Digital Twin Architecture for Drone Logistics Operations

This study proposes a digital twin (DT) model for drone logistics systems. While DT models typically rely on real-time data from physical assets, identifying and structuring such parameters for drone logistics remains underdeveloped in current literature. Existing studies primarily focus on routing and fleet optimization, offering limited guidance on the systematic design of DT architectures for drone operations. To address this gap, this work conducts an evidence-based, structured review of 116 drone logistics models to extract and consolidate key operational parameters, resulting in the development of a Drone Logistics Parameter Framework (DLPF). Building on this foundation, the study introduces the Drone Logistics Digital Twin (DLDT) architecture and model. The DLDT is a five-dimensional system consisting of a physical entity, a virtual entity, a service interface, a data layer, and connection layers. The DLDT enables essential capabilities, including real-time monitoring, predictive maintenance, route optimization, last-mile delivery management, and scheduling. To validate the DLDT architecture and model, a route-optimization case demonstration within the DT environment illustrates their internal coherence and practical utility. Hence, this study contributes to a structured approach for designing DT models in drone logistics, addressing a notable gap in the literature and offering actionable insights for advancing intelligent, data-driven logistics operations.