Optimizing the Scheduling of Autonomous Guided Vehicle in a Manufacturing Process

Abstract

Autonomous Guided Vehicles (AGVs) are considered as one of the key enablers of smart factories which make possible smart and flexible transportation of pallets and material on shopfloor. However, existing AGV fleet management solutions often suffer from poor integration with real-time manufacturing operations information systems, which negatively affects scheduling of AGVs. To exploit the full potential of AGVs in achieving just-intime (JIT) transportation, there is a need for intelligent AGV fleet management system which not only integrate with manufacturing information technology (IT) and operational technology (OT) but also provide prediction for the shop-floor logistic based on real-time manufacturing operations information to optimize scheduling of AGVs. This paper presents an approach for a Smart AGV Management System (SAMS), which combines the real-time data analysis and digital twin models that can be deployed within complex manufacturing environments for optimized scheduling. For a proof of concept, a case study of a line side supply of components to a manual assembly station is presented.