A branch and bound method for stochastic global optimization
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A stochastic branch and bound method for solving stochastic global optimization problems is proposed. As in the deterministic case, the feasible set is partitioned into compact subsets. To guide the partitioning process the method uses stochastic upper and lower estimates of the optimal value of the objective function in each subset. Convergence of the method is proved and random accuracy estimates derived. Methods for constructing stochastic upper and lower bounds are discussed. The theoretical considerations are illustrated with an example of a facility location problem.
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- A branch and bound method for stochastic global optimization
Volume 83, Issue 1-3 , pp 425-450
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- Stochastic programming
- Global optimization
- Branch and bound method
- Facility location
- Industry Sectors
- Author Affiliations
- 1. International Institute for Applied Systems Analysis, 2361, Laxenburg, Austria
- 2. Department of Management Science and Information Systems, Rutgers University, Janice H. Levin Building, 94 Rockafeller Rd., 08854, Piscataway, NJ, USA