End-to-end Simultaneous Prediction and Optimization Framework

Abstract

Many predictive machine learning (ML) models are integrated within the context of a larger system as part of a key component for decision making processes. Concretely, the 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.