Modeling drivers and disruptions to increase resilience in port systems
This research aims to develop a comprehensive methodology for analyzing the influence of various drivers on port resilience, focusing on the interaction between these drivers and disruptions that impact the resilience of port systems. The study is structured into four main stages: building a framewo...
- Autores:
-
González Solano, Fernando Rafael
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2024
- Institución:
- Universidad del Norte
- Repositorio:
- Repositorio Uninorte
- Idioma:
- eng
- OAI Identifier:
- oai:manglar.uninorte.edu.co:10584/13090
- Acceso en línea:
- http://hdl.handle.net/10584/13090
- Palabra clave:
- Planificación empresarial
Terminales marítimos -- Barranquilla (Colombia) -- Aspectos económicos
Gestión industrial
Toma de decisiones
- Rights
- openAccess
- License
- https://creativecommons.org/licenses/by/4.0/
id |
REPOUNORT2_84fae6b9fa215000902e93b801e29eef |
---|---|
oai_identifier_str |
oai:manglar.uninorte.edu.co:10584/13090 |
network_acronym_str |
REPOUNORT2 |
network_name_str |
Repositorio Uninorte |
repository_id_str |
|
dc.title.en_US.fl_str_mv |
Modeling drivers and disruptions to increase resilience in port systems |
dc.title.translated.es_ES.fl_str_mv |
Modelación de impulsores y disrupciones para incrementar la resiliencia en sistemas portuarios |
dc.title.abbreviated.es_ES.fl_str_mv |
Modeling Drivers and Disruptions |
title |
Modeling drivers and disruptions to increase resilience in port systems |
spellingShingle |
Modeling drivers and disruptions to increase resilience in port systems Planificación empresarial Terminales marítimos -- Barranquilla (Colombia) -- Aspectos económicos Gestión industrial Toma de decisiones |
title_short |
Modeling drivers and disruptions to increase resilience in port systems |
title_full |
Modeling drivers and disruptions to increase resilience in port systems |
title_fullStr |
Modeling drivers and disruptions to increase resilience in port systems |
title_full_unstemmed |
Modeling drivers and disruptions to increase resilience in port systems |
title_sort |
Modeling drivers and disruptions to increase resilience in port systems |
dc.creator.fl_str_mv |
González Solano, Fernando Rafael |
dc.contributor.advisor.none.fl_str_mv |
Galindo Pacheco, Gina Romero Rodríguez, Daniel Hernando |
dc.contributor.author.none.fl_str_mv |
González Solano, Fernando Rafael |
dc.subject.lemb.none.fl_str_mv |
Planificación empresarial Terminales marítimos -- Barranquilla (Colombia) -- Aspectos económicos Gestión industrial Toma de decisiones |
topic |
Planificación empresarial Terminales marítimos -- Barranquilla (Colombia) -- Aspectos económicos Gestión industrial Toma de decisiones |
description |
This research aims to develop a comprehensive methodology for analyzing the influence of various drivers on port resilience, focusing on the interaction between these drivers and disruptions that impact the resilience of port systems. The study is structured into four main stages: building a framework for identifying drivers and disruptions, establishing the interactions between these factors, developing a model to measure resilience, and validating the proposed methodology through a case study in the port area of Barranquilla, Colombia. The first stage of the research consisted of a literature review guided by three key questions: (1) What types of disruptions occur in port operations? (2) What factors influence port resilience? (3) What approaches assess the interactions between resilience factors in port operations? To answer these questions, the literature review helped filter relevant articles and identify the documented directions for port resilience, as well as the different resilience drivers and strategies that authors have addressed in previous research. Based on this, we constructed a tool to validate the port resilience drivers and strategies and their actual application in port resilience with a group of 8 port experts. This allowed us to build a complete framework of port resilience strategies classified by port resilience drivers, which subsequently allowed us to build the resilience strategy prioritization model and the resilience measurement model. In the same way, port disruptions were also validated according to a real context. In the second stage, we developed a model for prioritizing port resilience strategies. This model assists decision-makers in allocating resources more efficiently by identifying the most impactful resilience strategies. To achieve this, a multi-criteria decision-making approach was applied, specifically using the DEMATEL method to analyze the interactions between various port resilience strategies identified in the literature