# Gianpiero Canessa: Static risk averse models and applications

Abstract:

Stochastic optimization allows the modeler to incorporate risk into his decision making process. Different applications of this optimization methodology will be presented, showing in each case the innovation incorporated into a classical deterministic model and transforming it into the stochastic version.

The main challenge of stochastic optimization is that one often generates models that cannot be solved directly: we need to transform it to a tractable deterministic equivalent problem (DEP) and solve it using any of the commercial solvers available. Naive reformulations into DEPs can, and often will, result in complex and/or large DEPs that current solvers may not be able to solve in an adequate amount of time, or even load in memory due to its size. This work is centered in showing how we can circumvent these difficulties and obtain results that are equivalent to those that would be obtained in the original stochastic formulation.

**Time: **
Fri 2019-09-13 11.00 - 12.00

**Location: **
F11

**Participating: **
Gianpiero Canessa, Postdoctoral Researcher at KTH Royal Institute of Technology