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 variety 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-constraine...
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| Veröffentlicht in: | Operations research 2010-03, Vol.58 (2), p.470-485 |
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| container_title | Operations research |
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| creator | Chen, Wenqing Sim, Melvyn Sun, Jie Teo, Chung-Piaw |
| description | We review and develop different tractable approximations to individual chance-constrained problems in robust optimization on a variety 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 problems 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. |
| doi_str_mv | 10.1287/opre.1090.0712 |
| format | Article |
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| ispartof | Operations research, 2010-03, Vol.58 (2), p.470-485 |
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| language | eng |
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| source | Informs; EBSCOhost Business Source Complete; JSTOR Archive Collection A-Z Listing |
| subjects | application Approximation Decision analysis Decision-making Investment analysis Iterative solutions Mathematical functions Methods nonlinear Optimal solutions Optimization Probabilities probability programming Random variables Randomness Resource allocation risk Robust optimization stochastic Stochastic models Studies Transshipment Uncertainty |
| title | From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization |
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