and by practitioners. The results from DEMATEL were then used to prioritize these strategies using the Interpretative Structural Modeling method. This combined methodology allows port managers to allocate limited resources to resilience strategies that will have the greatest system-wide impact. The proposed model is adaptable to different port contexts worldwide, as long as resilience strategies are validated by experts from the relevant region or nation. To ensure robustness, port management experts with deep knowledge of the specific ports were selected for paired comparisons. The methodology was demonstrated in a case study within the Chilean port context, where port stakeholders such as authorities and terminal operators participated in the comparison process. The two last stages focused on developing a model for measuring port resilience, based on the findings of the previous stage and its validation. A Bayesian Network-based model was constructed to evaluate resilience, considering both disruptions and resilience drivers. This model was validated in a port terminal located in Barranquilla, Colombia, specifically in the area of clean bulk cargo handling. The case study demonstrated the versatility of the methodology, showing that it can be applied to any port terminal, with the success of the implementation depending largely on the selection of an expert group to review the resilience strategies and a focus group to evaluate the direct influences between these strategies. The study identified 18 resilience strategies, grouped into three key resilience capabilities: absorptive capacity, adaptive capacity, and restoration capacity. The findings highlighted that adaptation and restoration strategies have a significant impact on port resilience, while absorptive capacity was found to be less influential. Key strategies identified for adaptive capacity included communication protocols, the use of alternative transport modes, and remote access to information. For restoration capacity, the development of protocols for restoring both physical and technological infrastructure proved crucial for maintaining operational continuity. The BN model illustrated that the interactions between resilience strategies significantly affect overall resilience. The comparison of different scenarios revealed that neglecting these interactions can lead to an underestimation of the impacts of critical resilience strategies, potentially resulting in a misallocation of resources by port terminals. In conclusion, the research provides a robust framework for understanding and improving port resilience through the identification and prioritization of key drivers and disruptions. The use of a multi-criteria decision-making approach, combined with a Bayesian Network model for resilience measurement, offers a novel method for port authorities and terminal operators to enhance their resilience strategies. By validating the methodology in the context of the Barranquilla port, the study demonstrates its practical applicability, offering valuable lessons for ports worldwide. The findings emphasize the importance of prioritizing resilience strategies that have the greatest system-wide impact and highlight the need for collaboration among port stakeholders to ensure continuity during disruptions. |
publishDate |
2024 |
dc.date.issued.none.fl_str_mv |
2024 |
dc.date.accessioned.none.fl_str_mv |
2025-01-22T19:31:24Z |
dc.date.available.none.fl_str_mv |
2025-01-22T19:31:24Z |
dc.type.es_ES.fl_str_mv |
Trabajo de grado - Doctorado |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_71e4c1898caa6e32 |
dc.type.coar.es_ES.fl_str_mv |
http://purl.org/coar/resource_type/c_db06 |
dc.type.driver.es_ES.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
dc.type.content.es_ES.fl_str_mv |
Text |
dc.type.redcol.es_ES.fl_str_mv |
https://purl.org/redcol/resource_type/CC |
format |
http://purl.org/coar/resource_type/c_db06 |
dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/10584/13090 |
url |
http://hdl.handle.net/10584/13090 |
dc.language.iso.es_ES.fl_str_mv |
eng |
language |
eng |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.rights.creativecommons.es_ES.fl_str_mv |
https://creativecommons.org/licenses/by/4.0/ |
dc.rights.accessrights.es_ES.fl_str_mv |
info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/4.0/ http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
dc.format.es_ES.fl_str_mv |
application/pdf |
dc.format.extent.es_ES.fl_str_mv |
149 páginas |
dc.publisher.es_ES.fl_str_mv |
Universidad del Norte |
dc.publisher.program.es_ES.fl_str_mv |
Doctorado en Ingeniería Industrial |
dc.publisher.department.es_ES.fl_str_mv |
Departamento de ingeniería industrial |
dc.publisher.place.es_ES.fl_str_mv |
