From Predictive to Prescriptive Analytics

Dimitris Bertsimas
Dimitris Bertsimas
http://orcid.org/0000-0002-1985-1003
Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139;
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,
Nathan Kallus
Corresponding Author
Nathan Kallus
http://orcid.org/0000-0003-1672-0507
Cornell Tech and School of Operations Research and Information Engineering, Cornell University, New York, New York 10044
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Dimitris Bertsimas

Dimitris Bertsimas

http://orcid.org/0000-0002-1985-1003

Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139;

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,

Corresponding Author

Nathan Kallus

http://orcid.org/0000-0003-1672-0507

Cornell Tech and School of Operations Research and Information Engineering, Cornell University, New York, New York 10044

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Published Online:23 Aug 2019https://doi.org/10.1287/mnsc.2018.3253

Supplemental Material

mnsc.2018.3253.sm1.pdf

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- Query-Decision Regression Between Shortest Path and Minimum Steiner Tree
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- Tutorials in Operations Research: Advancing the Frontiers of OR/MS: From Methodologies to Applications
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- Integrating data analytics in teaching audit with machine learning and artificial intelligence
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- Analytics Applications in Fashion Supply Chain Management—A Review of Literature and Practice
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- Prescriptive analytics for a multi-shift staffing problem
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- Redundancy allocation under state‐dependent distributional uncertainty of component lifetimes
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- A Practical End-to-End Inventory Management Model with Deep Learning
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- The Intersection of Machine Learning with Forecasting and Optimisation: Theory and Applications
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- Dynamic optimization with side information
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- Gradient boosting for convex cone predict and optimize problems
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- Quality vs. Quantity of Data in Contextual Decision-Making: Exact Analysis under Newsvendor Loss
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- The Benefits and Limitations of ChatGPT in Business Education and Research: A Focus on Management Science, Operations Management and Data Analytics
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- Personalized Incentive for Active Label Acquisition in Assortment Optimization
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- Distributionally Robust Multi-Product Newsvendors: A Shadow-Price Perspective
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- Optimize-via-Predict: Realizing out-of-sample optimality in data-driven optimization
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- A Machine Learning Approach to Solving Large Bilevel and Stochastic Programs: Application to Cycling Network Design
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- Stochastic optimization model for ship inspection planning under uncertainty in maritime transportation
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- “Predict, then optimize” with quantile regression: A global method from predictive to prescriptive analytics and applications to multimodal transportation
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- Smooth Contextual Bandits: Bridging the Parametric and Nondifferentiable Regret Regimes
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- Prescriptive Trees for Integrated Forecasting and Optimization Applied in Trading of Renewable Energy
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- Smart urban transport and logistics: A business analytics perspective
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- Prescriptive Analytics in Urban Policing Operations
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- Data Aggregation and Demand Prediction
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- Optimal Order Batching in Warehouse Management: A Data-Driven Robust Approach
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- Online Resource Allocation with Personalized Learning
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- Bringing artificial intelligence to business management
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- Feature-Driven Economic Improvement for Network-Constrained Unit Commitment: A Closed-Loop Predict-and-Optimize Framework
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  IEEE Transactions on Power Systems, Vol. 37, No. 4
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- Fast Rates for Contextual Linear Optimization
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- Data‐driven storage operations: Cross‐commodity backtest and structured policies
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- Predictive and Descriptive Analysis for Healthcare Data
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- Coupled Learning Enabled Stochastic Programming with Endogenous Uncertainty
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  European Journal of Operational Research, Vol. 298, No. 3
- Applications of machine learning methods in port operations – A systematic literature review
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cover image Management Science

Volume 66, Issue 3

March 2020

Pages v, 1005-1507, iii-iv

Article Information

Supplemental Material

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Information

Received:August 12, 2015
Accepted:November 13, 2018
Published Online:August 23, 2019

Copyright © 2019, INFORMS

Cite as

Dimitris Bertsimas, Nathan Kallus (2019) From Predictive to Prescriptive Analytics. Management Science 66(3):1025-1044.

https://doi.org/10.1287/mnsc.2018.3253

Keywords