Integrated optimization of humanitarian relief chains: a multi-product nonlinear programming approach for disaster logistics under uncertainty

This research introduces a novel approach to optimize the distribution of relief items in post-disaster scenarios. Unlike traditional approaches, the model integrates dynamic demand and supply considerations, accounting for fluctuations in population movement within relief camps. The model uniquely incorporates the impact of disaster severity and risk on transportation costs, enabling a more realistic assessment of logistical challenges. By minimizing total costs, including transportation, inventory, and disposal of damaged items, our approach ensures cost-efficient distribution without compromising service delivery levels. A numerical example with a post-disaster relief distribution scenario demonstrates the model’s practical application. Sensitivity analysis with multiple parameters confirms the model’s robustness and potential to guide efficient and effective humanitarian relief operations. This study contributes to the field of relief chain optimization by offering a comprehensive framework that accounts for several factors influencing disaster logistics. The research highlights the model’s potential as a valuable tool for improving the efficiency and effectiveness of humanitarian relief efforts.