Mathematical Programming

, Volume 83, Issue 1, pp 425-450

First online:

A branch and bound method for stochastic global optimization

  • Vladimir I. NorkinAffiliated withInternational Institute for Applied Systems Analysis
  • , Georg Ch. PflugAffiliated withInternational Institute for Applied Systems Analysis
  • , Andrzej RuszczyńskiAffiliated withDepartment of Management Science and Information Systems, Rutgers University Email author 

Rent the article at a discount

Rent now

* Final gross prices may vary according to local VAT.

Get Access


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.


Stochastic programming Global optimization Branch and bound method Facility location