Deriving the priority weights from incomplete hesitant fuzzy preference relations in group decision making

The concept of hesitant fuzzy preference relation (HFPR) has been recently introduced to allow the de- cision makers (DMs) to provide several possible preference values over two alternatives. This paper in- troduces a new type of fuzzy preference structure, called incomplete HFPRs, to describe hesit...

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Detalhes bibliográficos
Autores: Xu, Yejun, Chen, Lei, Rodríguez, Rosa M., Herrera, Francisco, Wang, Huimin
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2016
País:España
Recursos:Universidad de Jaén
Repositório:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
OAI Identifier:oai:ruja.ujaen.es:10953/1798
Acesso em linha:https://www.sciencedirect.com/science/article/abs/pii/S0950705116000770
https://hdl.handle.net/10953/1798
Access Level:Acceso aberto
Palavra-chave:Group decision making
Incomplete hesitant fuzzy preference relations
Multiplicative consistency
Additive consistency
Priority weights
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spelling Deriving the priority weights from incomplete hesitant fuzzy preference relations in group decision makingXu, YejunChen, LeiRodríguez, Rosa M.Herrera, FranciscoWang, HuiminGroup decision makingIncomplete hesitant fuzzy preference relationsMultiplicative consistencyAdditive consistencyPriority weightsThe concept of hesitant fuzzy preference relation (HFPR) has been recently introduced to allow the de- cision makers (DMs) to provide several possible preference values over two alternatives. This paper in- troduces a new type of fuzzy preference structure, called incomplete HFPRs, to describe hesitant and incomplete evaluation information in the group decision making (GDM) process. Furthermore, we define the concept of multiplicative consistency incomplete HFPR and additive consistency incomplete HFPR, and then propose two goal programming models to derive the priority weights from an incomplete HFPR based on multiplicative consistency and additive consistency respectively. These two goal programming models are also extended to obtain the collective priority vector of several incomplete HFPRs. Finally, a numerical example and a practical application in strategy initiatives are provided to illustrate the validity and applicability of the proposed models.National Natural Science Foundation of China (NSFC) under Grants (nos. 71101043 , 71471056 and 71433003 ), the Fundamental Research Funds for the Central Universities (no. 2015B23014 ), Program for Excellent Talents in Ho- hai University, State Scholarship Fund (no. 201406715021 ), and Spanish Ministry of Economy and Finance Postdoctoral Training ( FPDI-2013-18193 ).Elsevier202420242016info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.sciencedirect.com/science/article/abs/pii/S0950705116000770https://hdl.handle.net/10953/1798reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaéninstname:Universidad de JaénInglésKnowledge-Based Systems [2016]; [99];[71-78]Atribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:ruja.ujaen.es:10953/17982026-06-24T12:41:07Z
dc.title.none.fl_str_mv Deriving the priority weights from incomplete hesitant fuzzy preference relations in group decision making
title Deriving the priority weights from incomplete hesitant fuzzy preference relations in group decision making
spellingShingle Deriving the priority weights from incomplete hesitant fuzzy preference relations in group decision making
Xu, Yejun
Group decision making
Incomplete hesitant fuzzy preference relations
Multiplicative consistency
Additive consistency
Priority weights
title_short Deriving the priority weights from incomplete hesitant fuzzy preference relations in group decision making
title_full Deriving the priority weights from incomplete hesitant fuzzy preference relations in group decision making
title_fullStr Deriving the priority weights from incomplete hesitant fuzzy preference relations in group decision making
title_full_unstemmed Deriving the priority weights from incomplete hesitant fuzzy preference relations in group decision making
title_sort Deriving the priority weights from incomplete hesitant fuzzy preference relations in group decision making
dc.creator.none.fl_str_mv Xu, Yejun
Chen, Lei
Rodríguez, Rosa M.
Herrera, Francisco
Wang, Huimin
author Xu, Yejun
author_facet Xu, Yejun
Chen, Lei
Rodríguez, Rosa M.
Herrera, Francisco
Wang, Huimin
author_role author
author2 Chen, Lei
Rodríguez, Rosa M.
Herrera, Francisco
Wang, Huimin
author2_role author
author
author
author
dc.subject.none.fl_str_mv Group decision making
Incomplete hesitant fuzzy preference relations
Multiplicative consistency
Additive consistency
Priority weights
topic Group decision making
Incomplete hesitant fuzzy preference relations
Multiplicative consistency
Additive consistency
Priority weights
description The concept of hesitant fuzzy preference relation (HFPR) has been recently introduced to allow the de- cision makers (DMs) to provide several possible preference values over two alternatives. This paper in- troduces a new type of fuzzy preference structure, called incomplete HFPRs, to describe hesitant and incomplete evaluation information in the group decision making (GDM) process. Furthermore, we define the concept of multiplicative consistency incomplete HFPR and additive consistency incomplete HFPR, and then propose two goal programming models to derive the priority weights from an incomplete HFPR based on multiplicative consistency and additive consistency respectively. These two goal programming models are also extended to obtain the collective priority vector of several incomplete HFPRs. Finally, a numerical example and a practical application in strategy initiatives are provided to illustrate the validity and applicability of the proposed models.
publishDate 2016
dc.date.none.fl_str_mv 2016
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://www.sciencedirect.com/science/article/abs/pii/S0950705116000770
https://hdl.handle.net/10953/1798
url https://www.sciencedirect.com/science/article/abs/pii/S0950705116000770
https://hdl.handle.net/10953/1798
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Knowledge-Based Systems [2016]; [99];[71-78]
dc.rights.none.fl_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
instname:Universidad de Jaén
instname_str Universidad de Jaén
reponame_str RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
collection RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
repository.name.fl_str_mv
repository.mail.fl_str_mv
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