 2015

Int. J. Fuzzy Syst.

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Section: Discussionmentioning

confidence: 99%

Critical Success Factors for the Iron and Steel Industry in Turkey: A Fuzzy DEMATEL Approach

Kabak

Ülengin

Çekyay

et al. 2015

Int. J. Fuzzy Syst.

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“…μ has an additivity property. When fuzzy measure μ(A) is related to cardinal number of set A, that is, the sets with the same number of elements have the same measure, then w i � μ(A i ) − μ(A i− 1 ), where A i is the subset of X with Card(A i ) � i, and then CI converts to ordered weighted averaging (OWA) [49].…”

Section: Enables Us To Compute Qualition Weights μ(E ∪ F) Of E F ⊂ P(x) Based On the Given Weights μ(E) μ(F) As μ(E ∪ F) � μ(E)+ μ(F) + λmentioning

confidence: 99%

Multiple-Attribute Decision-Making Problem Using TOPSIS and Choquet Integral with Hesitant Fuzzy Number Information

Garg

Keikha

Nehi

2020

Mathematical Problems in Engineering

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The paper aims are to present a method to solve the multiple-attribute decision-making (MADM) problems under the hesitant fuzzy set environment. In MADM problems, the information collection, aggregation, and the measure phases are crucial to direct the problem. However, to handle the uncertainties in the collection data, a hesitant fuzzy number is one of the most prominent ways to express uncertain and vague information in terms of different discrete numbers rather than a single crisp number. Additionally, to aggregate and to rank the collective numbers, a TOPSIS (“Technique for Order of Preference by Similarity to Ideal Solution”) and the Choquet integral (CI) are the useful tools. Keeping all these features, in the present paper, we combine the TOPSIS and CI methods for hesitant fuzzy information and hence present a method named as TOPSIS-CI to address the MADM problems. The presented method has been described with a numerical example. Finally, the validity of the stated method as well as a comparative analysis with the existing methods is addressed in detail.

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“…57 Applying aggregation procedure is typically easy when the information are represented as crisp values. 57 Applying aggregation procedure is typically easy when the information are represented as crisp values.…”

Section: Information Fuzzification According To Choquet Integral Comentioning

confidence: 99%

“…It is required to rank the obtained information as an initial step in the aggregation procedure using Choquet integral. 57 Applying aggregation procedure is typically easy when the information are represented as crisp values. However, in current study, the IFN-based TFNs are applied to properly illustrate the uncertainty of importance weight of criteria due to alternative ranking.…”

Section: Information Fuzzification According To Choquet Integral Comentioning

confidence: 99%

A perceptual computing–based method to prioritize intervention actions in the probabilistic risk assessment techniques

Yazdi

2019

Quality & Reliability Eng

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Probabilistic risk assessment techniques as the systematic tools have been widely used on the different type of industrial sectors to reduce the estimated risk to an acceptable level. The fact is to design inherently safety; hazards have to be eliminated and reduced in risk as much as possible with the consideration of several interventions. In this regard, multicriteria decision making (MCDM) science is commonly integrated with the probabilistic risk assessment techniques to improve the safety performance of a system. Thus, it has been widely used to assist decision makers in controlling the identified process hazards in a different type of engineering applications. However, by increasing the complexity of industrial sectors as well as human being judgments, typical MCDM methods cannot highly guarantee their output results. According to this point, proposing MCDM methods based on mathematical programming have been interested in scholars due to high reliability and feasibility of the results. In this paper, we extended integration of MULTIMOORA approach with the Choquet integral under subjectivity circumstances to prioritize corrective actions in a typical probabilistic risk assessment technique. To illustrate the efficiency and feasibility of the proposed method, it has been applied in a real case study.

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Fuzzified Choquet Integral and its Applications in MADM: A Review and A New Method

Cited by 10 publications

References 37 publications

Critical Success Factors for the Iron and Steel Industry in Turkey: A Fuzzy DEMATEL Approach

Critical Success Factors for the Iron and Steel Industry in Turkey: A Fuzzy DEMATEL Approach

Multiple-Attribute Decision-Making Problem Using TOPSIS and Choquet Integral with Hesitant Fuzzy Number Information

A perceptual computing–based method to prioritize intervention actions in the probabilistic risk assessment techniques

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