Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/289064 
Authors: 
Year of Publication: 
2020
Citation: 
[Journal:] Optimization Letters [ISSN:] 1862-4480 [Volume:] 15 [Issue:] 2 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2020 [Pages:] 327-336
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
We extend and improve recent results given by Singh and Watson on using classical bounds on the union of sets in a chance-constrained optimization problem. Specifically, we revisit the so-called Dawson and Sankoff bound that provided one of the best approximations of a chance constraint in the previous analysis. Next, we show that our work is a generalization of the previous work, and in fact the inequality employed previously is a very relaxed approximation with assumptions that do not generally hold. Computational results demonstrate on average over a 43% improvement in the bounds. As a byproduct, we provide an exact reformulation of the floor function in optimization models.
Subjects: 
Chance-constrained optimization
Bonferroni inequalities
Union bounds
Stochastic optimization
Floor function
Linearization
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.