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The Decision Rule Approach to Optimization under Uncertainty: Methodology and Applications

Angelos Georghiou (angelos.georghiou***at***mcgill.ca)
Daniel Kuhn (daniel.kuhn***at***epfl.ch)
Wolfram Wiesemann (wwiesema***at***imperial.ac.uk)

Abstract: Dynamic decision-making under uncertainty has a long and distinguished history in operations research. Due to the curse of dimensionality, solution schemes that naively 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.

Category 1: Robust Optimization

Category 2: Applications -- OR and Management Sciences

Citation:

Download: [PDF]

Entry Submitted: 12/21/2011
Entry Accepted: 12/21/2011
Entry Last Modified: 11/14/2018

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