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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