391. Bounding large-scale optimization problems under uncertainty
Invited abstract in session TC-1: Francesca Maggioni, stream Keynotes.
Tuesday, 12:30-14:00Room: Great Hall
Authors (first author is the speaker)
| 1. | Francesca Maggioni
|
| Department of Management, Information and Production Engineering, University of Bergamo |
Abstract
Many real world decision problems are dynamic and affected by uncertainty. Stochastic Programming provides a powerful approach to handle this uncertainty within a multi-period decision framework. However, as the number of stages increases, the computational complexity of these problems grows exponentially, posing significant challenges. To tackle this, approximation techniques are often used to simplify the original problem, providing useful upper and lower bounds for the objective function’s optimal value.
This talk explores methods for generating bounds for a wide variety of problem structures affected by uncertainty. We begin by discussing bounds based on scenario grouping under the assumption that a sufficiently large scenario tree is given but is unsolvable, both in the context of stochastic programming and distributionally robust optimization. Next, we extend these techniques to address more complex problems, including multi-horizon stochastic optimization and decision-dependent stochastic optimization.
Finally, the talk introduces the integration of these bounding methods with Benders’ decomposition, demonstrating how this combination can substantially reduce computation times and enhance the stability of the algorithm.
By exploring these approaches, the talk aims to inspire further advancements and innovative solutions in tackling large-scale optimization problems under uncertainty
Keywords
- Large Scale Optimization
- Stochastic Optimization
Status: accepted
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