Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly.

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-in time (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.