Discrete Event Simulation Using Distributional Random Forests to Model Event Outcomes

In discrete event simulation (DES), the events are random (aleatory) and typically represented by a probability distribution that fits the real phenomena that is studied. The true distributions of event outcomes, which may be multivariate, are often dependent on the values of covariates and this relationship may be complex. Due to difficulties in representing the influence of covariates within DES models, often only the averaged distribution or expected value of the conditional distribution is used. However, this can reduce modelling accuracy and prevent the model from being used to study the influence of covariates. Distributional random forests (DRF) are a machine learning technique for predicting the multivariate conditional distribution of an outcome from the values of covariates using an ensemble of decision trees. In this paper, the benefits of utilizing DRF in DES are explored through comparison with alternative approaches in a model of a powder coating industrial process.