Digital Twin Application for Dynamic Task Allocation in a Robotic System: A Flexsim-Based Case Study

In contemporary manufacturing systems the use of the Digital Twin technology provides valuable insights for enhancing real-time performance and adaptability. Digital Twins enable real-time monitoring and optimization of processes by creating virtual replicas of physical systems, with bidirectional data flow and constant synchronization. This paper explores the utilization of a Digital Twin in optimizing the performance of a physical system within a practical application developed in the Mind4Lab Lab of the Politecnico di Torino. The study examines how the use of a Digital Twin for dynamic scheduling and reallocation of tasks can effectively mitigate production problems and failures. The research is focused on the application of the Digital Twin for processes under changing conditions, as it addresses challenges related to failures and inefficiencies within a production system. The impacts of different dynamic scheduling approaches on mitigating failures are evaluated and compared against non-dynamic scheduling. The study includes an analysis of scenarios involving different reallocation strategies to optimize the system’s parameters (Throughput, Cycle Time, and Utilization). The work is structured into three main parts. Initially, the concepts and technologies behind Digital Twins are introduced, including their application in the industry. Secondly, a detailed Case Study is presented to demonstrate the implementation of the Digital Twin in managing a process in which a system of robots experiences time failures. Finally, an analysis of the performance metrics collected from the different scenarios is shown. The obtained results demonstrate that the use of the Digital Twin for dynamic scheduling improves the performance of the system by mitigating the effects of errors and time failures and optimizing the allocation of resources. The findings validate the effectiveness of Digital Twin technology in improving operational efficiency.