An integrated fuzzy MCDM approach based on Bonferroni functions for selection and evaluation of industrial robots for the automobile manufacturing industry

dc.authoridKundu, Pradip/0000-0001-8297-8894;
dc.authorwosidKundu, Pradip/O-3598-2016
dc.authorwosidGorcun, Omer Faruk/ADF-0541-2022
dc.contributor.authorGarg, Chandra Prakash
dc.contributor.authorGorcun, Omer F.
dc.contributor.authorKundu, Pradip
dc.contributor.authorKucukonder, Hande
dc.date.accessioned2023-10-19T15:12:14Z
dc.date.available2023-10-19T15:12:14Z
dc.date.issued2023
dc.department-temp[Garg, Chandra Prakash] Indian Inst Management Rohtak, Dept Operat Management & Quantitat Tech, Rohtak 124010, Haryana, India; [Gorcun, Omer F.] Kadir Has Univ, Dept Business Adm, Fac Econ Adm & Social Sci, Istanbul, Turkiye; [Kundu, Pradip] XIM Univ, Sch Comp Sci & Engn, Bhubaneswar, India; [Kundu, Pradip] Birla Global Univ, Fac Decis Sci & Operat Management, Bhubaneswar, India; [Kucukonder, Hande] Bartin Univ, Fac Econ & Adm, Dept Numer Methods, Bartin, Turkiyeen_US
dc.description.abstractIn recent years, there have been dramatic changes in manufacturing systems in many industries depending on technological developments. Robotics is one of the essential components of these changes. Today, the usage of robotics in manufacturing processes has become widespread in almost all industries. Also, it has become a very strong desire ever-increasing for even small and medium-sized enterprises at present. Almost all the previous studies emphasized that industrial robot selection is a highly complex decision-making problem as there are many conflicting factors and criteria. Besides, different and advanced specifications of these robotics added by robotic manufacturers have caused to increase the complexities much more. Hence, decision-makers encounter more complicated decision-making problems affected by many uncertainties. Because of that, an integrated fuzzy group MCDM framework can help overcome many ambiguities proposed in the current paper. The proposed fuzzy integrated model consists of the fuzzy SWARA (F-SWARA'B) and the fuzzy CoCoSo (F-CoCoSo'B), which are extended with the help of the Bonferroni function. The model selected the appropriate industrial robotics used in the automotive industry by considering 15 criteria and ten alternatives. According to the result of the study, the three most significant criteria have been determined: Working Accuracy, Reaching Distance, and Performance; and the most suitable option is the A8. The obtained results were validated with the help of a comprehensive sensitivity analysis consisting of different 150 scenarios. The results are also compared with some existing techniques. The sensitivity analysis results approve the validity and applicability of the proposed model.en_US
dc.identifier.citation21
dc.identifier.doi10.1016/j.eswa.2022.118863en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85139029285en_US
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2022.118863
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5384
dc.identifier.volume213en_US
dc.identifier.wosWOS:000870841200001en_US
dc.identifier.wosqualityQ1
dc.khas20231019-WoSen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDecision-Making ApproachEn_Us
dc.subjectSupplier SelectionEn_Us
dc.subjectTopsis MethodEn_Us
dc.subjectSwaraEn_Us
dc.subjectCriteriaEn_Us
dc.subjectQualityEn_Us
dc.subjectFuzzySWARA?Ben_US
dc.subjectDecision-Making Approach
dc.subjectFuzzyCoCoSo?Ben_US
dc.subjectSupplier Selection
dc.subjectAutomotive industryen_US
dc.subjectTopsis Method
dc.subjectRobot selectionen_US
dc.subjectSwara
dc.subjectBonferroni functionen_US
dc.subjectCriteria
dc.subjectFuzzy group multi-criteria decision makingen_US
dc.subjectQuality
dc.subject(FMCGDM)en_US
dc.titleAn integrated fuzzy MCDM approach based on Bonferroni functions for selection and evaluation of industrial robots for the automobile manufacturing industryen_US
dc.typeArticleen_US
dspace.entity.typePublication

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