Optimizing Drone-Based Humanitarian Relief in Post-Disaster Scenarios: A Hybrid MCDM and Maximum Coverage Approach

Abstract

This study proposes a novel hybrid decision-making framework that integrates expert-driven supply prioritization via the Stepwise Weight Assessment Ratio Analysis (SWARA) method with an operationally constrained Maximum Coverage Problem (MCP) model to optimize drone-based humanitarian logistics in post-disaster scenarios. Grounded in a real-world case study of the 2023 Kahramanmaraş earthquake, the model systematically elicits expert preferences to rank critical supplies such as food, medical items, and cold chain products, and embeds these weights directly into a constrained MCP formulation. The model incorporates drone-specific operational limits, including battery consumption, payload capacity, and round-trip feasibility, to ensure realistic deployment strategies. Results show that scenario configurations with four to five strategically located drone bases, each equipped with four to five drones, can increase the achieved priority-weighted delivered quantity by up to 35-40% compared to minimal base-drone configurations within the proposed model framework. Moreover, the proposed framework improves responsiveness by prioritizing urgent deliveries and supporting more timely allocation decisions under operational constraints. Unlike traditional MCP approaches that rely on static weights, this method offers a context-sensitive and scalable optimization model informed by field expertise. The findings underscore the potential of structured expert-based weighting combined with operational optimization to enhance the efficiency and responsiveness of drone-assisted disaster relief systems.