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870. Solution of Two-Stage Stochastic Programs under Decision-Dependent Uncertainty
Invited abstract in session TC-35: Stochastic Optimization with Decision-Dependent Uncertainty, stream Stochastic, Robust and Distributionally Robust Optimization.
Tuesday, 12:30-14:00Room: 44 (building: 303A)
Authors (first author is the speaker)
| 1. | Giovanni Pantuso
|
| Mathematical Sciences, University of Copenhagen | |
| 2. | Mike Hewitt
|
| Loyola University Chicago |
Abstract
We present an exact solution algorithm for two-stage stochastic programs under decision-dependent uncertainty. In such problems, first-stage decisions determine the probability distribution of second-stage uncertain parameters. Particularly, we focus on a broad class of problems where the number of potential probability distributions is finite but exponentially large.
The proposed method extends the well-known L-Shaped method and is applicable also to two-stage stochastic program with integer variables at both stages. We show that the new version converges finitely.
In addition, we present results from a computational study based on facility location problems under endogenous uncertainty. The results provide promising evidence of efficiency and scalability.
Keywords
- Programming, Stochastic
- Algorithms
- Branch and Cut
Status: accepted
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