Fuzzy risk analysis in familial breast cancer using a similarity measure of interval-valued fuzzy numbers

Sen, Sanjib; Patra, Kartik; Mondal, Shyamal Kumar

doi:10.1016/j.psra.2016.09.014

Cited by 10 publications

(6 citation statements)

References 29 publications

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“…Similar to generalized fuzzy numbers, these numbers can find a trapezoidal shape. Each IVFNs include an upper bound and lower bound, whose membership functions are defined as follows (Sen et al , 2016):andwhere A~L and A~U denote, respectively, the membership functions of lower bound and upper bound, while λ and □ imply the most likely value of membership function related to upper bound and lower bound, respectively. Figure 2 presents the normal TFNs together with IVFNs and the generalized IVFNs.…”

Section: Methodsmentioning

confidence: 99%

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What drives clients to purchase green building?: The cybernetic fuzzy analytic hierarchy process approach

Durdyev

Mohandes

Mahdiyar

et al. 2021

ECAM

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PurposeHomebuyers' motivation to purchase the green building (GB) is vital for the widespread adoption of greener practices and for tackling the adverse impact of the built environment. In this regard, through the lenses of qualified and competent GB developers (GBD) and real estate agents (REAs) using judgment sampling technique, this paper aims to evaluate the factors stimulating the purchase intention of Malaysian homebuyers.Design/methodology/approachThe study uses the interval-valued fuzzy Delphi method (IVFDM) to identify influential factors, while the cybernetic fuzzy analytic hierarchy process (CFAHP) for prioritizing the identified factors according to their significance in nationwide promotion of GBs is utilized.FindingsThe findings, which are consistent with those reported in the literature and validated by the focus group, uncover the most prominent factors that drive the purchase intention of GB homebuyers. The results indicate that the homebuyers are familiar with the benefits of GB; however, the benefits are not sufficient to overcome the perceived “high cost” of green practices. Hence, homebuyers still seek financial incentives, which is a clear indication that financial issues are the most motivating factor.Practical implicationsIncrease in informative promotion of GB is necessary, particularly among prospective homebuyers in developing countries where GB adoption is in its infancy. Frontline players (those who are responsible for wider promotion) can utilize the factors that this study found to be important to identify appropriate steps to increase prospective homebuyers' motivation for purchasing greener dwellings.Originality/valueThe contribution of this paper is threefold. First, review and identification of the most cited determinants that have been reported in the context of the subject. Second, this paper presents the most significant determinants in a developing country setting through the lenses of Malaysian experts in the area. Third, improvement measures could be implemented in Malaysia and other developing countries to promote the GB paradigm with an aim to make the building environment more sustainable.

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Section: Methodsmentioning

confidence: 99%

Section: Fuzzy Set Theorymentioning

confidence: 99%

What drives clients to purchase green building?: The cybernetic fuzzy analytic hierarchy process approach

Durdyev

Mohandes

Mahdiyar

et al. 2021

ECAM

View full text Add to dashboard Cite

show abstract

“…numerical value form and interval value form) allow the non-homogeneous risk analyst's preferences elicitation that are completely known, completely unknown, partially known and partially unknown, to be consistently represented. Therefore, a novel fuzzy risk analysis method that is developed from the grey number perspective and structure of fuzzy risk analysis (Jana & Ghosh, 2018;Du & Hu, 2017;Sen et al, 2016) is proposed for the first time here. The proposed method first resolves the uncertain interactions between homogeneous and non-homogeneous natures of risk analyst's preferences elicitation by using a novel consensus reaching approach that transforms grey number forms into grey parametric fuzzy numbers.…”

Section: Methods Formulationmentioning

confidence: 99%

Fuzzy risk analysis under influence of non-homogeneous preferences elicitation in fiber industry

et al. 2019

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Fuzzy risk analysis plays an important role in mitigating the levels of harm of a risk. In real world scenarios, it is a big challenge for risk analysts to make a proper and comprehensive decision when coping with risks that are incomplete, vague and fuzzy. Many established fuzzy risk analysis approaches do not have the flexibility to deal with knowledge in the form of preferences elicitation which lead to incorrect risk decision. The inefficiency is reflected when they consider only risk analyst preferences elicitation that is partially known. Nonetheless, the preferences elicited by the risk analyst are often nonhomogeneous in nature such that they can be completely known, completely unknown, partially known and partially unknown. In this case, established fuzzy risk analysis methods are considered as inefficient in handling risk, hence an appropriate fuzzy risk analysis method that can deal with the non-homogeneous nature of risk analyst's preferences elicitation is worth developing. Therefore, this paper proposes a novel fuzzy risk analysis method that is capable to deal with the non-homogeneous risk analyst's preferences elicitation based on grey numbers. The proposed method aims at resolving the uncertain interactions between homogeneous and non-homogeneous natures of risk analyst's preferences elicitation by using a novel consensus reaching approach that involves transformation of grey numbers into grey parametric fuzzy numbers. Later on, a novel fuzzy risk assessment score approach is presented to correctly evaluate and distinguish the levels of harm of the risks faced, such that these evaluations are consistent with preferences elicitation of the risk analyst. A real world risk analysis problem in fiber industry is then carried out to demonstrate the novelty, validity and feasibility of the proposed method.

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“…When analyzing the uncertain factors of large and complex equipment and systems, it is difcult to obtain the real distribution of uncertain variables because of the large amount of data, but it is relatively convenient to obtain the interval bounds of uncertain variables, and the amount of data required is relatively small [5]. Many researchers are looking for efective methods to reduce the computational burden and improve computational accuracy [6][7][8], while the interval number method has been proposed in the feld of mathematics. Interval design optimization is an uncertainty optimization method that does not need to calculate the probability distribution function of uncertain design variables, which uses interval range to characterize the uncertainty range of variables.…”

Section: Introductionmentioning

confidence: 99%

A Reliability-Based Multidisciplinary Design Optimization Strategy considering Interval Uncertainty Based on the Point-Infilled Kriging Model

Yuan

Zhang

Sun

et al. 2022

Mathematical Problems in Engineering

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Generally, traditional uncertainty design optimization (UDO) methods are based on probability density distribution function or fuzzy membership function. In this situation, a large amount of uncertain information is necessary to construct the UDO model accurately. While, the interval UDO methods require less design information. Only the upper and lower bounds of interval uncertainty are utilized to construct the optimization model. In this study, to enhance the efficiency and accuracy of UBDO considering interval uncertainty, a reliability-based multidisciplinary design optimization (RBMDO) strategy using the point-infilled Kriging model is proposed. In the given method, a double-nested RBMDO model considering interval uncertainty is established. The collaborative optimization is utilized to deal with coupling relationships among complex systems. Then, the point-infilled Kriging response surface strategy is introduced to approximate the RBMDO model. The procedure of the interval multidisciplinary collaborative optimization method based on the Kriging model is discussed. Two examples are given to illustrate the application of the proposed method.

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Fuzzy risk analysis in familial breast cancer using a similarity measure of interval-valued fuzzy numbers

Cited by 10 publications

References 29 publications

What drives clients to purchase green building?: The cybernetic fuzzy analytic hierarchy process approach

What drives clients to purchase green building?: The cybernetic fuzzy analytic hierarchy process approach

Fuzzy risk analysis under influence of non-homogeneous preferences elicitation in fiber industry

A Reliability-Based Multidisciplinary Design Optimization Strategy considering Interval Uncertainty Based on the Point-Infilled Kriging Model

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