On the role of bounds in stochastic linear programming
نویسنده
چکیده
Stochastic linear programming (SLP) models involve multivariate integrals. Although in the discretely distributed case these integrals become sums they typically contain a large amount of terms. The purpose of this paper is twofold: On the one hand we discuss the usage of bounds concerning integrals for constructing SLP algorithms and secondly we point out the role of bounds–based algorithms for solving SLP problems. The conceptual considerations are demonstrated in the last section by computational results. The tests have been carried out by utilizing SLP–IOR, our model management system for SLP.
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تاریخ انتشار 2002