Tighter reformulations using classical Dawson and Sankoff bounds for approximating two-stage chance-constrained programs

Singh B (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Optimization Letters Springer Verlag (Germany)

DOI: 10.1007/s11590-020-01592-1

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.

Authors with CRIS profile

Bismark Singh Economics - Discrete Optimization - Mathematics (EDOM)

How to cite

APA:

Singh, B. (2020). Tighter reformulations using classical Dawson and Sankoff bounds for approximating two-stage chance-constrained programs. Optimization Letters. https://dx.doi.org/10.1007/s11590-020-01592-1

MLA:

Singh, Bismark. "Tighter reformulations using classical Dawson and Sankoff bounds for approximating two-stage chance-constrained programs." Optimization Letters (2020).

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