Simulation-Based Digital Twin of a Complex Shop-Floor Logistics System

Abstract

Digital analytics tools have been at the forefront of innovation in manufacturing industry in recent years. To keep pace with the demands of industrial digitization, companies seek opportunities to streamline processes and enhance overall efficacy, opting to replace conventional engineering tools with data-driven models. In a high-tech factory, detailed data is collected about the products, processes, and assets in near-real time, providing a basis to build trustworthy analytical models. In this paper, a novel discrete-event simulation (DES) model is proposed for the detailed representation of a complex shop-floor logistics system, employing automated robotic vehicles (AGV). The simulation model is applied to test new AGV management policies, involving both vehicle capacity planning and dispatching decisions. In order to illustrate the usefulness of the model and the effectiveness of the selected policy, numerical results of a case-study are presented, in which the selected policy was realized in a real manufacturing environment.