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A new decision model with integrated approach for healthcare waste treatment technology selection with generalized orthopair fuzzy information.

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Title: A new decision model with integrated approach for healthcare waste treatment technology selection with generalized orthopair fuzzy information.
Authors: Krishankumar, R.1 (AUTHOR) r_krishankumar@cb.amrita.edu, Raj Mishra, Arunodaya2 (AUTHOR) arunodaya87@outlook.com, Rani, Pratibha3 (AUTHOR) pratibha138@gmail.com, Zavadskas, Edmundas Kazimieras1,4 (AUTHOR) edmundas.zavadskas@vilniustech.lt, Ravichandran, K.S.3,5 (AUTHOR) ravichandran20962@gmail.com, Kar, Samarjit6 (AUTHOR) samarjit.kar@maths.nitdgp.ac.in
Source: Information Sciences. Sep2022, Vol. 610, p1010-1028. 19p.
Subjects: Waste treatment, Missing data (Statistics), Multiple criteria decision making, Group decision making, Fuzzy sets, Medical care, Decision making
Geographic Terms: Tamil Nadu (India)
Abstract: [Display omitted] • Generalized preference information is used to reduce subjective randomness. • Imputation of missing values occurs systematically under different scenarios. • Weights of experts and criteria are systematically determined. • Waste treatment options are ranked rationally. • Practicality is validated by using a case study from Tamil Nadu. A generalized orthopair fuzzy set can express uncertain information easier than most other processes, which gives us more space for decision-making. In recent times, the selection of healthcare waste treatment (HCWT) technology can be considered as multi-criteria decision-making (MCDM) problem due to the involvement of multiple conflicting criteria. To reduce the unhealthy impact on the ecosystem and promote sustainable disposal, researchers have investigated MCDM problems to select apt HCWTs. In a bid to the minimize negative environmental impact of healthcare waste, researchers adopted MCDM, and faced challenges such as: (i) handling uncertainty/subjective randomness; (ii) imputation of missing values; and (iii) consideration to experts' attitude and interdependencies during MCDM process. To remedy these, this paper has attempted to establish a novel decision model with generalized orthopair fuzzy information (GOFI). Initially, missing values are systematically imputed. Experts' preferences were fused to obtain an aggregated matrix by considering the interdependencies among experts. Also, criteria weights were computed utilizing attitude-based entropy measures, and HCWTs were ranked using the GOFI-evaluation based on distance from average solution (GOFI-EDAS) approach. Lastly, the methodology's superiority was validated using an illustrative example, followed by a comparison with extant models. The results confirm that the developed framework is more efficient than and consistent with earlier methods under uncertainty. [ABSTRACT FROM AUTHOR]
Copyright of Information Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 158863506
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  Data: A new decision model with integrated approach for healthcare waste treatment technology selection with generalized orthopair fuzzy information.
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  Data: <searchLink fieldCode="AR" term="%22Krishankumar%2C+R%2E%22">Krishankumar, R.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> r_krishankumar@cb.amrita.edu</i><br /><searchLink fieldCode="AR" term="%22Raj+Mishra%2C+Arunodaya%22">Raj Mishra, Arunodaya</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> arunodaya87@outlook.com</i><br /><searchLink fieldCode="AR" term="%22Rani%2C+Pratibha%22">Rani, Pratibha</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> pratibha138@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Zavadskas%2C+Edmundas+Kazimieras%22">Zavadskas, Edmundas Kazimieras</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<i> edmundas.zavadskas@vilniustech.lt</i><br /><searchLink fieldCode="AR" term="%22Ravichandran%2C+K%2ES%2E%22">Ravichandran, K.S.</searchLink><relatesTo>3,5</relatesTo> (AUTHOR)<i> ravichandran20962@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Kar%2C+Samarjit%22">Kar, Samarjit</searchLink><relatesTo>6</relatesTo> (AUTHOR)<i> samarjit.kar@maths.nitdgp.ac.in</i>
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  Data: <searchLink fieldCode="JN" term="%22Information+Sciences%22">Information Sciences</searchLink>. Sep2022, Vol. 610, p1010-1028. 19p.
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  Data: <searchLink fieldCode="DE" term="%22Waste+treatment%22">Waste treatment</searchLink><br /><searchLink fieldCode="DE" term="%22Missing+data+%28Statistics%29%22">Missing data (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+criteria+decision+making%22">Multiple criteria decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Group+decision+making%22">Group decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+sets%22">Fuzzy sets</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+care%22">Medical care</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Tamil+Nadu+%28India%29%22">Tamil Nadu (India)</searchLink>
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  Label: Abstract
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  Data: [Display omitted] • Generalized preference information is used to reduce subjective randomness. • Imputation of missing values occurs systematically under different scenarios. • Weights of experts and criteria are systematically determined. • Waste treatment options are ranked rationally. • Practicality is validated by using a case study from Tamil Nadu. A generalized orthopair fuzzy set can express uncertain information easier than most other processes, which gives us more space for decision-making. In recent times, the selection of healthcare waste treatment (HCWT) technology can be considered as multi-criteria decision-making (MCDM) problem due to the involvement of multiple conflicting criteria. To reduce the unhealthy impact on the ecosystem and promote sustainable disposal, researchers have investigated MCDM problems to select apt HCWTs. In a bid to the minimize negative environmental impact of healthcare waste, researchers adopted MCDM, and faced challenges such as: (i) handling uncertainty/subjective randomness; (ii) imputation of missing values; and (iii) consideration to experts' attitude and interdependencies during MCDM process. To remedy these, this paper has attempted to establish a novel decision model with generalized orthopair fuzzy information (GOFI). Initially, missing values are systematically imputed. Experts' preferences were fused to obtain an aggregated matrix by considering the interdependencies among experts. Also, criteria weights were computed utilizing attitude-based entropy measures, and HCWTs were ranked using the GOFI-evaluation based on distance from average solution (GOFI-EDAS) approach. Lastly, the methodology's superiority was validated using an illustrative example, followed by a comparison with extant models. The results confirm that the developed framework is more efficient than and consistent with earlier methods under uncertainty. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Information Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.ins.2022.08.022
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 19
        StartPage: 1010
    Subjects:
      – SubjectFull: Waste treatment
        Type: general
      – SubjectFull: Missing data (Statistics)
        Type: general
      – SubjectFull: Multiple criteria decision making
        Type: general
      – SubjectFull: Group decision making
        Type: general
      – SubjectFull: Fuzzy sets
        Type: general
      – SubjectFull: Medical care
        Type: general
      – SubjectFull: Decision making
        Type: general
      – SubjectFull: Tamil Nadu (India)
        Type: general
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      – TitleFull: A new decision model with integrated approach for healthcare waste treatment technology selection with generalized orthopair fuzzy information.
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            NameFull: Krishankumar, R.
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            NameFull: Raj Mishra, Arunodaya
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            NameFull: Rani, Pratibha
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            NameFull: Zavadskas, Edmundas Kazimieras
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            NameFull: Ravichandran, K.S.
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            NameFull: Kar, Samarjit
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            – D: 15
              M: 09
              Text: Sep2022
              Type: published
              Y: 2022
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              Value: 610
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