JANOS: An Integrated Predictive and Prescriptive Modeling Framework

TL;DR

JANOS is a novel framework that integrates predictive models directly into optimization problems, enabling seamless combination of machine learning and prescriptive analytics for practical decision-making.

Contribution

It introduces modeling constructs that embed pre-trained predictive models into optimization frameworks, supporting linear, logistic regression, and neural networks.

Findings

Demonstrated flexibility with a scholarship allocation example

Provided numeric performance evaluation

Supports multiple predictive model types

Abstract

Business research practice is witnessing a surge in the integration of predictive modeling and prescriptive analysis. We describe a modeling framework JANOS that seamlessly integrates the two streams of analytics, for the first time allowing researchers and practitioners to embed machine learning models in an optimization framework. JANOS allows for specifying a prescriptive model using standard optimization modeling elements such as constraints and variables. The key novelty lies in providing modeling constructs that allow for the specification of commonly used predictive models and their features as constraints and variables in the optimization model. The framework considers two sets of decision variables; regular and predicted. The relationship between the regular and the predicted variables are specified by the user as pre-trained predictive models. JANOS currently supports linear regression, logistic regression, and neural network with rectified linear activation functions, but we plan to expand on this set in the future. In this paper, we demonstrate the flexibility of the framework through an example on scholarship allocation in a student enrollment problem and provide a numeric performance evaluation.