- -

Optimising the Preparedness Capacity of Enterprise Resilience Using Mathematical Programming

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Optimising the Preparedness Capacity of Enterprise Resilience Using Mathematical Programming

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: SanchisDuran-Hera ...
Tamaño: 2.006Mb
Formato: PDF
Descripción: Versión editorial
Abrir
dc.contributor.author Sanchis, R. es_ES
dc.contributor.author Duran-Heras, Alfonso es_ES
dc.contributor.author Poler, R. es_ES
dc.date.accessioned 2021-09-10T03:30:52Z
dc.date.available 2021-09-10T03:30:52Z
dc.date.issued 2020-09 es_ES
dc.identifier.uri http://hdl.handle.net/10251/171997
dc.description.abstract [EN] In today's volatile business arena, companies need to be resilient to deal with the unexpected. One of the main pillars of enterprise resilience is the capacity to anticipate, prevent and prepare in advance for disruptions. From this perspective, the paper proposes a mixed-integer linear programming (MILP) model for optimising preparedness capacity. Based on the proposed reference framework for enterprise resilience enhancement, the MILP optimises the activation of preventive actions to reduce proneness to disruption. To do so, the objective function minimizes the sum of the annual expected cost of disruptive events after implementing preventive actions and the annual cost of such actions. Moreover, the algorithm includes a constraint capping the investment in preventive actions and an attenuation formula to deal with the joint savings produced by the activation of two or more preventive actions on the same disruptive event. The management and business rationale for proposing the MILP approach is to keep it as simple and comprehensible as possible so that it does not require highly mathematically skilled personnel, thus allowing top managers at enterprises of any size to apply it effortlessly. Finally, a real pilot case study was performed to validate the mathematical formulation. es_ES
dc.description.sponsorship This work was supported by the Spanish State Research Agency (Agencia Estatal de Investigacion) under the Reference No. RTI2018-101344-B-I00-AR. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Mathematics es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Preparedness es_ES
dc.subject Enterprise resilience es_ES
dc.subject Optimisation es_ES
dc.subject Mathematical programming es_ES
dc.subject MILP es_ES
dc.subject.classification ORGANIZACION DE EMPRESAS es_ES
dc.title Optimising the Preparedness Capacity of Enterprise Resilience Using Mathematical Programming es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/math8091596 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-101344-B-I00/ES/OPTIMIZACION DE TECNOLOGIAS DE PRODUCCION CERO-DEFECTOS HABILITADORAS PARA CADENAS DE SUMINISTRO 4.0/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses es_ES
dc.description.bibliographicCitation Sanchis, R.; Duran-Heras, A.; Poler, R. (2020). Optimising the Preparedness Capacity of Enterprise Resilience Using Mathematical Programming. Mathematics. 8(9):1-29. https://doi.org/10.3390/math8091596 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/math8091596 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 29 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 8 es_ES
dc.description.issue 9 es_ES
dc.identifier.eissn 2227-7390 es_ES
dc.relation.pasarela S\424221 es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.description.references Day, J. M. (2013). Fostering emergent resilience: the complex adaptive supply network of disaster relief. International Journal of Production Research, 52(7), 1970-1988. doi:10.1080/00207543.2013.787496 es_ES
dc.description.references Kumar, S., & Anbanandam, R. (2019). An integrated Delphi – fuzzy logic approach for measuring supply chain resilience: an illustrative case from manufacturing industry. Measuring Business Excellence, 23(3), 350-375. doi:10.1108/mbe-01-2019-0001 es_ES
dc.description.references Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain resilience. The International Journal of Logistics Management, 20(1), 124-143. doi:10.1108/09574090910954873 es_ES
dc.description.references Madni, A. M., & Jackson, S. (2009). Towards a Conceptual Framework for Resilience Engineering. IEEE Systems Journal, 3(2), 181-191. doi:10.1109/jsyst.2009.2017397 es_ES
dc.description.references Gilly, J.-P., Kechidi, M., & Talbot, D. (2014). Resilience of organisations and territories: The role of pivot firms. European Management Journal, 32(4), 596-602. doi:10.1016/j.emj.2013.09.004 es_ES
dc.description.references Tomlin, B. (2006). On the Value of Mitigation and Contingency Strategies for Managing Supply Chain Disruption Risks. Management Science, 52(5), 639-657. doi:10.1287/mnsc.1060.0515 es_ES
