Scenarios and policy aggregation in optimization under uncertainty
Mathematics of Operations Research
Journal of Global Optimization
Risk Aversion via Excess Probabilities in Stochastic Programs with Mixed-Integer Recourse
SIAM Journal on Optimization
Applications of Stochastic Programming (Mps-Siam Series on Optimization) (Mps-Saimseries on Optimization)
The Million-Variable "March" for Stochastic Combinatorial Optimization
Journal of Global Optimization
On a stochastic sequencing and scheduling problem
Computers and Operations Research
On stochastic dynamic programming for solving large-scale planning problems under uncertainty
Computers and Operations Research
A general algorithm for solving two-stage stochastic mixed 0-1 first-stage problems
Computers and Operations Research
Computers and Operations Research
A branch-and-cluster coordination scheme for selecting prison facility sites under uncertainty
Computers and Operations Research
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In the branch-and-fix coordination (BFC-MSMIP) algorithm for solving large-scale multistage stochastic mixed integer programming problems, we find it crucial to decide the stages where the nonanticipativity constraints are explicitly considered in the model. This information is materialized when the full model is broken down into a scenario cluster partition with smaller subproblems. In this paper we present a scheme for obtaining strong bounds and branching strategies for the Twin Node Families to increase the efficiency of the procedure BFC-MSMIP, based on the information provided by the nonanticipativity constraints that are explicitly considered in the problem. Some computational experience is reported to support the efficiency of the new scheme.