“…Li et al [ 22 ] prioritized the elective surgery patient admission in a Chinese public tertiary hospital using the hesitant fuzzy linguistic ORESTE method. Zheng et al [ 23 ] developed an extended IT2F-ORESTE method for risk analysis in FMEA. Liao et al [ 54 ] presented a new hesitant fuzzy linguistic ORESTE method for hybrid MCDM.…”
Section: Literature Reviewsmentioning
confidence: 99%
“…The lower MF and upper MF are defined as follows: Fig. 1 A geometrical interpretation of an IT2FS [ 23 , 24 ] …”
Section: Preliminariesmentioning
confidence: 99%
“…[ 23 ] Assume that , be a collection of IT2FSs. Then, the collective value of interval type-2 fuzzy power average (IT2FPA) operator is still an IT2FS, and …”
Section: Preliminariesmentioning
confidence: 99%
“…Compared with the existing MCDM methods, the ORESTE method not only can determine the utility values of alternatives but also can capture the preference interrelations, incomparability interrelations and the indifference interrelations between alternatives. Moreover, a large number of researchers have developed some extended forms of the OREST method, such as probabilistic hesitant fuzzy ORESTE method [ 21 ], hesitant fuzzy linguistic ORESTE method [ 22 ], interval type-2 fuzzy ORESTE (IT2F-ORESTE) method [ 23 ]. Although the IT2F-ORESTE method can overcome the above drawback (2), it still has some other drawbacks: Most of the distance measures (DMs) for IT2FS are generalizations of the distances applied in the crisp sets, using the membership function to take place of the characteristic function, such as the normalized Hamming DM and the normalized Euclidean DM.…”
The economic implications from the COVID-19 crisis are not like anything people have ever experienced. As predictions indicated, it is not until the year 2025 may the global economy recover to the ideal situation as it was in 2020. Regions lacked of developing category is among the mostly affected regions, because the category includes weakly and averagely potential power. For supporting the decision of economic system recovery scientifically and accurately under the stress of COVID-19, one feasible solution is to assess the regional economic restorability by taking into account a variety of indicators, such as development foundation, industrial structure, labor forces, financial support and government's ability. This is a typical multi-criteria decision-making (MCDM) problem with quantitative and qualitative criteria/indicator. To solve this problem, in this paper, an investigation is conducted to obtain 14 indicators affecting regional economic restorability, which form an indicator system. The interval type-2 fuzzy set (IT2FS) is an effective tool to express experts’ subjective preference values (PVs) in the process of decision-making. First, some formulas are developed to convert quantitative PVs to IT2FSs. Second, an improved interval type-2 fuzzy ORESTE (IT2F-ORESTE) method based on distance and likelihood are developed to assess the regional economic restorability. Third, a case study is given to illustrate the method. Then, robust ranking results are acquired by performing a sensitivity analysis. Finally, some comparative analyses with other methods are conducted to demonstrate that the developed IT2F-ORESTE method can supporting the decision of economic system recovery scientifically and accurately.
“…Li et al [ 22 ] prioritized the elective surgery patient admission in a Chinese public tertiary hospital using the hesitant fuzzy linguistic ORESTE method. Zheng et al [ 23 ] developed an extended IT2F-ORESTE method for risk analysis in FMEA. Liao et al [ 54 ] presented a new hesitant fuzzy linguistic ORESTE method for hybrid MCDM.…”
Section: Literature Reviewsmentioning
confidence: 99%
“…The lower MF and upper MF are defined as follows: Fig. 1 A geometrical interpretation of an IT2FS [ 23 , 24 ] …”
Section: Preliminariesmentioning
confidence: 99%
“…[ 23 ] Assume that , be a collection of IT2FSs. Then, the collective value of interval type-2 fuzzy power average (IT2FPA) operator is still an IT2FS, and …”
Section: Preliminariesmentioning
confidence: 99%
“…Compared with the existing MCDM methods, the ORESTE method not only can determine the utility values of alternatives but also can capture the preference interrelations, incomparability interrelations and the indifference interrelations between alternatives. Moreover, a large number of researchers have developed some extended forms of the OREST method, such as probabilistic hesitant fuzzy ORESTE method [ 21 ], hesitant fuzzy linguistic ORESTE method [ 22 ], interval type-2 fuzzy ORESTE (IT2F-ORESTE) method [ 23 ]. Although the IT2F-ORESTE method can overcome the above drawback (2), it still has some other drawbacks: Most of the distance measures (DMs) for IT2FS are generalizations of the distances applied in the crisp sets, using the membership function to take place of the characteristic function, such as the normalized Hamming DM and the normalized Euclidean DM.…”
The economic implications from the COVID-19 crisis are not like anything people have ever experienced. As predictions indicated, it is not until the year 2025 may the global economy recover to the ideal situation as it was in 2020. Regions lacked of developing category is among the mostly affected regions, because the category includes weakly and averagely potential power. For supporting the decision of economic system recovery scientifically and accurately under the stress of COVID-19, one feasible solution is to assess the regional economic restorability by taking into account a variety of indicators, such as development foundation, industrial structure, labor forces, financial support and government's ability. This is a typical multi-criteria decision-making (MCDM) problem with quantitative and qualitative criteria/indicator. To solve this problem, in this paper, an investigation is conducted to obtain 14 indicators affecting regional economic restorability, which form an indicator system. The interval type-2 fuzzy set (IT2FS) is an effective tool to express experts’ subjective preference values (PVs) in the process of decision-making. First, some formulas are developed to convert quantitative PVs to IT2FSs. Second, an improved interval type-2 fuzzy ORESTE (IT2F-ORESTE) method based on distance and likelihood are developed to assess the regional economic restorability. Third, a case study is given to illustrate the method. Then, robust ranking results are acquired by performing a sensitivity analysis. Finally, some comparative analyses with other methods are conducted to demonstrate that the developed IT2F-ORESTE method can supporting the decision of economic system recovery scientifically and accurately.
“…Wang et al., 48 adopted the ORESTE method to evaluate the urban traffic congestion under a double hierarchy hesitant fuzzy linguistic environment. Zheng et al., 49 firstly introduced the ORESTE method to rank the risk of failure modes under interval type‐2 fuzzy environment. The combination of ORESTE with various methods mentioned above has improving its practicability.…”
The failure mode and effect analysis (FMEA) model is an effective safety system analysis method to identify and analyze potential risks. Nevertheless, the traditional FMEA possesses deficiencies associating with questionable multiplication procedure, overlooking uncertainty and interaction relationships among risk factors. Besides, determining the risk priority considering the detailed interrelation among the failure modes is beyond the capability of current FMEA models, especially in the case of complex preference information. Thus, this paper proposed an extended FMEA by integrating ORESTE (organization, rangement et Synthèse de données relarionnelles, in French) method, Heronian Mean (HM) operator, and interval type‐2 fuzzy sets (IT2FSs). First, the IT2FSs are utilized to depict the complex risk preference information. Second, the HM operator, as an aggregated tool, is utilized to obtain collective evaluation information, which can fully reflect the overall correlations among individual risk assessment information. Then, an extended ORESTE method based on the likelihood of IT2FSs is utilized to obtain the priority ranking orders of failure modes. In this extended method, a new IT2FSs global preference score is constructed to determine the weak ranking. And, three new preference intensities based on the likelihood of IT2FSs are constructed to determine PIR (preference, indifference, and incomparability) structure which can show the detailed relationships between failure modes. Finally, an illustrative example of a nuclear reheat valve system is selected to verify its application and efficiency.
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