Accessibility-aware multi-objective truck-drone hybrid routing for post-disaster humanitarian logistics

Recent large-scale disasters severely disrupt transportation infrastructures, making humanitarian logistics a complex decision-making problem under uncertainty and time pressure. In such environments, heterogeneous road accessibility substantially reduces the effectiveness of conventional ground-based distribution systems. Although truck–drone routing has attracted increasing attention in recent years, existing studies primarily focus on commercial delivery settings and rarely incorporate disaster-specific conditions such as disrupted accessibility and priority-sensitive demand structures. This study proposes an accessibility-aware hybrid humanitarian distribution framework integrating multiple trucks and drones for post-disaster operations. Demand points are categorized into fully accessible, partially accessible, and inaccessible regions according to post-disaster road conditions, and coordinated truck–drone operations are designed accordingly. The problem is formulated as a multi-objective optimization model that simultaneously minimizes makespan and priority-weighted total completion time. Given the NP-hard nature of the problem, a customized hierarchical Ant Colony Optimization (ACO) algorithm is developed to generate efficient routing and allocation decisions. The effectiveness of the proposed ACO framework is validated using disaster-adapted benchmark instances through comparisons with Adaptive Large Neighborhood Search (ALNS), Genetic Algorithm (GA), and Simulated Annealing (SA), demonstrating superior solution quality and robustness. The proposed framework is additionally demonstrated through a real-world case study conducted in Sakarya. Computational results indicate that the hybrid truck–drone system significantly improves operational efficiency, particularly under limited-accessibility conditions, and consistently outperforms truck-only distribution strategies. Overall, the study provides a practical, resilience-oriented, and accessibility-aware decision-support framework for enhancing the efficiency, equity, and responsiveness of post-disaster humanitarian logistics operations.