Social Sustainability and Resilience in Supply Chains of Latin America on COVID-19 Times: Classification Using Evolutionary Fuzzy Knowledge

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-09-15T00:30:04Z
Fecha de publicación2022-01-01
ResumenThe number of research papers interested in studying the social dimension of supply chain sustainability and resilience is increasing in the literature. However, the social dimension is complex, with several uncertainty variables that cannot be expressed with a traditional Boolean logic of totally true or false. To cope with uncertainty, Fuzzy Logic allows the development of models to obtain crisp values from the concept of fuzzy linguistic variables. Using the Structural Equation Model by Partial Least Squares (SEM-PLS) and Evolutionary Fuzzy Knowledge, this research aims to analyze the predictive power of social sustainability characteristics on supply chain resilience performance in the context of the COVID-19 pandemic with representative cases from Mexico and Chile. We validate our approach using the Chile database for training our model and the Mexico database for testing. The fuzzy knowledge database has a predictive power of more than 80%, using social sustainability features as inputs regarding supply chain resilience in the context of the COVID-19 pandemic disruption. To our knowledge, no works in the literature use fuzzy evolutionary knowledge to study social sustainability in correlation with resilience. Moreover, our proposed approach is the only one that does not require a priori expert knowledge or a systematic mathematical setup.es_ES
Doihttps://doi.org/10.3390/math10142371es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/2018
Idiomaeses_ES
EditorialMDPI AGes_ES
RelaciónMathematicses_ES
URL relacionadohttps://doi.org/10.3390/math10142371es_ES
DerechosAcceso abierto (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_abf2es_ES
FuenteMathematics
TítuloSocial Sustainability and Resilience in Supply Chains of Latin America on COVID-19 Times: Classification Using Evolutionary Fuzzy Knowledgees_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorReyna-Castillo, Miguel
AutorSantiago, Alejandro
AutorMartínez, Salvador Ibarra
AutorRocha, José Antonio Castán
AutorReyna-Castillo, Migueles_ES
AutorSantiago, Alejandroes_ES
AutorMartínez, Salvador Ibarraes_ES
AutorRocha, José Antonio Castánes_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Número14es_ES
Rango de páginas2371es_ES
URL relacionadahttps://doi.org/10.3390/math10142371
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen10es_ES

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