  Many machine learning (ML) models are integrated within the context of a
larger system as part of a key component for decision making processes.
Concretely, predictive models are often employed in estimating the parameters
for the input values that are utilized for optimization models as isolated
processes. Traditionally, the predictive models are built first, then the model
outputs are used to generate decision values separately. However, it is often
the case that the prediction values that are trained independently of the
optimization process produce sub-optimal solutions. In this paper, we propose a
formulation for the Simultaneous Prediction and Optimization (SimPO) framework.
This framework introduces the use of a joint weighted loss of a decision-driven
predictive ML model and an optimization objective function, which is optimized
end-to-end directly through gradient-based methods.