Barranquilla, Colombia |
institution |
Universidad del Norte |
bitstream.url.fl_str_mv |
https://manglar.uninorte.edu.co/bitstream/10584/13090/7/Resumen%20Tesis%20Doctorado.pdf https://manglar.uninorte.edu.co/bitstream/10584/13090/1/Thesis_Dissertation_PortResilience_Bayes%20Biblioteca.pdf https://manglar.uninorte.edu.co/bitstream/10584/13090/2/Formato%20de%20Aprobaci%c3%b3n_Tesis%20Doctoral_Fernando%20Gonz%c3%a1lez.pdf https://manglar.uninorte.edu.co/bitstream/10584/13090/3/Formatodeautorizacion_Biblioteca%20Signed%20%281%29.pdf https://manglar.uninorte.edu.co/bitstream/10584/13090/4/Acta%20Defensa%20Final%20Tesis.PDF https://manglar.uninorte.edu.co/bitstream/10584/13090/5/Rev_Turnitin%2017%20Enero.pdf https://manglar.uninorte.edu.co/bitstream/10584/13090/6/license.txt |
bitstream.checksum.fl_str_mv |
5751fba5ad0402f4d25f6a3347632175 1900cc085b2ef126bce3f8d725efb61b 2c2a59af32e14629575c971e7822b376 7988072e7e502b16ec2e346d9dc4f8c3 6cdf925ec4bd8b947e95a30d2a0c5ce9 906dd9231642546547ad5cb86f58743d 8a4605be74aa9ea9d79846c1fba20a33 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositorio Digital de la Universidad del Norte |
repository.mail.fl_str_mv |
mauribe@uninorte.edu.co |
_version_ |
1828169942523969536 |
spelling |
Galindo Pacheco, GinaRomero Rodríguez, Daniel HernandoGonzález Solano, Fernando Rafael2025-01-22T19:31:24Z2025-01-22T19:31:24Z2024http://hdl.handle.net/10584/13090This research aims to develop a comprehensive methodology for analyzing the influence of various drivers on port resilience, focusing on the interaction between these drivers and disruptions that impact the resilience of port systems. The study is structured into four main stages: building a framework for identifying drivers and disruptions, establishing the interactions between these factors, developing a model to measure resilience, and validating the proposed methodology through a case study in the port area of Barranquilla, Colombia. The first stage of the research consisted of a literature review guided by three key questions: (1) What types of disruptions occur in port operations? (2) What factors influence port resilience? (3) What approaches assess the interactions between resilience factors in port operations? To answer these questions, the literature review helped filter relevant articles and identify the documented directions for port resilience, as well as the different resilience drivers and strategies that authors have addressed in previous research. Based on this, we constructed a tool to validate the port resilience drivers and strategies and their actual application in port resilience with a group of 8 port experts. This allowed us to build a complete framework of port resilience strategies classified by port resilience drivers, which subsequently allowed us to build the resilience strategy prioritization model and the resilience measurement model. In the same way, port disruptions were also validated according to a real context. In the second stage, we developed a model for prioritizing port resilience strategies. This model assists decision-makers in allocating resources more efficiently by identifying the most impactful resilience strategies. To achieve this, a multi-criteria decision-making approach was applied, specifically using the DEMATEL method to analyze the interactions between various port resilience strategies identified in the literature and by practitioners. The results from DEMATEL were then used to prioritize these strategies using the Interpretative Structural Modeling method. This combined methodology allows port managers to allocate limited resources to resilience strategies that will have the greatest system-wide impact. The proposed model is adaptable to different port contexts worldwide, as long as resilience strategies are validated by experts from the relevant region or nation. To ensure robustness, port management experts with deep knowledge of the specific ports were selected for paired comparisons. The methodology was demonstrated in a case study within the Chilean port context, where port stakeholders such as authorities and terminal operators participated in the comparison process. The two last stages focused on developing a model for measuring port resilience, based on the findings of the previous stage and its validation. A Bayesian Network-based model was constructed to evaluate resilience, considering both disruptions and resilience drivers. This model was validated in a port terminal located in Barranquilla, Colombia, specifically in the area of clean bulk cargo handling. The case study demonstrated the versatility of the methodology, showing that it can be applied to any port terminal, with the success of the implementation depending largely on the selection of an