dc.description.references Haimes, Y. Y., Crowther, K., & Horowitz, B. M. (2008). Homeland security preparedness: Balancing protection with resilience in emergent systems. Systems Engineering, 11(4), 287-308. doi:10.1002/sys.20101 es_ES
dc.description.references Sanchis, R., Canetta, L., & Poler, R. (2020). A Conceptual Reference Framework for Enterprise Resilience Enhancement. Sustainability, 12(4), 1464. doi:10.3390/su12041464 es_ES
dc.description.references Lee, A. V., Vargo, J., & Seville, E. (2013). Developing a Tool to Measure and Compare Organizations’ Resilience. Natural Hazards Review, 14(1), 29-41. doi:10.1061/(asce)nh.1527-6996.0000075 es_ES
dc.description.references Kim, Y., Chen, Y.-S., & Linderman, K. (2014). Supply network disruption and resilience: A network structural perspective. Journal of Operations Management, 33-34(1), 43-59. doi:10.1016/j.jom.2014.10.006 es_ES
dc.description.references Soni, U., Jain, V., & Kumar, S. (2014). Measuring supply chain resilience using a deterministic modeling approach. Computers & Industrial Engineering, 74, 11-25. doi:10.1016/j.cie.2014.04.019 es_ES
dc.description.references Munoz, A., & Dunbar, M. (2015). On the quantification of operational supply chain resilience. International Journal of Production Research, 53(22), 6736-6751. doi:10.1080/00207543.2015.1057296 es_ES
dc.description.references Pettit, T. J., Croxton, K. L., & Fiksel, J. (2013). Ensuring Supply Chain Resilience: Development and Implementation of an Assessment Tool. Journal of Business Logistics, 34(1), 46-76. doi:10.1111/jbl.12009 es_ES
dc.description.references Manopiniwes, W., & Irohara, T. (2016). Stochastic optimisation model for integrated decisions on relief supply chains: preparedness for disaster response. International Journal of Production Research, 55(4), 979-996. doi:10.1080/00207543.2016.1211340 es_ES
dc.description.references Sanchis, R., & Poler, R. (2019). Enterprise Resilience Assessment—A Quantitative Approach. Sustainability, 11(16), 4327. doi:10.3390/su11164327 es_ES
dc.description.references Namdar, J., Li, X., Sawhney, R., & Pradhan, N. (2017). Supply chain resilience for single and multiple sourcing in the presence of disruption risks. International Journal of Production Research, 56(6), 2339-2360. doi:10.1080/00207543.2017.1370149 es_ES
dc.description.references Wang, X., Herty, M., & Zhao, L. (2015). Contingent rerouting for enhancing supply chain resilience from supplier behavior perspective. International Transactions in Operational Research, 23(4), 775-796. doi:10.1111/itor.12151 es_ES
dc.description.references Aleksić, A., Stefanović, M., Arsovski, S., & Tadić, D. (2013). An assessment of organizational resilience potential in SMEs of the process industry, a fuzzy approach. Journal of Loss Prevention in the Process Industries, 26(6), 1238-1245. doi:10.1016/j.jlp.2013.06.004 es_ES
dc.description.references Tan, R. R., Aviso, K. B., Cayamanda, C. D., Chiu, A. S. F., Promentilla, M. A. B., Ubando, A. T., & Yu, K. D. S. (2016). A fuzzy linear programming enterprise input–output model for optimal crisis operations in industrial complexes. International Journal of Production Economics, 181, 410-418. doi:10.1016/j.ijpe.2015.10.012 es_ES
dc.description.references Tadić, D., Aleksić, A., Stefanović, M., & Arsovski, S. (2014). Evaluation and Ranking of Organizational Resilience Factors by Using a Two-Step Fuzzy AHP and Fuzzy TOPSIS. Mathematical Problems in Engineering, 2014, 1-13. doi:10.1155/2014/418085 es_ES
dc.description.references Tukamuhabwa, B. R., Stevenson, M., Busby, J., & Zorzini, M. (2015). Supply chain resilience: definition, review and theoretical foundations for further study. International Journal of Production Research, 53(18), 5592-5623. doi:10.1080/00207543.2015.1037934 es_ES
dc.description.references Shirali, G. A., Shekari, M., & Angali, K. A. (2016). Quantitative assessment of resilience safety culture using principal components analysis and numerical taxonomy: A case study in a petrochemical plant. Journal of Loss Prevention in the Process Industries, 40, 277-284. doi:10.1016/j.jlp.2016.01.007 es_ES
dc.description.references Tang, C. S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics, 103(2), 451-488. doi:10.1016/j.ijpe.2005.12.006 es_ES
dc.description.references Sanchis, R., & Poler, R. (2019). Origins of Disruptions Sources Framework to Support the Enterprise Resilience Analysis. IFAC-PapersOnLine, 52(13), 2062-2067. doi:10.1016/j.ifacol.2019.11.509 es_ES
dc.description.references Sanchis, R., & Poler, R. (2013). Definition of a framework to support strategic decisions to improve Enterprise Resilience. IFAC Proceedings Volumes, 46(9), 700-705. doi:10.3182/20130619-3-ru-3018.00600 es_ES
