Transforming Discrete Event Models To Machine Learning Models

Discrete event simulation, formalized as deductive modeling, has been shown to be effective for studying dynamical systems. Development of models, however, is challenging when numerous interacting components are involved and should operate under different conditions. Machine Learning (ML) holds the promise to help reduce the effort needed to develop models. Toward this goal, a collection of ML algorithms, including Automatic Relevance Determination is used. Parallel Discrete Event System Specification (PDEVS) models are developed for Single-stage and Two-stage cascade factories. Each model is simulated under different demand profiles. The simulated data sets are partitioned into subsets, each for one or more model components. The ML algorithms are applied to the data sets for generating models. The throughputs predicted by the ML models closely match those in the PDEVS simulated data. This study contributes to modeling by demonstrating the potential benefits and complications of utilizing ML for discrete-event systems.