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 employing robotics and autonomous systems, including Unmanned Air Vehicles (UAVs), can mitigate these challenges. Although UAVs are increasingly used in various commercial applications, nonetheless, it is imperative to strategize path planning effectively to address supply chain operations and circumvent collisions and congestions. This study aims to develop a 3D path planning algorithm for UAVs using the Ant Colony Optimization (ACO) metaheuristic algorithm, considering the convenience of applying queuing theory in a three-dimensional space. The generated paths will be compared to previous works that used conventional methods. Furthermore, we use the Interfered Fluid Dynamical Systems and Lyapunov Guidance Vector Field hybrid algorithm, which has shown superior performance compared to ACO in terms of computational time and multiple path creation. Additionally, this study develops a framework to avoid supply chain obstacles and possible collisions with multiple UAVs. Despite the increased use of drones in supply chains, there is a gap between academic research and industry adoption. This study aims to bridge this gap by providing insights for more sustainable supply chain operations using UAVs.