Approximating two-stage chance-constrained programs with classical probability bounds

Singh B, Watson JP (2019)


Publication Type: Journal article

Publication year: 2019

Journal

Book Volume: 13

Pages Range: 1403-1416

Journal Issue: 6

DOI: 10.1007/s11590-019-01387-z

Abstract

We consider a joint-chance constraint (JCC) as a union of sets, and approximate this union using bounds from classical probability theory. When these bounds are used in an optimization model constrained by the JCC, we obtain corresponding upper and lower bounds on the optimal objective function value. We compare the strength of these bounds against each other under two different sampling schemes, and observe that a larger correlation between the uncertainties tends to result in more computationally challenging optimization models. We also observe the same set of inequalities to provide the tightest upper and lower bounds in our computational experiments.

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APA:

Singh, B., & Watson, J.-P. (2019). Approximating two-stage chance-constrained programs with classical probability bounds. Optimization Letters, 13(6), 1403-1416. https://dx.doi.org/10.1007/s11590-019-01387-z

MLA:

Singh, Bismark, and Jean-Paul Watson. "Approximating two-stage chance-constrained programs with classical probability bounds." Optimization Letters 13.6 (2019): 1403-1416.

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