Internal hospital logistics are inherently complex, characterized by the critical need to move essential materials with high efficiency, precision, and safety. The adoption of automated guided vehicles (AGVs) is essential for automating these flows, but designing and optimizing their routes represents a significant challenge. This study presents a methodology for analyzing and optimizing AGV paths within healthcare facilities, effectively managing three-dimensional spatial complexity. The methodology leverages BIM and the open IFC standard to obtain an accurate geometric and semantic representation of the building. These data are then converted into a graph model using graph theory. Pathfinding algorithms, such as A*, are applied to this graph to calculate and optimize AGV trajectories, considering operational and collision constraints. The approach provides distance-optimized AGV paths. The integration of BIM, IFC, and graph theory proves to be an effective tool for logistical planning, simulation, and proactive management of AGVs in multi-level environments. This research contributes to the digital transformation of the construction sector by demonstrating how the integration of open standards and advanced algorithms can optimize the operational performance of complex buildings. By bridging the gap between architectural modeling and robotic logistics, the proposed approach supports the development of “smart buildings” and promotes more sustainable and technologically advanced management of healthcare facilities.
Toldo et al. (Wed,) studied this question.