Designing a Global Warming-Resilient Humanitarian Welfare Network: A Multi-Objective Optimization Approach to Minimize Cost, Shortage, and Distributional Inequity

Abstract: This study introduces a multi-objective optimization model for formulating global warming-resilient humanitarian welfare network to minimize total cost, minimize total shortage, and minimize distributional inequity for flood response in flood prone areas. The framework strategically addresses the complexities of disaster relief by considering five key echelons: affected areas, distribution hubs, hospitals, temporary accommodation centers and temporary care centers to integrates casualties costs at temporary care and accommodation centers, transportation cost across all echelons or routes, facilities establishment cost, relief supply shortage ,and distributional inequity .. We employ the epsilon-constraint (𝜀-constraint), Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and modified multi-objective particle swarm optimization (MMOPSO) methods to solve the complex optimization problem. The proposed approach for the formulated model efficacy was demonstrated through its application to June/July 2025, real-world case study in Sapele and Amukpe environs, Delta State, Nigeria, a region significantly impacted by perennial floods exacerbated by global warming. Numerical simulations reveal that the MMOPSO method outperforms NSGA-II and 𝜀-constraint methods in terms of solutions quality and computational efficiency to provide Pareto-optimal solutions that balance response time, costs, relief shortage, distributional inequity and community impact. This research contributes to the development of data-driven decision support systems for humanitarian welfare network, enhancing the resilience of communities prone to floods and other disasters. The framework offers policymakers a tool to balance overall costs-efficiency associated with facilities location-allocation, and to minimize distributional inequity in global warming disasters-induced prone regions.