Integration Of Learning Curves In Digital Twins For Dynamic Assembly Line Re-Balancing

Assembly lines often face variability in operator performance, causing inefficiencies that can lead to prolonged throughput times and suboptimal resource utilization. This paper explores how Digital Twins (DT) integrated within a Decision Support System (DSS) can address these issues by simulating and re-balancing assembly lines in real-time. Real-time data is used to update DT predictions including learning effects to generate alternative solutions in terms of task allocation. Numerical experiments on a simplified assembly line structure show consistent reductions in cycle time when re-balancing and adjusting re-balancing intervals.