Adjustable robust optimization: Theory, algorithm and applications

Jianzhe Zhen

Research output: ThesisDoctoral ThesisScientific

249 Downloads (Pure)

Abstract

Adjustable robust optimization is a methodology to help decision makers make robust and resilient decisions that extend well into the future. In this thesis, we exploit Fourier-Motzkin elimination to investigate the theories and applications of adjustable robust optimization. As a result, a generic technique is developed to enhance the classical approximation scheme, and applied to several applications, e.g., medical appointment scheduling, lot-sizing on a network, wireless sensor networks, to demonstrate the efficiency and effectiveness of the proposed approach. We further show how to formulate two generic optimization problems, i.e., computing the maximum volume inscribed ellipsoid in a polytopic projection, and finding centered solutions for uncertain linear equations, as adjustable robust optimization problems, and investigate these two problems through the lens of Fourier-Motzkin elimination.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Tilburg University
Supervisors/Advisors
  • den Hertog, Dick, Promotor
  • Sim, M., Promotor, External person
Award date9 Apr 2018
Place of PublicationTilburg
Publisher
Print ISBNs978 90 5668 557 7
Publication statusPublished - 2018

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Medical applications
Linear equations
Wireless sensor networks
Lenses
Scheduling

Cite this

Zhen, J. (2018). Adjustable robust optimization: Theory, algorithm and applications. Tilburg: CentER, Center for Economic Research.
Zhen, Jianzhe. / Adjustable robust optimization : Theory, algorithm and applications. Tilburg : CentER, Center for Economic Research, 2018. 136 p.
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title = "Adjustable robust optimization: Theory, algorithm and applications",
abstract = "Adjustable robust optimization is a methodology to help decision makers make robust and resilient decisions that extend well into the future. In this thesis, we exploit Fourier-Motzkin elimination to investigate the theories and applications of adjustable robust optimization. As a result, a generic technique is developed to enhance the classical approximation scheme, and applied to several applications, e.g., medical appointment scheduling, lot-sizing on a network, wireless sensor networks, to demonstrate the efficiency and effectiveness of the proposed approach. We further show how to formulate two generic optimization problems, i.e., computing the maximum volume inscribed ellipsoid in a polytopic projection, and finding centered solutions for uncertain linear equations, as adjustable robust optimization problems, and investigate these two problems through the lens of Fourier-Motzkin elimination.",
author = "Jianzhe Zhen",
note = "Series: CentER Dissertation Series Volume: 556",
year = "2018",
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series = "CentER Dissertation Series",
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Zhen, J 2018, 'Adjustable robust optimization: Theory, algorithm and applications', Doctor of Philosophy, Tilburg University, Tilburg.

Adjustable robust optimization : Theory, algorithm and applications. / Zhen, Jianzhe.

Tilburg : CentER, Center for Economic Research, 2018. 136 p.

Research output: ThesisDoctoral ThesisScientific

TY - THES

T1 - Adjustable robust optimization

T2 - Theory, algorithm and applications

AU - Zhen, Jianzhe

N1 - Series: CentER Dissertation Series Volume: 556

PY - 2018

Y1 - 2018

N2 - Adjustable robust optimization is a methodology to help decision makers make robust and resilient decisions that extend well into the future. In this thesis, we exploit Fourier-Motzkin elimination to investigate the theories and applications of adjustable robust optimization. As a result, a generic technique is developed to enhance the classical approximation scheme, and applied to several applications, e.g., medical appointment scheduling, lot-sizing on a network, wireless sensor networks, to demonstrate the efficiency and effectiveness of the proposed approach. We further show how to formulate two generic optimization problems, i.e., computing the maximum volume inscribed ellipsoid in a polytopic projection, and finding centered solutions for uncertain linear equations, as adjustable robust optimization problems, and investigate these two problems through the lens of Fourier-Motzkin elimination.

AB - Adjustable robust optimization is a methodology to help decision makers make robust and resilient decisions that extend well into the future. In this thesis, we exploit Fourier-Motzkin elimination to investigate the theories and applications of adjustable robust optimization. As a result, a generic technique is developed to enhance the classical approximation scheme, and applied to several applications, e.g., medical appointment scheduling, lot-sizing on a network, wireless sensor networks, to demonstrate the efficiency and effectiveness of the proposed approach. We further show how to formulate two generic optimization problems, i.e., computing the maximum volume inscribed ellipsoid in a polytopic projection, and finding centered solutions for uncertain linear equations, as adjustable robust optimization problems, and investigate these two problems through the lens of Fourier-Motzkin elimination.

M3 - Doctoral Thesis

SN - 978 90 5668 557 7

T3 - CentER Dissertation Series

PB - CentER, Center for Economic Research

CY - Tilburg

ER -

Zhen J. Adjustable robust optimization: Theory, algorithm and applications. Tilburg: CentER, Center for Economic Research, 2018. 136 p. (CentER Dissertation Series).