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Vincent Guigues (vguiguesimpa.br) Abstract: We consider riskaverse formulations of stochastic linear programs having a structure that is common in reallife applications. Specifically, the optimization problem corresponds to controlling over a certain horizon a system whose dynamics is given by a transition equation depending affinely on an interstage dependent stochastic process. We put in place a rollinghorizon time consistent policy. For each time step, a riskaverse problem with constraints that are deterministic for the current time step and uncertain for future times is solved. To each uncertain constraint corresponds both a chance and a Conditional ValueatRisk constraint. We show that the resulting riskaverse problems are numerically tractable, being at worst conic quadratic programs. For the particular case in which uncertainty appears only on the righthand side of the constraints, such riskaverse problems are linear programs. We show how to write dynamic programming equations for these problems and define robust recourse functions that can be approximated recursively by cutting planes. The methodology is assessed and favourably compared with Stochastic Dual Dynamic Programming on a real size waterresource planning problem. Keywords: Stochastic Programming; Chance constraints; CVaR; Interstage dependence; Dynamic programming; Rolling horizon Category 1: Stochastic Programming Category 2: Other Topics (Dynamic Programming ) Category 3: Convex and Nonsmooth Optimization (Convex Optimization ) Citation: Download: [PDF] Entry Submitted: 02/02/2009 Modify/Update this entry  
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