Abstract
As the second type of stochastic programming developed by Charnes and Cooper [41], chance-constrained programming (CCP) offers a powerful means of modeling stochastic decision systems with assumption that the stochastic constraints will hold at least a of time, where a is referred to as the confidence level provided as an appropriate safety margin by the decision-maker.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Liu, B. (2002). Stochastic Chance-Constrained Programming. In: Theory and Practice of Uncertain Programming. Studies in Fuzziness and Soft Computing, vol 102. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1781-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1781-2_6
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-662-13196-1
Online ISBN: 978-3-7908-1781-2
eBook Packages: Springer Book Archive