A novel sequential block path planning method for 3D unmanned aerial vehicle routing in sustainable supply chains

Managing sustainable supply chain operations in dynamic three-dimensional (3D) environments is a significant challenge. Unmanned Aerial Vehicles (UAVs) offer transformative solutions to supply chains. This study aims to enhance sustainable supply chain management by considering new opportunities for optimizing UAV networks. The primary objective is to develop advanced path planning and routing algorithms that improve the quality of service in a supply chain. We present a novel Sequential Block Path Planning (SBPP) method, a modified version of the heuristic D*Lite algorithm, to achieve the shortest logistics path with reduced computation time. We utilize queuing theory for task scheduling and UAV assignments within a supply chain network while ensuring efficient and effective task distribution. The results demonstrate that the proposed combination of routing and path planning algorithms significantly improves performance in 3D environments, resulting in shorter logistics paths, enhanced quality of service, and reduced computation time. The outcomes of this study represent a substantial contribution to UAV network management, particularly in terms of efficiency and operational effectiveness. The novel approach utilized in this study contributes to the emerging UAV field in supply chains and enhances sustainability and operational efficiency in logistics networks.