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Scenario Clustering in a Progressive Hedging-Based MetaHeuristic for Stochastic Network Design

We present a technique for enhancing a progressive hedging-based metaheuristic for a network design problem that models demand uncertainty with scenarios. The technique uses machine learning methods to cluster scenarios and, subsequently, the metaheuristic repeatedly solves multi-scenario subproblems. With an extensive computational study we see that solving multi-scenario subproblems leads to a significant increase in solution quality and that how you construct these multi-scenario subproblems directly impacts solution quality.