Document Type
Article
Publication Date
2-2015
Publication Source
Stochastic Programming E-print Series
Abstract
We solve the chance constrained optimization with convex feasible set through approximating the chance constraint by another convex smooth function. The approximation is based on the numerical properties of the Bernstein polynomial that is capable of effectively controlling the approximation error for both function value and gradient. Thus, we adopt a first-order algorithm to reach a satisfactory solution which is expected to be optimal. When the explicit expression of joint distribution is not available, we then use Monte Carlo approach to numerically evaluate the chance constraint to obtain an optimal solution by probability. Numerical results for known problem instances are presented.
Document Version
Preprint
Copyright
Copyright © 2015, Lijian Chen.
Publisher
Stochastic Programming Society
Volume
2015
Issue
1
Place of Publication
Berlin, Germany
Peer Reviewed
yes
eCommons Citation
Chan, Lijian, "A Simulation-based Approach to Solve a Specific Type of Chance Constrained Optimization" (2015). MIS/OM/DS Faculty Publications. 8.
https://ecommons.udayton.edu/mis_fac_pub/8
Included in
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Comments
The document available for download is the pre-reviewed and pre-edited author's accepted manuscript, provided in compliance with the publisher's policy on self-archiving. The version of record may contain differences that have come about after the copy editing and layout processes.
Permission documentation is on file.