From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization

We review and develop different tractable approximations to individual chance constrained problems in robust optimization on a varieties of uncertainty sets and show their interesting connections with bounds on the conditional-value-at-risk (CVaR) measure. We extend the idea to joint chance constrai...

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Main Authors: Chen, W., Sim, M., Sun, Jie, teo, C.
Format: Journal Article
Published: Institute for Operations Research and the Management Sciences (I N F O R M S) 2010
Online Access:http://hdl.handle.net/20.500.11937/11995
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author Chen, W.
Sim, M.
Sun, Jie
teo, C.
author_facet Chen, W.
Sim, M.
Sun, Jie
teo, C.
author_sort Chen, W.
building Curtin Institutional Repository
collection Online Access
description We review and develop different tractable approximations to individual chance constrained problems in robust optimization on a varieties of uncertainty sets and show their interesting connections with bounds on the conditional-value-at-risk (CVaR) measure. We extend the idea to joint chance constrained problems and provide a new formulation that improves upon the standard approach. Our approach builds on a classical worst case bound for order statistics problem and is applicable even if the constraints are correlated. We provide an application of the model on a network resource allocation problem with uncertain demand.
first_indexed 2025-11-14T06:57:22Z
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T06:57:22Z
publishDate 2010
publisher Institute for Operations Research and the Management Sciences (I N F O R M S)
recordtype eprints
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spelling curtin-20.500.11937-119952018-03-29T09:05:57Z From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization Chen, W. Sim, M. Sun, Jie teo, C. We review and develop different tractable approximations to individual chance constrained problems in robust optimization on a varieties of uncertainty sets and show their interesting connections with bounds on the conditional-value-at-risk (CVaR) measure. We extend the idea to joint chance constrained problems and provide a new formulation that improves upon the standard approach. Our approach builds on a classical worst case bound for order statistics problem and is applicable even if the constraints are correlated. We provide an application of the model on a network resource allocation problem with uncertain demand. 2010 Journal Article http://hdl.handle.net/20.500.11937/11995 10.1287/opre.1090.0712 Institute for Operations Research and the Management Sciences (I N F O R M S) restricted
spellingShingle Chen, W.
Sim, M.
Sun, Jie
teo, C.
From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization
title From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization
title_full From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization
title_fullStr From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization
title_full_unstemmed From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization
title_short From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization
title_sort from cvar to uncertainty set: implications in joint chance-constrained optimization
url http://hdl.handle.net/20.500.11937/11995