Methodology for Incorporating Stochastic Factors into the Simulation Model of a Robotic Production Cell

This article presents a methodological approach to improving the reliability of simulation results for robotic production systems by incorporating uncertainty factors and random events into the model. The relevance of the study stems from the need to obtain realistic performance forecasts, which are not achievable using traditional deterministic models that neglect equipment failures, process variability, and external disturbances. The proposed approach includes the classifi cation of stochastic factors, the application of the Monte Carlo method for probabilistic modeling, and the implementation of algorithms in Python within the R-Pro simulation environment. The results demonstrate the feasibility of building simulation models that replicate not only nominal technological workfl ows but also random failures and process disruptions. This enables more accurate throughput analysis, identifi cation of bottlenecks, and justifi cation of design decisions under conditions of uncertainty.