The Improved CURLI Method for Multi-Criteria Decision Making

Multi-Criteria Decision Making (MCDM) investigates the best available choice in the presence of multiple conflicting criteria, whereas the Collaborative Unbiased Rank List Integration (CURLI) method has been proposed recently and has been applied in various fields of daily life. However, most previ...

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Main Author: Anh-Tu Nguyen
Format: Article
Language:English
Published: D. G. Pylarinos 2023-02-01
Series:Engineering, Technology & Applied Science Research
Subjects:
Online Access:https://etasr.com/index.php/ETASR/article/view/5538
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author Anh-Tu Nguyen
author_facet Anh-Tu Nguyen
author_sort Anh-Tu Nguyen
collection DOAJ
description Multi-Criteria Decision Making (MCDM) investigates the best available choice in the presence of multiple conflicting criteria, whereas the Collaborative Unbiased Rank List Integration (CURLI) method has been proposed recently and has been applied in various fields of daily life. However, most previous works concentrated on analyzing cases in which the factor of a criterion is a specific quantity. The present paper proposes an approach developed from the original CURLI method, named Improved CURLI. This improvement helps solve a problem when the factors of the criteria can be linguistic variables or a data set. The proposed method is applied to rank the alternatives for two case studies: choosing the best grinding wheel and the best service suppliers. The ranking results are compared to those obtained using other methods. Furthermore, sensitivity analysis is also conducted to examine the stability and reliability of the ranking results in various scenarios. The results demonstrate the validity of the Improved CURLI method and prove that it is applicable for making decisions in various fields.
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spelling doaj.art-661576a5d2044a9e8595e83598405a6a2023-02-06T05:00:16ZengD. G. PylarinosEngineering, Technology & Applied Science Research2241-44871792-80362023-02-0113110.48084/etasr.5538The Improved CURLI Method for Multi-Criteria Decision MakingAnh-Tu Nguyen0Faculty of Mechanical Engineering, Hanoi University of Industry, Vietnam Multi-Criteria Decision Making (MCDM) investigates the best available choice in the presence of multiple conflicting criteria, whereas the Collaborative Unbiased Rank List Integration (CURLI) method has been proposed recently and has been applied in various fields of daily life. However, most previous works concentrated on analyzing cases in which the factor of a criterion is a specific quantity. The present paper proposes an approach developed from the original CURLI method, named Improved CURLI. This improvement helps solve a problem when the factors of the criteria can be linguistic variables or a data set. The proposed method is applied to rank the alternatives for two case studies: choosing the best grinding wheel and the best service suppliers. The ranking results are compared to those obtained using other methods. Furthermore, sensitivity analysis is also conducted to examine the stability and reliability of the ranking results in various scenarios. The results demonstrate the validity of the Improved CURLI method and prove that it is applicable for making decisions in various fields. https://etasr.com/index.php/ETASR/article/view/5538MCDMCURLI methodimproved CURLI methoddata set
spellingShingle Anh-Tu Nguyen
The Improved CURLI Method for Multi-Criteria Decision Making
Engineering, Technology & Applied Science Research
MCDM
CURLI method
improved CURLI method
data set
title The Improved CURLI Method for Multi-Criteria Decision Making
title_full The Improved CURLI Method for Multi-Criteria Decision Making
title_fullStr The Improved CURLI Method for Multi-Criteria Decision Making
title_full_unstemmed The Improved CURLI Method for Multi-Criteria Decision Making
title_short The Improved CURLI Method for Multi-Criteria Decision Making
title_sort improved curli method for multi criteria decision making
topic MCDM
CURLI method
improved CURLI method
data set
url https://etasr.com/index.php/ETASR/article/view/5538
work_keys_str_mv AT anhtunguyen theimprovedcurlimethodformulticriteriadecisionmaking
AT anhtunguyen improvedcurlimethodformulticriteriadecisionmaking