Simulation of autonomous guided vehicles movement configurations for material transport in a specialized painting workstation

Abstract This study analyzes potential productivity improvements from implementing Autonomous Guided Vehicles (AGV) in a painting workstation characterized by variable processing times within an industrial drive manufacturing environment. The research is distinctive in applying AGV simulation to a niche and underexplored stage—specialized painting—while empirically assessing the impact of disruptive automation on an existing production system. A simulation-based experimental method was employed to evaluate three operational scenarios: the current process, a deck-lift AGV implementation, and a tugger AGV implementation. Results show that the deck-lift AGV scenario delivers the most substantial performance improvements, reducing waiting time by 99.17% and decreasing lead times by 33.78–92.16%, depending on the painting type. Furthermore, it enhances spatial efficiency, reducing required floor space by 84.76% compared to the current layout. The study contributes to the literature by detailing the simulation modeling steps necessary for accurately assessing AGV impact and by offering insights into the design and evaluation of AGV-based material handling systems. This work provides a framework for decision-making in the adoption of autonomous technologies in complex manufacturing environments. Furthermore, explores the performance benefits of implementing AGVs in a specialized painting context (non-standard, high-variability, excluded from automation), providing new insights and evidence for research and industrial practice. Graphical Abstract