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Sampling Based Progressive Hedging Algorithms For Stochastic Programming Problems, Nezir Aydin

Wayne State University Dissertations

Many real-world optimization problems have parameter uncertainty. For instances where the uncertainties can be estimated to a certain degree, stochastic programming (SP) methodologies are used to identify robust plans. Despite advances in SP, it is still a challenge to solve real world stochastic programming problems, in part due to the exponentially increasing number of scenarios. For two-stage and multi-stage problems, the number of scenarios increases exponentially with the number of uncertain parameters, and for multi-stage problems also with the number of decision stages.

In the case of large scale mixed integer stochastic problem instances, there are usually two common approaches: …