JANOS: An Integrated Predictive and Prescriptive Modeling Framework

David Bergman (), Teng Huang (), Philip Brooks (), Andrea Lodi () and Arvind U. Raghunathan ()
Additional contact information
David Bergman: Department of Operations and Information Management, School of Business, University of Connecticut, Storrs, Connecticut 06268
Teng Huang: Lingnan (University) College, Sun Yat-sen University, Guangzhou 510275, China
Philip Brooks: Optimized Operations, LLC, Boston, Massachusetts 02116
Andrea Lodi: CERC and MAGI, Polytechnique Montréal, Montréal, Québec H3C 3A7, Canada
Arvind U. Raghunathan: Mitsubishi Electric Research Laboratories, Cambridge, Massachusetts 02139

INFORMS Journal on Computing, 2022, vol. 34, issue 2, 807-816

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, allowing researchers and practitioners to embed machine learning models in an end-to-end 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 enable the specification of commonly used predictive models within an optimization model, have the features of the predictive model as variables in the optimization model, and incorporate the output of the predictive models as part of the objective. The framework considers two sets of decision variables: regular and predicted . The relationship between the regular and the predicted variables is specified by the user as pretrained predictive models. JANOS currently supports linear regression, logistic regression, and neural network with rectified linear activation functions. 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. Summary of Contribution. This paper describes a new software tool, JANOS, that integrates predictive modeling and discrete optimization to assist decision making. Specifically, the proposed solver takes as input user-specified pretrained predictive models and formulates optimization models directly over those predictive models by embedding them within an optimization model through linear transformations.

Keywords: predictive modeling; prescriptive analysis; discrete optimization; solver (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:34:y:2022:i:2:p:807-816

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