Scenarios and policy aggregation in optimization under uncertainty
Mathematics of Operations Research
A Heuristic for Moment-Matching Scenario Generation
Computational Optimization and Applications
Journal of Global Optimization
Decision Making Under Uncertainty: Is Sensitivity Analysis of Any Use?
Operations Research
Capacitated Network Design with Uncertain Demand
INFORMS Journal on Computing
Generating Scenario Trees for Multistage Decision Problems
Management Science
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
A Study of Demand Stochasticity in Service Network Design
Transportation Science
Machine Learning: An Algorithmic Perspective
Machine Learning: An Algorithmic Perspective
Computers and Operations Research
Introduction to Stochastic Programming
Introduction to Stochastic Programming
A note on scenario reduction for two-stage stochastic programs
Operations Research Letters
Scenario Cluster Decomposition of the Lagrangian dual in two-stage stochastic mixed 0-1 optimization
Computers and Operations Research
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We propose a methodological approach to build strategies for grouping scenarios as defined by the type of scenario decomposition, type of grouping, and the measures specifying scenario similarity. We evaluate these strategies in the context of stochastic network design by analyzing the behavior and performance of a new progressive hedging-based meta-heuristic for stochastic network design that solves subproblems comprising multiple scenarios. We compare the proposed strategies not only among themselves, but also against the strategy of grouping scenarios randomly and the lower bound provided by a state-of-the-art MIP solver. The results show that, by solving multi-scenario subproblems generated by the strategies we propose, the meta-heuristic produces better results in terms of solution quality and computing efficiency than when either single-scenario subproblems or multiple-scenario subproblems that are generated by picking scenarios at random are solved. The results also show that, considering all the strategies tested, the covering strategy with respect to commodity demands leads to the highest quality solutions and the quickest convergence.