A survey of adjustable robust optimization

•Surveys the literature on applications of adjustable robust optimization.•Surveys theoretical and methodological aspects of adjustable robust optimization.•Guides practitioners on how to apply adjustable robust optimization methods.•Presents the advantages and limitations of the adjustable robust o...

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Bibliographic Details
Published in:	European journal of operational research Vol. 277; no. 3; pp. 799 - 813
Main Authors:	Yanıkoğlu, İhsan, Gorissen, Bram L., den Hertog, Dick
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 19.09.2019
Subjects:	Adjustable robust optimization Multistage decision making Robust optimization Semi-infinite programming Semi-infinite programming Adjustable robust optimization Robust optimization Multistage decision making
ISSN:	0377-2217, 1872-6860
Online Access:	Get full text
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Summary:	•Surveys the literature on applications of adjustable robust optimization.•Surveys theoretical and methodological aspects of adjustable robust optimization.•Guides practitioners on how to apply adjustable robust optimization methods.•Presents the advantages and limitations of the adjustable robust optimization. Static robust optimization (RO) is a methodology to solve mathematical optimization problems with uncertain data. The objective of static RO is to find solutions that are immune to all perturbations of the data in a so-called uncertainty set. RO is popular because it is a computationally tractable methodology and has a wide range of applications in practice. Adjustable robust optimization (ARO), on the other hand, is a branch of RO where some of the decision variables can be adjusted after some portion of the uncertain data reveals itself. ARO generally yields a better objective function value than that in static robust optimization because it gives rise to more flexible adjustable (or wait-and-see) decisions. Additionally, ARO also has many real life applications and is a computationally tractable methodology for many parameterized adjustable decision variables and uncertainty sets. This paper surveys the state-of-the-art literature on applications and theoretical/methodological aspects of ARO. Moreover, it provides a tutorial and a road map to guide researchers and practitioners on how to apply ARO methods, as well as, the advantages and limitations of the associated methods.
ISSN:	0377-2217 1872-6860
DOI:	10.1016/j.ejor.2018.08.031