Development of a relief distribution model for emergency logistics

Purpose The study aims to create a realistic model for emergency logistics, specifically focusing on relief distribution during disasters, by incorporting the prioritization of nodes. It addresses the vehicle routing problem (VRP) and resource allocation problem (RAP) to optimize routes and resource delivery. By integrating these two problems, the study seeks to enhance the efficiency and effectiveness of emergency response operations, ensuring timely and equitable distribution of resources to affected areas. This model is particularly relevant for disaster management agencies looking to improve their logistical strategies and minimize the impact of disasters on communities. Design/methodology/approach The study uses a two-phase multi-objective model. In the first phase, it minimizes routing costs associated with the VRP. In the second phase, it focuses on minimizing penalty costs for total unsatisfied demand, addressing the RAP. The model uses the CPLEX solver and introduces the modified decomposition and assignment heuristic (MDAH) and genetic algorithm (GA) for handling large-scale scenarios. This approach ensures both computational efficiency and solution quality, making it suitable for practical applications in emergency logistics. Findings The model effectively addresses both the VRP and RAP, optimizing routes and resource allocation. However among both the solution algorithms MDAH is found to solve the model faster for large-scale problems, whereas solution of the model from GA provides better solution quality. The practical application of the model to the 2019 Alappuzha flood data demonstrates its utility in real-world disaster scenarios, showcasing its potential to enhance emergency response operations and resource distribution during disasters. Research limitations/implications The study is limited to single-depot scenarios and does not consider demand uncertainty. Future research could explore multiple depots, uncertain demands and integrate additional problems such as the location allocation problem and the casualty allocation problem. These extensions would enhance the model’s applicability and robustness, providing more comprehensive solutions for emergency logistics. Addressing these limitations would further improve the efficiency and effectiveness of disaster response operations, ensuring better preparedness and resource management. Practical implications The model provides valuable insights for emergency management agencies, aiding in decision-making processes for relief distribution. By minimizing routing and penalty costs, the model ensures timely and efficient delivery of resources, improving overall resource utilization during disasters. This practical application can significantly enhance the effectiveness of emergency response operations, reducing the impact of disasters on affected communities and ensuring that resources are distributed equitably and efficiently. Social implications By optimizing relief distribution, the model helps reduce fatalities and property damage during disasters. It ensures equitable resource distribution, addressing the needs of affected communities effectively. This contributes to the overall resilience of communities, helping them recover more quickly from disasters. The model’s focus on efficient and timely resource allocation can significantly improve the quality of life for disaster-affected populations, providing them with the necessary support to rebuild and recover. Originality/value The study presents a novel approach by integrating the VRP and RAP in a two-phase model for emergency logistics. This innovative approach offers practical solutions for large-scale disaster scenarios, enhancing the efficiency of relief operations. The model’s ability to address both routing and resource allocation challenges simultaneously sets it apart from existing models, providing a valuable tool for disaster management agencies looking to improve their logistical strategies and response capabilities.