A branch and bound method for stochastic global optimization

被引：180
|
作者：
Norkin, VI
Pflug, GC
Ruszczynski, A
机构：
[1] Rutgers State Univ, Dept Management Sci & Informat Syst, Piscataway, NJ 08854 USA
[2] Int Inst Appl Syst Anal, A-2361 Laxenburg, Austria
来源：
MATHEMATICAL PROGRAMMING | 1998年 / 83卷 / 03期
关键词：
stochastic programming; global optimization; branch and bound method; facility location;
D O I：
10.1007/BF02680569
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
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. (C) 1998 The Mathematical Programming Society, Inc. Published by Elsevier Science B.V.
引用
收藏
页码：425 / 450
页数：26
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