A survey of adjustable robust optimization

Name: A survey of adjustable robust optimization
Author: Yanıkoğlu, İhsan, Gorissen, B. L., Hertog, D. den

İsim	A survey of adjustable robust optimization
Yazar	Yanıkoğlu, İhsan, Gorissen, B. L., Hertog, D. den
Basım Tarihi:	2019-09
Basım Yeri	- Elsevier
Konu	Semi-infinite programming, Robust optimization, Adjustable robust optimization, Multistage decision making
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0377-2217
Kayıt Numarası	f0fa7b05-3c9e-4f10-a9ea-f88a075a5722
Lokasyon	Industrial Engineering
Tarih	2019-09
Örnek Metin	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.
DOI	10.1016/j.ejor.2018.08.031
Cilt	277

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

A survey of adjustable robust optimization

Yazar Yanıkoğlu, İhsan, Gorissen, B. L., Hertog, D. den

Basım Tarihi 2019-09

Basım Yeri - Elsevier

Konu Semi-infinite programming, Robust optimization, Adjustable robust optimization, Multistage decision making

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0377-2217

Kayıt Numarası f0fa7b05-3c9e-4f10-a9ea-f88a075a5722

Lokasyon Industrial Engineering

Tarih 2019-09

Örnek Metin 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.

DOI 10.1016/j.ejor.2018.08.031

Cilt 277