SimPO: Simultaneous Prediction and Optimization
This addresses inefficiencies in integrated ML and optimization systems for decision-making, though it appears incremental as it builds on existing joint optimization ideas.
The paper tackles the problem of sub-optimal solutions from using predictive models separately from optimization in decision-making systems, proposing the SimPO framework that jointly optimizes prediction and optimization objectives end-to-end with gradient-based methods.
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.