Evaluation of agriculture-food 4.0 supply chain approaches using Fermatean probabilistic hesitant-fuzzy sets based decision making model

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dc.authoridPamucar, Dragan/0000-0001-8522-1942
dc.authoridDeveci, Muhammet/0000-0002-3712-976X
dc.authoridDelen, Dursun/0000-0001-8857-5148
dc.authoridQahtan, Sarah/0000-0002-5636-5901
dc.authoridA.Alsattar, Hassan/0000-0003-1182-936X
dc.authorwosidPamucar, Dragan/AAG-8288-2019
dc.authorwosidDeveci, Muhammet/V-8347-2017
dc.authorwosidDelen, Dursun/AGA-9892-2022
dc.authorwosidQahtan, Sarah/E-9160-2019
dc.authorwosidA.Alsattar, Hassan/S-1079-2017
dc.contributor.authorQahtan, Sarah
dc.contributor.authorAlsattar, Hassan A.
dc.contributor.authorZaidan, A. A.
dc.contributor.authorDeveci, Muhammet
dc.contributor.authorPamucar, Dragan
dc.contributor.authorDelen, Dursun
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2024-05-19T14:41:31Z
dc.date.available2024-05-19T14:41:31Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThe benchmarking of agri-food 4.0 supply chain (Agri4SC) falls under the multiple criteria problem in supply chain visibility (SCV) and supply chain resource integration (SCRI) for improving data analytics capabilities and achieving sustainable performance (SP). It is considered a multiple criteria decision -making (MCDM) problem due to three main concerns, namely, multiple Agri4SC evaluation criteria including the SCV, SCRI and SP criteria. These criteria have relative importance and trade-offs. Despite the tremendous efforts over the last years, none of the developed Agri4SCs have met all of the essential Agri4SC evaluation criteria. Another concern raised in the evaluation and benchmarking of the Agri4SC is the uncertainty of experts. Thus, the main contribution of this research is to propose an Agri4SC benchmarking framework in SCV and SCRI for improving data analytics capabilities and achieving SP based on an extension of the proposed Fermatean probabilistic hesitant fuzzy sets (FPHFSs) and MCDM methods. The methodology process is divided into six main parts. Firstly, an Agri4SC decision matrix is formulated based on the intersection of the Agri4SC alternatives and criteria to cover multiple Agri4SC evaluation criteria issues. Secondly, novel FPHFSs are proposed along with their operational laws, score function, accuracy function, Fermatean probabilistic hesitant fuzzy average mean operator and Fermatean probabilistic hesitant fuzzy weighted average operator. The FPHFS can encompass more sophisticated and uncertain evaluation information. Thirdly, Fermatean probabilistic hesitant fuzzy weighted zero inconsistency is formulated to assign weights to the evaluation criteria. Fourthly, the Fermatean probabilistic hesitant fuzzy decision by opinion score method (FPH-FDOSM) is formulated and used to score the alternatives that were evaluated subjectively based on SCV criteria. Fifthly, the FPH-FDOSM-based multi attributive ideal-real comparative analysis (MAIRCA) scoring method with equal probabilities is proposed to score Agri4SC alternatives that were evaluated subjectively based on weighted economic, environmental and social factors. Lastly, the MAIRCA ranking method with unequal probabilities is introduced to benchmark Agri4SC alternatives that were evaluated objectively based on the weighted subcriteria of SP and the trade-offs amongst the identified criteria. The robustness and reliability of the results are tested via sensitivity analysis and Spearman's correlation coefficient.& COPY; 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).en_US
dc.description.sponsorshipRoyal School of Mines, Imperial College Londonen_US
dc.description.sponsorshipThanks in advance for the entire worker in this project, and the people who support in any way, also we want to thank Royal School of Mines, Imperial College London for the support which came from them.en_US
dc.identifier.doi10.1016/j.asoc.2023.110170
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85150018344en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.asoc.2023.110170
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5121
dc.identifier.volume138en_US
dc.identifier.wosWOS:001015327600001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectFermatean Probabilistic Hesitant Fuzzy Seten_US
dc.subjectAgriculture -Food 4en_US
dc.subject0 Supply Chainen_US
dc.subjectSustainabilityen_US
dc.subjectMulti -Criteria Decision Makingen_US
dc.titleEvaluation of agriculture-food 4.0 supply chain approaches using Fermatean probabilistic hesitant-fuzzy sets based decision making modelen_US
dc.typeArticleen_US

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