dc.description.references Luers, A. L., Lobell, D. B., Sklar, L. S., Addams, C. L., & Matson, P. A. (2003). A method for quantifying vulnerability, applied to the agricultural system of the Yaqui Valley, Mexico. Global Environmental Change, 13(4), 255-267. doi:10.1016/s0959-3780(03)00054-2 es_ES
dc.description.references Sandanam, A., Diedrich, A., Gurney, G., & Richardson, T. (2018). Perceptions of Cyclone Preparedness: Assessing the Role of Individual Adaptive Capacity and Social Capital in the Wet Tropics, Australia. Sustainability, 10(4), 1165. doi:10.3390/su10041165 es_ES
dc.description.references Tarafdar, M., & Qrunfleh, S. (2016). Agile supply chain strategy and supply chain performance: complementary roles of supply chain practices and information systems capability for agility. International Journal of Production Research, 55(4), 925-938. doi:10.1080/00207543.2016.1203079 es_ES
dc.description.references Svensson, G. (2002). A conceptual framework of vulnerability in firms’ inbound and outbound logistics flows. International Journal of Physical Distribution & Logistics Management, 32(2), 110-134. doi:10.1108/09600030210421723 es_ES
dc.description.references Pettit, T. J., Fiksel, J., & Croxton, K. L. (2010). ENSURING SUPPLY CHAIN RESILIENCE: DEVELOPMENT OF A CONCEPTUAL FRAMEWORK. Journal of Business Logistics, 31(1), 1-21. doi:10.1002/j.2158-1592.2010.tb00125.x es_ES
dc.description.references BDO Technology Risk Factor Report https://www.bdo.com/getattachment/d10c417f-beb7-4bb9-8835-2b2ec727ce2b/attachment.aspx?2017-Technology-Riskfactor-ReportBrochure_WEB.pdf es_ES
dc.description.references Supply Chain Resilience Report http://www.bcifiles.com/bci-supply-chain-resilience-2015.pdf es_ES
dc.description.references Lichtenstein, S., & Newman, J. R. (1967). Empirical scaling of common verbal phrases associated with numerical probabilities. Psychonomic Science, 9(10), 563-564. doi:10.3758/bf03327890 es_ES
dc.description.references Hamm, R. M. (1991). Selection of verbal probabilities: A solution for some problems of verbal probability expression. Organizational Behavior and Human Decision Processes, 48(2), 193-223. doi:10.1016/0749-5978(91)90012-i es_ES
dc.description.references Dickson, T. J. (2002). Calculating Risks: Fine’s Mathematical Formula 30 Years Later. Journal of Outdoor and Environmental Education, 6(1), 31-39. doi:10.1007/bf03400742 es_ES
dc.description.references Patterson, F. D., & Neailey, K. (2002). A Risk Register Database System to aid the management of project risk. International Journal of Project Management, 20(5), 365-374. doi:10.1016/s0263-7863(01)00040-0 es_ES
dc.description.references Chou, T.-C., & Talalay, P. (1983). Analysis of combined drug effects: a new look at a very old problem. Trends in Pharmacological Sciences, 4, 450-454. doi:10.1016/0165-6147(83)90490-x es_ES
dc.description.references Chou, T.-C., & Talalay, P. (1984). Quantitative analysis of dose-effect relationships: the combined effects of multiple drugs or enzyme inhibitors. Advances in Enzyme Regulation, 22, 27-55. doi:10.1016/0065-2571(84)90007-4 es_ES
dc.description.references Belen’kii, M. S., & Schinazi, R. F. (1994). Multiple drug effect analysis with confidence interval. Antiviral Research, 25(1), 1-11. doi:10.1016/0166-3542(94)90089-2 es_ES
dc.description.references Glossary of Terms and Symbols Used in Pharmacology. Pharmacology and Experimental Therapeutics Department at Boston University School of Medicine http://www.bumc.bu.edu/busm-pm/academics/resources/glossary/ es_ES
dc.description.references Sanchis, R., & Poler, R. (2019). Mitigation proposal for the enhancement of enterprise resilience against supply disruptions. IFAC-PapersOnLine, 52(13), 2833-2838. doi:10.1016/j.ifacol.2019.11.638 es_ES
dc.description.references Dunning, I., Huchette, J., & Lubin, M. (2017). JuMP: A Modeling Language for Mathematical Optimization. SIAM Review, 59(2), 295-320. doi:10.1137/15m1020575 es_ES
dc.description.references Jump https://jumdev/JuMjl/dev es_ES
dc.description.references Forrest, J., & Lougee-Heimer, R. (2005). CBC User Guide. Emerging Theory, Methods, and Applications, 257-277. doi:10.1287/educ.1053.0020 es_ES
dc.description.references Agencia Estatal de Meteorología. Las Gotas Frías/DANAs: Ideas y Conceptos Básicos https://www.aemet.es/documentos/es/conocermas/recursos_en_linea/publicaciones_y_estudios/estudios/dana_ext.pdf es_ES
dc.description.references Driessen, P., Hegger, D., Kundzewicz, Z., van Rijswick, H., Crabbé, A., Larrue, C., … Wiering, M. (2018). Governance Strategies for Improving Flood Resilience in the Face of Climate Change. Water, 10(11), 1595. doi:10.3390/w10111595 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem