A decision-making framework for the design of local production networks under largescale disruptions

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dc.contributor.authorHaddad, Yousef
dc.contributor.authorSalonitis, Konstantinos
dc.contributor.authorEmmanouilidis, Christos
dc.date.accessioned2021-11-08T11:56:53Z
dc.date.available2021-11-08T11:56:53Z
dc.date.issued2021-11-03
dc.description.abstractIn this paper, a model-based decision-making framework for the design of localized networked production systems under largescale disruptions is developed. The framework consists of optimization and agent-based simulation models that run successively in an iterative manner, gradually improving the performance of the perceived system. The framework integrates uncertainty, provides decisions at different decision-making levels and embeds an algorithm that allows for communication between demand nodes and production sites once inventory shortages occur. The framework has been applied on a case study for the design of localized production and distribution networks, powered by additive manufacturing (AM), in South East England during the early stages of the COVID-19 pandemic outbreak. Results revealed that implementing the framework indeed results in performance improvements to AM-powered production networks, particularly with regards to inventory shortages and lead time.en_UK
dc.identifier.citationHaddad Y, Salonitis K, Emmanouilidis C. (2021) A decision-making framework for the design of local production networks under largescale disruptions. Procedia Manufacturing, Volume 55, 2021, pp. 393-400en_UK
dc.identifier.issn2351-9789
dc.identifier.urihttps://doi.org/10.1016/j.promfg.2021.10.054
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17248
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectlocalized productionen_UK
dc.subjectadditive manufacturingen_UK
dc.subjectagent-based modellingen_UK
dc.titleA decision-making framework for the design of local production networks under largescale disruptionsen_UK
dc.typeArticleen_UK

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