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Low-carbon tourism strategy evaluation and selection using interval-valued intuitionistic fuzzy additive ratio assessment approach based on similarity measures

Recently, the assessment and selection of most suitable low-carbon tourism strategy has gained an extensive consideration from sustainable perspectives. Owing to participation of multiple qualitative and quantitative attributes, the low-carbon tourism strategy (LCTS) selection process can be conside...

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Autores principales: Mishra, Arunodaya Raj, Chandel, Ayushi, Saeidi, Parvaneh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8413083/
https://www.ncbi.nlm.nih.gov/pubmed/34493927
http://dx.doi.org/10.1007/s10668-021-01746-w
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author Mishra, Arunodaya Raj
Chandel, Ayushi
Saeidi, Parvaneh
author_facet Mishra, Arunodaya Raj
Chandel, Ayushi
Saeidi, Parvaneh
author_sort Mishra, Arunodaya Raj
collection PubMed
description Recently, the assessment and selection of most suitable low-carbon tourism strategy has gained an extensive consideration from sustainable perspectives. Owing to participation of multiple qualitative and quantitative attributes, the low-carbon tourism strategy (LCTS) selection process can be considered as multi-criteria decision-making (MCDM) problem. As uncertainty is usually occurred in LCTSs evaluation, the theory of interval-valued intuitionistic fuzzy sets (IVIFSs) has been established as more flexible and efficient tool to model the uncertain decision-making problems. The idea of the present study is to develop an extended method using additive ratio assessment (ARAS) framework and similarity measures in a way to find an effective solution to the decision-making problems using IVIFSs. The bases of the proposed method are the IVIFSs operators, some modifications in the traditional ARAS framework and a calculation procedure of the weights of the criteria. To calculate criterion weight, new similarity measures for IVIFSs are developed aiming at the achievement of more realistic weights. Also, a comparison is demonstrated to the currently used similarity measures in order to show the efficiency of the developed approach. To confirm that the developed IVIF-ARAS approach can be successfully employed to practical decision-making problems, a case study of LCTS selection problem is considered. The final results from the developed approach and the extant models are compared for the validation of the proposed approach in this study.
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spelling pubmed-84130832021-09-03 Low-carbon tourism strategy evaluation and selection using interval-valued intuitionistic fuzzy additive ratio assessment approach based on similarity measures Mishra, Arunodaya Raj Chandel, Ayushi Saeidi, Parvaneh Environ Dev Sustain Article Recently, the assessment and selection of most suitable low-carbon tourism strategy has gained an extensive consideration from sustainable perspectives. Owing to participation of multiple qualitative and quantitative attributes, the low-carbon tourism strategy (LCTS) selection process can be considered as multi-criteria decision-making (MCDM) problem. As uncertainty is usually occurred in LCTSs evaluation, the theory of interval-valued intuitionistic fuzzy sets (IVIFSs) has been established as more flexible and efficient tool to model the uncertain decision-making problems. The idea of the present study is to develop an extended method using additive ratio assessment (ARAS) framework and similarity measures in a way to find an effective solution to the decision-making problems using IVIFSs. The bases of the proposed method are the IVIFSs operators, some modifications in the traditional ARAS framework and a calculation procedure of the weights of the criteria. To calculate criterion weight, new similarity measures for IVIFSs are developed aiming at the achievement of more realistic weights. Also, a comparison is demonstrated to the currently used similarity measures in order to show the efficiency of the developed approach. To confirm that the developed IVIF-ARAS approach can be successfully employed to practical decision-making problems, a case study of LCTS selection problem is considered. The final results from the developed approach and the extant models are compared for the validation of the proposed approach in this study. Springer Netherlands 2021-09-03 2022 /pmc/articles/PMC8413083/ /pubmed/34493927 http://dx.doi.org/10.1007/s10668-021-01746-w Text en © The Author(s), under exclusive licence to Springer Nature B.V. 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Mishra, Arunodaya Raj
Chandel, Ayushi
Saeidi, Parvaneh
Low-carbon tourism strategy evaluation and selection using interval-valued intuitionistic fuzzy additive ratio assessment approach based on similarity measures
title Low-carbon tourism strategy evaluation and selection using interval-valued intuitionistic fuzzy additive ratio assessment approach based on similarity measures
title_full Low-carbon tourism strategy evaluation and selection using interval-valued intuitionistic fuzzy additive ratio assessment approach based on similarity measures
title_fullStr Low-carbon tourism strategy evaluation and selection using interval-valued intuitionistic fuzzy additive ratio assessment approach based on similarity measures
title_full_unstemmed Low-carbon tourism strategy evaluation and selection using interval-valued intuitionistic fuzzy additive ratio assessment approach based on similarity measures
title_short Low-carbon tourism strategy evaluation and selection using interval-valued intuitionistic fuzzy additive ratio assessment approach based on similarity measures
title_sort low-carbon tourism strategy evaluation and selection using interval-valued intuitionistic fuzzy additive ratio assessment approach based on similarity measures
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8413083/
https://www.ncbi.nlm.nih.gov/pubmed/34493927
http://dx.doi.org/10.1007/s10668-021-01746-w
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