Adaptive Distributionally Robust Optimization

Dimitris Bertsimas (), Melvyn Sim () and Meilin Zhang ()
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Dimitris Bertsimas: Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Melvyn Sim: Department of Analytics and Operations Research, NUS Business School, National University of Singapore, Singapore 119077
Meilin Zhang: SUSS Business School, Singapore University of Social Science, Singapore 599494

Management Science, 2019, vol. 65, issue 2, 604-618

Abstract: We develop a modular and tractable framework for solving an adaptive distributionally robust linear optimization problem, where we minimize the worst-case expected cost over an ambiguity set of probability distributions. The adaptive distributionally robust optimization framework caters for dynamic decision making, where decisions adapt to the uncertain outcomes as they unfold in stages. For tractability considerations, we focus on a class of second-order conic (SOC) representable ambiguity set, though our results can easily be extended to more general conic representations. We show that the adaptive distributionally robust linear optimization problem can be formulated as a classical robust optimization problem. To obtain a tractable formulation, we approximate the adaptive distributionally robust optimization problem using linear decision rule (LDR) techniques. More interestingly, by incorporating the primary and auxiliary random variables of the lifted ambiguity set in the LDR approximation, we can significantly improve the solutions, and for a class of adaptive distributionally robust optimization problems, exact solutions can also be obtained. Using the new LDR approximation, we can transform the distributionally adaptive robust optimization problem to a classical robust optimization problem with an SOC representable uncertainty set. Finally, to demonstrate the potential for solving management decision problems, we develop an algebraic modeling package and illustrate how it can be used to facilitate modeling and obtain high-quality solutions for medical appointment scheduling and inventory management problems.

Keywords: dynamic programming; linear programming; nonlinear programming; quadratic; uncertainty; hospitals (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (39)

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