expert group to review the resilience strategies and a focus group to evaluate the direct influences between these strategies. The study identified 18 resilience strategies, grouped into three key resilience capabilities: absorptive capacity, adaptive capacity, and restoration capacity. The findings highlighted that adaptation and restoration strategies have a significant impact on port resilience, while absorptive capacity was found to be less influential. Key strategies identified for adaptive capacity included communication protocols, the use of alternative transport modes, and remote access to information. For restoration capacity, the development of protocols for restoring both physical and technological infrastructure proved crucial for maintaining operational continuity. The BN model illustrated that the interactions between resilience strategies significantly affect overall resilience. The comparison of different scenarios revealed that neglecting these interactions can lead to an underestimation of the impacts of critical resilience strategies, potentially resulting in a misallocation of resources by port terminals. In conclusion, the research provides a robust framework for understanding and improving port resilience through the identification and prioritization of key drivers and disruptions. The use of a multi-criteria decision-making approach, combined with a Bayesian Network model for resilience measurement, offers a novel method for port authorities and terminal operators to enhance their resilience strategies. By validating the methodology in the context of the Barranquilla port, the study demonstrates its practical applicability, offering valuable lessons for ports worldwide. The findings emphasize the importance of prioritizing resilience strategies that have the greatest system-wide impact and highlight the need for collaboration among port stakeholders to ensure continuity during disruptions.DoctoradoDoctor en Ingeniería Industrialapplication/pdf149 páginasengUniversidad del NorteDoctorado en Ingeniería IndustrialDepartamento de ingeniería industrialBarranquilla, ColombiaModeling drivers and disruptions to increase resilience in port systemsModelación de impulsores y disrupciones para incrementar la resiliencia en sistemas portuariosModeling Drivers and DisruptionsTrabajo de grado - Doctoradohttp://purl.org/coar/resource_type/c_db06info:eu-repo/semantics/doctoralThesisTexthttps://purl.org/redcol/resource_type/CChttp://purl.org/coar/version/c_71e4c1898caa6e32https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Planificación empresarialTerminales marítimos -- Barranquilla (Colombia) -- Aspectos económicosGestión industrialToma de decisionesEstudiantesComunidad científica colombianaDoctoradoORIGINALResumen Tesis Doctorado.pdfResumen Tesis Doctorado.pdfapplication/pdf442670https://manglar.uninorte.edu.co/bitstream/10584/13090/7/Resumen%20Tesis%20Doctorado.pdf5751fba5ad0402f4d25f6a3347632175MD57Thesis_Dissertation_PortResilience_Bayes Biblioteca.pdfThesis_Dissertation_PortResilience_Bayes Biblioteca.pdfDocumento de Tesisapplication/pdf4445690https://manglar.uninorte.edu.co/bitstream/10584/13090/1/Thesis_Dissertation_PortResilience_Bayes%20Biblioteca.pdf1900cc085b2ef126bce3f8d725efb61bMD51Formato de Aprobación_Tesis Doctoral_Fernando González.pdfFormato de Aprobación_Tesis Doctoral_Fernando González.pdfFormato aprobación de trabajos de grados, trabajos de investigación y/o tesisapplication/pdf1216364https://manglar.uninorte.edu.co/bitstream/10584/13090/2/Formato%20de%20Aprobaci%c3%b3n_Tesis%20Doctoral_Fernando%20Gonz%c3%a1lez.pdf2c2a59af32e14629575c971e7822b376MD52Formatodeautorizacion_Biblioteca Signed (1).pdfFormatodeautorizacion_Biblioteca Signed (1).pdfFormato de autorización del trabajo de grado, trabajo de investigación y tesisapplication/pdf623595https://manglar.uninorte.edu.co/bitstream/10584/13090/3/Formatodeautorizacion_Biblioteca%20Signed%20%281%29.pdf7988072e7e502b16ec2e346d9dc4f8c3MD53Acta Defensa Final Tesis.PDFActa Defensa Final Tesis.PDFActa de sustentaciónapplication/pdf417317https://manglar.uninorte.edu.co/bitstream/10584/13090/4/Acta%20Defensa%20Final%20Tesis.PDF6cdf925ec4bd8b947e95a30d2a0c5ce9MD54Rev_Turnitin 17 Enero.pdfRev_Turnitin 17 Enero.pdfInforme de Originalidadapplication/pdf20513https://manglar.uninorte.edu.co/bitstream/10584/13090/5/Rev_Turnitin%2017%20Enero.pdf906dd9231642546547ad5cb86f58743dMD55LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://manglar.uninorte.edu.co/bitstream/10584/13090/6/license.txt8a4605be74aa9ea9d79846c1fba20a33MD5610584/13090oai:manglar.uninorte.edu.co:10584/130902025-02-04 10:17:15.22Repositorio Digital de la Universidad del Nortemauribe@uninorte.edu.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 |