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Empirical stochastic branch-and-bound for optimization via simulation

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2 Author(s)
Xu, W.L. ; Dept. of Ind. Eng. & Manage. Sci., Northwestern Univ., Evanston, IL, USA ; Nelson, B.L.

We introduce a new method for discrete-decision-variable optimization via simulation that combines the stochastic branch-and-bound method and the nested partitions method in the sense that we take advantage of the partitioning structure of stochastic branch and bound, but estimate the bounds based on the performance of sampled solutions as the nested partitions method does. Our Empirical Stochastic Branch-and-Bound algorithm also uses improvement bounds to guide solution sampling for better performance.

Published in:

Simulation Conference (WSC), Proceedings of the 2010 Winter

Date of Conference:

5-8 Dec. 2010