Partial sample average approximation method for chance constrained problems
AffiliationUniv Arizona, Dept Syst & Ind Engn
MetadataShow full item record
CitationCheng, J., Gicquel, C. & Lisser, A. Optim Lett (2019) 13: 657. https://doi.org/10.1007/s11590-018-1300-8
Rights© Springer-Verlag GmbH Germany, part of Springer Nature 2018.
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AbstractIn this paper, we present a new scheme of a sampling-based method to solve chance constrained programs. The main advantage of our approach is that the approximation problem contains only continuous variables whilst the standard sample average approximation (SAA) formulation contains binary variables. Although our approach generates new chance constraints, we show that such constraints are tractable under certain conditions. Moreover, we prove that the proposed approach has the same convergence properties as the SAA approach. Finally, numerical experiments show that the proposed approach outperforms the SAA approach on a set of tested instances.
Note12 month embargo; published online: 23 July 2018
VersionFinal accepted manuscript
SponsorsFMJH Program Gaspard Monge in Optimization and Operations Research"; EDF [2012-042H]; Science Foundation Arizona; Bisgrove Scholars program