ADAPTIVE LOGISTICS FOR CLIMATE RESILIENCE IN MULTIMODAL TRANSPORT USING BUSINESS INTELLIGENCE TOOLS

Transport networks are increasingly vulnerable to climate-driven disruptions, yet existing planning tools often fail to capture cascading failures across interdependent systems. This thesis addresses that gap by developing and validating an agent-based simulation framework to assess the resilience of multimodal freight corridors under compound hazards. The UK Midlands is used as a case study, where road freight operations, electric vehicle charging infrastructure, and flooding risks intersect with power dependency. A four-tier simulation architecture was implemented in AnyLogic, integrating GIS-based flood modelling, infrastructure accessibility, and adaptive routing for electric trucks. Four disruption scenarios were tested and resilience performance was evaluated using multi-metric indicators such as stop completion, charger availability, and weighted resilience scores. Results show that flooding reduces delivery completion by about 50%, while compound flood events intensify operational degradation. Validation against route data confirmed strong spatial accuracy, with an average error of only 1.02%. The study contributes a hybrid and scalable methodology linking facility-level disruptions to network-wide resilience and explicitly models power-dependent infrastructure within agent-based frameworks. Its outputs provide practical decision-support by identifying vulnerable corridors, informing charger siting and redundancy planning, and guiding investment in adaptive strategies. These findings are relevant for policymakers, infrastructure operators, and researchers addressing the resilience of freight systems under escalating climate risks.