An innovative sustainable relief transportation model for post-catastrophe: Addressing uncertainty with flood-based case study

• Proposes a sustainable mathematical model for optimizing disaster resource allocation and redistribution. • Incorporates Trapezoidal Neutrosophic Numbers to address uncertainty in supply, demand, and logistics. • Minimizes cost and delivery time while ensuring equitable and efficient relief distribution. • Strengthens collaboration with Independent Aid Organizations (IAOs) to maximize resource effectiveness. • Enhances humanitarian logistics by promoting resilience, efficient surplus utilization, and reduced human suffering. The increasing frequency and intensity of natural disasters, such as floods and cyclones, present significant challenges to humanitarian logistics and disaster response efforts in Tamil Nadu, India. This study addresses the pressing need for effective resource allocation to alleviate the impacts of these disasters on vulnerable communities amidst uncertainty. The primary goal of this research is to develop a robust two-stage mathematical model that optimizes the allocation and reallocation of relief resources in post-disaster scenarios, thereby improving the efficiency of disaster response operations. The model is structured in two phases: the first phase focuses on the immediate distribution of essential supplies to affected areas, while the second phase deals with reallocating surplus resources to regions with ongoing needs. The computational framework of the model employs advanced techniques, including Neutrosophic Goal Programming (NGP), Fuzzy Goal Programming (FGP), and the Neutrosophic Compromise Approach (NCA), to manage uncertainties related to resource availability and demand, utilizing Trapezoidal Neutrosophic Numbers (TNNs) for optimization. A case study of the 2015 Chennai floods demonstrates the model's practical applicability, revealing significant reductions in allocation costs and improvements in response times. These findings underscore the model's potential to enhance the efficiency of disaster response operations. The insights gained from this research are valuable for policymakers and emergency management agencies, highlighting the importance of effective planning and resource management in disaster-prone regions. Ultimately, the proposed model contributes to building resilient communities capable of effectively responding to natural disasters. Future research could explore the model's applicability in diverse geographical contexts and integrate additional variables to further enhance its effectiveness.