Earthquake Decision-Making Tool for Humanitarian Logistics Network: An Application in Popayan, Colombia

Abstract

Background: This study presents a comprehensive methodology for enhancing humanitarian logistics planning and management in natural disasters, focusing on earthquakes. Methods: The innovative approach combines a deterministic mathematical model with a simulation model to address the problem from multiple perspectives, aiming to improve efficiency and equity in post-disaster supply distribution. In the deterministic modeling phase, optimal locations for humanitarian distribution centers and points in Popayan, Colombia, were identified, enabling efficient resource allocation for affected families. Subsequently, the simulation model evaluated scenarios based on real earthquakes in Colombia and Latin America, providing a comprehensive view of the logistics system’s response capacity to different disaster conditions and magnitudes. Results: The results demonstrated that the proposed methodology significantly reduced supply delivery time, achieving a 30% improvement compared to traditional humanitarian logistics approaches. Moreover, it led to a more equitable coverage of affected communities, with a 25% increase in families served in previously underserved areas. Expert validation from the Disaster Risk Management Committee of the study area confirmed the methodology’s usefulness for informed and effective decision-making in real situations. Conclusions: This integrated approach of mathematical modeling and discrete event simulation offers valuable insights to address disaster management and support decision-making in humanitarian crises.

A simulation-based performance evaluation model for decision support on drone location and delivery scheduling

“…To keep the focus of our literature review on the intersection of delivery drones and humanitarian logistics, we skip reviewing these studies. However, interested readers may refer to (D'Uffizi et al, 2015;Krejci, 2015;Mosterman et al, 2014;Paz-Orozco et al, 2023;Yale et al, 2020).…”