The decision rule approach to optimization under uncertainty: methodology and applications

Angelos Georghiou, Daniel Kuhn () and Wolfram Wiesemann
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Angelos Georghiou: McGill University
Daniel Kuhn: Risk Analytics and Optimization Chair, EPFL
Wolfram Wiesemann: Imperial College London

Computational Management Science, 2019, vol. 16, issue 4, No 2, 545-576

Abstract: Abstract Dynamic decision-making under uncertainty has a long and distinguished history in operations research. Due to the curse of dimensionality, solution schemes that naïvely partition or discretize the support of the random problem parameters are limited to small and medium-sized problems, or they require restrictive modeling assumptions (e.g., absence of recourse actions). In the last few decades, several solution techniques have been proposed that aim to alleviate the curse of dimensionality. Amongst these is the decision rule approach, which faithfully models the random process and instead approximates the feasible region of the decision problem. In this paper, we survey the major theoretical findings relating to this approach, and we investigate its potential in two applications areas.

Keywords: Robust optimization; Stochastic programming; Decision rules; Optimization under uncertainty (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (10)

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DOI: 10.1007/s10287-018-0338-5

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