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

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Title: The decision rule approach to optimization under uncertainty: methodology and applications
Authors: Georghiou, A
Kuhn, D
Wiesemann, W
Item Type: Journal Article
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.
Issue Date: 1-Oct-2019
Date of Acceptance: 15-Nov-2018
URI: http://hdl.handle.net/10044/1/77453
DOI: 10.1007/s10287-018-0338-5
ISSN: 1619-697X
Publisher: Springer (part of Springer Nature)
Start Page: 545
End Page: 576
Journal / Book Title: Computational Management Science
Volume: 16
Issue: 4
Copyright Statement: © Springer-Verlag GmbH Germany, part of Springer Nature 2018. The final publication is available at Springer via https://link.springer.com/article/10.1007%2Fs10287-018-0338-5
Sponsor/Funder: Engineering & Physical Science Research Council (E
Funder's Grant Number: EP/M028240/1
Keywords: Social Sciences
Social Sciences, Mathematical Methods
Mathematical Methods In Social Sciences
Robust optimization
Stochastic programming
Decision rules
Optimization under uncertainty
ADJUSTABLE ROBUST OPTIMIZATION
FACILITY LOCATION
FINITE ADAPTABILITY
DESIGN
GENERATION
DUALITY
POWER
Social Sciences
Social Sciences, Mathematical Methods
Mathematical Methods In Social Sciences
Robust optimization
Stochastic programming
Decision rules
Optimization under uncertainty
ADJUSTABLE ROBUST OPTIMIZATION
FACILITY LOCATION
FINITE ADAPTABILITY
DESIGN
GENERATION
DUALITY
POWER
0102 Applied Mathematics
0103 Numerical and Computational Mathematics
1503 Business and Management
Operations Research
Publication Status: Published
Online Publication Date: 2018-11-26
Appears in Collections:Imperial College Business School