2026-04-27T08:18:53Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/738812025-01-15T20:01:30Zcom_10324_1173com_10324_931com_10324_894col_10324_1372

Fuzzy logic-based approaches in supply chain risk management: a review Díaz Curbelo, Alina Gento Municio, Ángel Manuel Espin Andrade, Rafael Alejandro Producción Científica Uncertainty is inherent in the supply chains nature. In the context of various uncertainties, risk management plays a crucial role in effective supply chain management. The uncertainty involved in the risk assessment process can be divided into two types: random uncertainty and epistemic uncertainty. The fuzzy theory has been applied to address uncertainties in this context. The purpose of this paper is to develop a literature review of the major contributions of fuzzy logic in addressing uncertainty in supply chain risk management approaches. The results revealed that integration with disruptive analysis tools and multi-criteria decision-making methods are the most common types adopted, with the increasing trend of Petri nets and Bayesian approaches. The reviewed literature highlights some limitations related to the holistic complexity of risks in supply chains, the dynamic nature of the environment, and the reliability of the knowledge base in the assessment. In that sense, these observations reveal interesting future lines of research. 2025-01-15T14:02:53Z 2025-01-15T14:02:53Z 2021 9999-12-31 info:eu-repo/semantics/bookPart Witold Pedrycz, Luis Martínez, Rafael Alejandro Espin-Andrade, Gilberto Rivera, Jorge Marx Gómez (eds.). Computational Intelligence for Business Analytics. 2021, p. 79-94. 978-3-030-73818-1 https://uvadoc.uva.es/handle/10324/73881 10.1007/978-3-030-73819-8_5 79 94 Computational Intelligence for Business Analytics eng Studies in Computational Intelligence, 953 ink.springer.com/chapter/10.1007/978-3-030-73819-8_5 info:eu-repo/semantics/restrictedAccess © The Author(s) Springer