A Branch and Bound Method for Stochastic Global Optimization
A stochastic version of the branch and bound method is proposed for solving stochastic global optimization problems. The method, instead of deterministic bounds, uses stochastic upper and lower estimates of the optimal value of subproblems, to guide the partitioning process. Almost sure convergence of the method is proved and random accuracy estimates derived. Methods for constructing random bounds for stochastic global optimization problems are discussed. The theoretical considerations are illustrated with an example of a facility location problem.
|Date of creation:||Jun 1996|
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- labbe, M. & Peeters, D. & Thisse, J.F., 1992.
"Location on Networks,"
9216, Universite Libre de Bruxelles - C.E.M.E..
- LABBE, Martine & PEETERS, Dominique & THISSE, Jacques-François, 1993. "Location on Networks," CORE Discussion Papers 1993040, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- John R. Birge, 1985. "Decomposition and Partitioning Methods for Multistage Stochastic Linear Programs," Operations Research, INFORMS, vol. 33(5), pages 989-1007, October.
- V.I. Norkin & Y.M. Ermoliev & A. Ruszczynski, 1994. "On Optimal Allocation of Indivisibles Under Uncertainty," Working Papers wp94021, International Institute for Applied Systems Analysis. Full references (including those not matched with items on IDEAS)
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