JANOS: An Integrated Predictive and Prescriptive Modeling Framework
This provides a novel tool for researchers and practitioners in business analytics to combine predictive and prescriptive modeling, though it is incremental as it builds on existing methods.
The authors tackled the integration of predictive modeling and prescriptive analysis by developing JANOS, a framework that embeds machine learning models into optimization, and demonstrated its flexibility with a scholarship allocation example and numeric evaluation.
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.