A Data-Driven Approach to Multistage Stochastic Linear Optimization

Dimitris Bertsimas (), Shimrit Shtern () and Bradley Sturt ()
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Dimitris Bertsimas: Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Shimrit Shtern: The Faculty of Industrial Engineering and Management, Technion?Israel Institute of Technology, Haifa 3200003, Israel
Bradley Sturt: Department of Information and Decision Sciences, University of Illinois at Chicago, Chicago, Illinois 60607

Management Science, 2023, vol. 69, issue 1, 51-74

Abstract: We propose a new data-driven approach for addressing multistage stochastic linear optimization problems with unknown distributions. The approach consists of solving a robust optimization problem that is constructed from sample paths of the underlying stochastic process. We provide asymptotic bounds on the gap between the optimal costs of the robust optimization problem and the underlying stochastic problem as more sample paths are obtained, and we characterize cases in which this gap is equal to zero. To the best of our knowledge, this is the first sample path approach for multistage stochastic linear optimization that offers asymptotic optimality guarantees when uncertainty is arbitrarily correlated across time. Finally, we develop approximation algorithms for the proposed approach by extending techniques from the robust optimization literature and demonstrate their practical value through numerical experiments on stylized data-driven inventory management problems.

Keywords: stochastic programming; robust optimization; sample-path approximations (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:69:y:2023:i:1:p:51-74

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