Utilizing Digital Twins to Model and Optimize Hydraulic Excavator Operator Performance Through Arena Simulation

Abstract This study aims to provide a digital twin tool for mining and construction companies to evaluate the effect of operator behaviors on the truck-loading process, focusing on swing and bucket rotation. While equipment performance estimators are available, there are few tools to assess the influence of operators. The aim is to create a discrete event simulation (DES) model in Arena® to evaluate how variations in swing and bucket angles affect hydraulic excavator production rates. We validated the DES model using real-world data from a case study to assess the differences in behavior among four operators. Results showed that Operator 1 was the most efficient, with a production rate of 78.2 tons/min. Operators 3 and 4 had lower rates of 58.0 and 55.4 tons/min, respectively, despite having shorter cycle times than Operator 2. These results emphasize that the simulator can elucidate the balance between loading time and payloads.