Risk-averse decision-making to maintain supply chain viability under propagated disruptions
| dc.contributor.author | Sawik, Tadeusz | |
| dc.contributor.author | Sawik, Bartosz | |
| dc.contributor.department | Estadística, Informática y Matemáticas | es_ES |
| dc.contributor.department | Estatistika, Informatika eta Matematika | eu |
| dc.date.accessioned | 2024-04-29T14:35:39Z | |
| dc.date.issued | 2023 | |
| dc.date.updated | 2024-04-29T14:20:02Z | |
| dc.description.abstract | In this paper, stochastic optimisation of CVaR is applied to maintain risk-averse viability and improve resilience of a supply chain under propagated disruptions. In order to establish the risk-averse boundaries on supply chain viability space, two stochastic optimisation models are developed with the two conflicting objectives: minimisation of Conditional Cost-at-Risk and maximisation of Conditional Service-at-Risk. Then, the risk-averse viable production trajectory between the two boundaries is selected using a stochastic mixed integer quadratic programming model. The proposed approach is applied to maintain the supply chain viability in the smartphone manufacturing and the results of computational experiments are provided. The findings indicate that when the decision-making is more risk-aversive, the size of the viability space between the two boundaries is greater. As a result, more room is available for selecting viable production trajectories under severe disruptions. Moreover, the larger is viability space, the higher is both worst-case and average resilience of the supply chain. Risk-neutral, single-objective decision-making may reduce the supply chain viability. A single-objective supply chain optimisation which moves production to the corresponding boundary of the viability space, should not be applied under severe disruption risks to avoid greater losses. | en |
| dc.embargo.lift | 2024-07-19 | |
| dc.embargo.terms | 2024-07-19 | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Sawik, T., & Sawik, B. (2024). Risk-averse decision-making to maintain supply chain viability under propagated disruptions. International Journal of Production Research, 62(8), 2853-2867. https://doi.org/10.1080/00207543.2023.2236726 | en |
| dc.identifier.doi | 10.1080/00207543.2023.2236726 | |
| dc.identifier.issn | 0020-7543 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/48043 | |
| dc.language.iso | eng | en |
| dc.publisher | Taylor & Francis | en |
| dc.relation.ispartof | International Journal of Production Research 2024, 62(8), 2853-2867 | en |
| dc.relation.publisherversion | https://doi.org/10.1080/00207543.2023.2236726 | |
| dc.rights | © 2023 Informa UK Limited, trading as Taylor & Francis Group. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.subject | Stochastic mixed integer linear programming | en |
| dc.subject | Stochastic mixed integer quadratic programming | en |
| dc.subject | Supply chain disruption management | en |
| dc.subject | Supply chain resilience | en |
| dc.subject | Supply chain viability | en |
| dc.title | Risk-averse decision-making to maintain supply chain viability under propagated disruptions | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | 66821c59-c6d7-464f-90ff-4661afdf1ada | |
| relation.isAuthorOfPublication.latestForDiscovery | 66821c59-c6d7-464f-90ff-4661afdf1ada |