Mostrar el registro sencillo del ítem

dc.contributor.advisorGalindo Pacheco, Gina
dc.contributor.advisorRomero Rodríguez, Daniel Hernando
dc.contributor.authorGonzález Solano, Fernando Rafael
dc.date.accessioned2025-01-22T19:31:24Z
dc.date.available2025-01-22T19:31:24Z
dc.date.issued2024
dc.identifier.urihttp://hdl.handle.net/10584/13090
dc.description.abstractThis 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.
dc.formatapplication/pdfes_ES
dc.format.extent149 páginases_ES
dc.language.isoenges_ES
dc.publisherUniversidad del Nortees_ES
dc.titleModeling drivers and disruptions to increase resilience in port systemsen_US
dc.typeTrabajo de grado - Doctoradoes_ES
dc.publisher.programDoctorado en Ingeniería Industriales_ES
dc.publisher.departmentDepartamento de ingeniería industriales_ES
dc.description.degreelevelDoctoradoes_ES
dc.publisher.placeBarranquilla, Colombiaes_ES
dc.rights.creativecommonshttps://creativecommons.org/licenses/by/4.0/es_ES
dc.type.coarhttp://purl.org/coar/resource_type/c_db06es_ES
dc.type.driverinfo:eu-repo/semantics/doctoralThesises_ES
dc.type.contentTextes_ES
dc.type.redcolhttps://purl.org/redcol/resource_type/CCes_ES
dc.type.versioninfo:eu-repo/semantics/submittedVersiones_ES
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaes_ES
dc.title.translatedModelación de impulsores y disrupciones para incrementar la resiliencia en sistemas portuarioses_ES
dc.title.abbreviatedModeling Drivers and Disruptionses_ES
dc.description.degreenameDoctor en Ingeniería Industriales_ES
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2es_ES
dcterms.audience.educationalcontextEstudianteses_ES
dcterms.audience.redcolComunidad científica colombianaes_ES
dc.subject.lembPlanificación empresarial
dc.subject.lembTerminales marítimos -- Barranquilla (Colombia) -- Aspectos económicos
dc.subject.lembGestión industrial
dc.subject.lembToma de decisiones
dcterms.audience.professionaldevelopmentDoctoradoes_ES
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesses_ES


Ficheros en el ítem

No Thumbnail [100%x80]
No Thumbnail [100%x80]
No Thumbnail [100%x80]
No Thumbnail [100%x80]
No Thumbnail [100%x80]
No Thumbnail [100%x80]

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

Mostrar el registro sencillo del ítem