2255. A heuristic for two-stage mixed-binary stochastic programming problems based on scenario decomposition and machine learning techniques
Invited abstract in session WE-11: Heuristics, stream Heuristics, Metaheuristics and Matheuristics.
Wednesday, 16:30-18:00Room: U2-200
Authors (first author is the speaker)
| 1. | Jonas Wendisch
|
| DSDS, Europa-Universität Viadrina | |
| 2. | Achim Koberstein
|
| Information and Operations Management, European University Viadrina Frankfurt (Oder) | |
| 3. | Kevin Tierney
|
| Business Decisions and Analytics, University of Vienna |
Abstract
We consider the scenario decomposition method for two-stage mixed-binary stochastic programming problems first introduced by Ahmed in 2013. This algorithm systematically evaluates and cuts off first-stage candidate solutions obtained from scenario subproblems. We turn this method into a heuristic by using classification and ranking techniques to reduce the set of candidate first-stage solutions. We evaluate different variants emerging from this heuristic framework on the well-known stochastic server location (sslp) problem instances.
Keywords
- Stochastic Programming
- Artificial Intelligence
- Integer Programming
Status: accepted
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