  Techniques to reduce the energy burden of an Industry 4.0 ecosystem often
require solving a multiobjective optimization problem. However, collecting
experimental data can often be either expensive or time-consuming. In such
cases, statistical methods can be helpful. This article proposes Pareto Front
(PF) and Pareto Set (PS) estimation methods using Bayesian Additive Regression
Trees (BART), which is a non-parametric model whose assumptions are typically
less restrictive than popular alternatives, such as Gaussian Processes. The
performance of our BART-based method is compared to a GP-based method using
analytic test functions, demonstrating convincing advantages. Finally, our
BART-based methodology is applied to a motivating Industry 4.0 engineering
problem.
