  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.
