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Approximating Two-Stage Chance-Constrained Programs with Classical Probability Bounds

Optimization Letters

Singh, Bismark; Watson, Jean-Paul

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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TYPE Journal Article YEAR 2018
DOIOSTI
Results 2551–2600 of 9,998
Results 2551–2600 of 9,998
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