An integrated decision-making COPRAS approach to probabilistic hesitant fuzzy set information

Krishankumar, Raghunathan; Garg, Harish; Arun, Karthik; Saha, Abhijit; Ravichandran, K. S.; Kar, Samarjit

doi:10.1007/s40747-021-00387-w

Cited by 45 publications

(21 citation statements)

References 72 publications

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“…Rani et al evaluated sustainable suppliers by adopting a SWARA-COPRAS combined method with hesitant fuzzy data. Krishankumar et al [42] proposed an integrated framework under the probabilistic version of hesitant fuzzy data with Hamy mean and COPRAS for evaluating cloud vendors for suitable data processing/migration. Lu et al [43] extended the COPRAS ranking approach to picture fuzzy setting and ranked suppliers who actively take up green strategies for gaining a sustainable supply chain.…”

Section: Review Of the Copras Methodsmentioning

confidence: 99%

An Integrated Variance-COPRAS Approach with Nonlinear Fuzzy Data for Ranking Barriers Affecting Sustainable Operations

et al. 2022

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Sustainability is becoming the core theme of every organization to protect the planet from the drastic effects of climate change. Many organizations have drastically changed their practices to encourage green habits for sustainable operations. Practitioners have discussed the difficulties in the literature owing to the adoption of sustainable aspects of environmental, economic and social paradigms in the organization. One can identify diverse barriers, and ranking them would help policy-makers plan their actions. Motivated by this claim, a new integrated approach with nonlinear fuzzy data is put forward in this paper. The nonlinear mapping of fuzzy data provides a better representation of uncertainty, which inspired the authors to use nonlinear data. Further, the attitudinal variance method is proposed for a weight assessment of the criteria that can handle hesitation effectively and consider each agent’s reliability. The Boran principle in the nonlinear context is used to calculate the reliability values. Complex proportional assessment (COPRAS), a popular ranking algorithm, is extended to nonlinear data for rationally ranking barriers that affect sustainable operations. An illustrative example exemplifies the usability of the approach, and a comparison/sensitivity analysis reveals the pros and cons of the framework.

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Section: Review Of the Copras Methodsmentioning

confidence: 99%

An Integrated Variance-COPRAS Approach with Nonlinear Fuzzy Data for Ranking Barriers Affecting Sustainable Operations

et al. 2022

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“…Alipour et al [ 6 ] evaluated fuel cell and hydrogen component suppliers using the Pythagorean fuzzy Entropy–SWARA–COPRAS method. To handle the cloud vendor assessment problem, Krishankumar et al [44] extended the COPRAS technique under a probabilistic hesitant fuzzy set environment. For the first time, the present paper captures the combined advantages of FFS, Archimedean copula MSM and COPRAS methods, and introduces an innovative FFI-based MCDM framework to evaluate and rank the RES alternatives.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Afterwards, four key aspects were recognized, such as the environmental, technical, economic, and social aspects, and the 15 indicators for selecting the RESs assessment. In addition, open interviews and literature reviews helped us recognize RESs [ 2 , 28 , 44 , 45 , 66 ]. Meanwhile, six RESs were chosen as the most suitable option in Gujarat, India.…”

Section: Case Study: Renewable Energy Source (Res) Selectionmentioning

confidence: 99%

Fermatean fuzzy copula aggregation operators and similarity measures-based complex proportional assessment approach for renewable energy source selection

Mishra¹

2022

Complex Intell. Syst.

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Selecting the optimal renewable energy source (RES) is a complex multi-criteria decision-making (MCDM) problem due to the association of diverse conflicting criteria with uncertain information. The utilization of Fermatean fuzzy numbers is successfully treated with the qualitative data and uncertain information that often occur in realistic MCDM problems. In this paper, an extended complex proportional assessment (COPRAS) approach is developed to treat the decision-making problems in a Fermatean fuzzy set (FFS) context. First, to aggregate the Fermatean fuzzy information, a new Fermatean fuzzy Archimedean copula-based Maclaurin symmetric mean operator is introduced with its desirable characteristics. This proposed operator not only considers the interrelationships between multiple numbers of criteria, but also associates more than one marginal distribution, thus avoiding information loss in the process of aggregation. Second, new similarity measures are developed to quantify the degree of similarity between Fermatean fuzzy perspectives more effectively and are further utilized to compute the weights of the criteria. Third, an integrated Fermatean fuzzy-COPRAS approach using the Archimedean copula-based Maclaurin symmetric mean operator and similarity measure has been developed to assess and rank the alternatives under the FFS perspective. Furthermore, a case study of RES selection is presented to validate the feasibility and practicality of the developed model. Comparative and sensitivity analyses are used to check the reliability and strength of the proposed method.

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“…In a study, Rani et al ( 2020a , b , c , d ) designed a decision support system by unifying the COPRAS technique on PFS and applied it to calculate the pharmacological therapies for type-2 diabetes patients. Some scholars have currently proposed the COPRAS method in various areas of MCDM (see Lu et al 2021 ; Krishankumar et al 2021 ; Jain and Chand 2021 ). This method effectively establishes the interrelationship between criteria and allows decision experts to capture the ambiguity involved in decisions of many inconsistent criteria.…”

Section: Introductionmentioning

confidence: 99%

Pythagorean fuzzy entropy measure-based complex proportional assessment technique for solving multi-criteria healthcare waste treatment problem

Chaurasiya

Jain

2022

Granul. Comput.

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With the increasing risk to human health and environmental issues, the selection of appropriate management and treatment of healthcare waste has become a major problem, especially in developing countries. There are various alternatives to dispose of health care waste. The important is to assess the best alternative among them. The assessment of each alternative should be done based on public health, psychological, economic, environmental, technological, and operational aspect. The selection of the best health care waste treatment (HCWT) alternative is a complicated, multi-criteria decision-making (MCDM) problem involving numerous disparate qualitative and quantitative features. Hence, in this research article, the MCDM method is presented for estimating and choosing the best alternative of HCWT by COPRAS technique in a Pythagorean fuzzy set (PFS). Here, in this paper, first of all, a new entropy measure on PFSs is proposed and its validity is studied. Thereafter, the MCDM technique Complex Proportional Assessment (COPRAS) is discussed in which the criteria weights are assessed by the proposed entropy measure and score function to enhance an efficacy and efficiency of the proposed technique. Furthermore, the above-defined technique is employed to resolve the real-life problem to obtain the best treatment alternative to disposal of the health care waste. Finally, sensitivity analysis is presented to rationale the proposed viewpoint for prioritizing HCWT alternatives.

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An integrated decision-making COPRAS approach to probabilistic hesitant fuzzy set information

Cited by 45 publications

References 72 publications

An Integrated Variance-COPRAS Approach with Nonlinear Fuzzy Data for Ranking Barriers Affecting Sustainable Operations

An Integrated Variance-COPRAS Approach with Nonlinear Fuzzy Data for Ranking Barriers Affecting Sustainable Operations

Fermatean fuzzy copula aggregation operators and similarity measures-based complex proportional assessment approach for renewable energy source selection

Pythagorean fuzzy entropy measure-based complex proportional assessment technique for solving multi-criteria healthcare waste treatment problem

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