Abstract: An online simulation model optimization receives data representative of a business process captured in real time to form instance metrics, aggregates the instance metrics to form aggregated instance metrics, and uses a particle filter for filtering the aggregated instance metrics to form calibrated data. The process iteratively computes an output value using the calibrated data, by a simulation model. Responsive to a determination that the output value is not within a predetermined tolerance of an error threshold, the process adjusts a weight previously assigned to an aggregated instance metric by the particle filter to form recalibrated data, whereby the recalibrated data is submitted to the simulation model for computation. Responsive to a determination that the output value is within the predetermined tolerance, the process sends a result to a correction selection process of a business process optimizer, the result comprising the output value, the calibrated data, and/or the recalibrated data.