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Bi-objective stochastic supply chain network design under supplier and facility disruption risks

2026-08Artigo científico Acesso aberto
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dc.contributor.authorFilho, Angelo Aliano
dc.contributor.authorCorreia, Isabel
dc.contributor.authorMelo, Teresa
dc.contributor.institutionDM - Departamento de Matemática
dc.contributor.institutionCMA - Centro de Matemática e Aplicações
dc.contributor.pblSpringer Nature
dc.date.accessioned2026-07-29T08:39:01Z
dc.date.available2026-07-29T08:39:01Z
dc.date.issued2026-08
dc.descriptionThis support is gratefully acknowledged. We also appreciate the insightful comments received during the review process, which contributed to improving the manuscript. Publisher Copyright: © The Author(s) 2026.
dc.description.abstractWe propose a novel bi-objective two-stage stochastic formulation for the problem of designing a resilient and agile supply chain network with suppliers, potential locations for facilities and customers. As suppliers and facilities are vulnerable to disruption risks, preparedness and reactive measures are considered. These include conditional backup sourcing, facility fortification and deferral of customer demand. This flexibility enhances the supply chain’s responsiveness to disruptions and to uncertainty in demand and costs. First-stage decisions define a schedule for facility deployment, the choice of fortification levels for unreliable facilities, and the selection of primary and backup suppliers. Once uncertainty is disclosed, second-stage decisions determine the activation of backup suppliers and the material flows across the network. Flows may include delayed deliveries to customers, bounded by a maximum lateness threshold. Two conflicting objectives are considered: minimising total expected cost and total expected unmet demand. We develop a tailored two-phase heuristic procedure that is embedded in the ε-constraint method. Computational experiments on randomly generated instances demonstrate the effectiveness of the proposed methodology, especially for large-scale instances where a state-of-the-art solver fails to find a feasible solution within a given time limit. Moreover, we provide a comparative analysis of a representative subset of Pareto-optimal solutions that highlights cost-resilience-responsiveness trade-offs to support decision-making.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent49
dc.format.extent1343323
dc.identifier.doi10.1007/s40314-026-03672-x
dc.identifier.issn2238-3603
dc.identifier.otherPURE: 160757375
dc.identifier.otherPURE UUID: 1ad12e62-300a-4708-9924-caa7c7663ba5
dc.identifier.otherScopus: 105033804309
dc.identifier.urihttp://hdl.handle.net/10362/204928
dc.identifier.urlhttps://www.scopus.com/pages/publications/105033804309
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/Avaliação UID 2023%2F2024/UID%2F00297%2F2025/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/Avaliação UID 2023%2F2024 PRR/UID%2FPRR%2F00297%2F2025/PT
dc.subjectDisruption risks
dc.subjectMIP-based heuristic
dc.subjectNetwork design
dc.subjectResilient and flexible supply chains
dc.subjectTwo-stage stochastic programming
dc.subjectComputational Mathematics
dc.subjectApplied Mathematics
dc.titleBi-objective stochastic supply chain network design under supplier and facility disruption risksen
dc.typejournal article
degois.publication.issue7
degois.publication.titleComputational and Applied Mathematics
degois.publication.volume45
dspace.entity.typePublication
rcaap.rightsopenAccess

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