Role of unmanned air vehicles in sustainable supply chain: queuing theory and ant colony optimization approach

The COVID-19 pandemic disrupted global supply chains and posed significant challenges to human lives. Innovative strategies, such as the use of robotics and autonomous systems, including Unmanned Aerial Vehicles (UAVs), can help mitigate these challenges. Although UAVs are increasingly employed in various commercial applications, effective path-planning strategies are essential for supporting supply chain operations while avoiding collisions and congestion. This study aims to develop a three-dimensional path-planning algorithm for UAVs using the Ant Colony Optimization (ACO) metaheuristic algorithm, while considering the application of queuing theory in a three-dimensional environment. The generated paths are compared with those developed in previous studies using conventional methods. Furthermore, the study employs a hybrid algorithm combining Interfered Fluid Dynamical Systems and the Lyapunov Guidance Vector Field, which has demonstrated superior performance compared with ACO in terms of computational time and the generation of multiple paths. Additionally, this study develops a framework to avoid supply chain obstacles and potential collisions involving multiple UAVs. Despite the growing use of drones in supply chain operations, a considerable gap remains between academic research and industry adoption. This study seeks to bridge this gap by offering practical insights into the use of UAVs for more sustainable, efficient, and resilient supply chain operations.

Computational Intelligence Techniques for Sustainable Supply Chain Management, 2024, 57–86