Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/289064 
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Erscheinungsjahr: 
2020
Quellenangabe: 
[Journal:] Optimization Letters [ISSN:] 1862-4480 [Volume:] 15 [Issue:] 2 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2020 [Pages:] 327-336
Verlag: 
Springer, Berlin, Heidelberg
Zusammenfassung: 
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.
Schlagwörter: 
Chance-constrained optimization
Bonferroni inequalities
Union bounds
Stochastic optimization
Floor function
Linearization
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