Overview Statistic: PDF-Downloads (blue) and Frontdoor-Views (gray)
  • Treffer 1 von 10
Zurück zur Trefferliste

Generative deep learning for decision making in gas networks

Zitieren Sie bitte immer diese URN: urn:nbn:de:0297-zib-81103
  • A decision support system relies on frequent re-solving of similar problem instances. While the general structure remains the same in corresponding applications, the input parameters are updated on a regular basis. We propose a generative neural network design for learning integer decision variables of mixed-integer linear programming (MILP) formulations of these problems. We utilise a deep neural network discriminator and a MILP solver as our oracle to train our generative neural network. In this article, we present the results of our design applied to the transient gas optimisation problem. With the trained network we produce a feasible solution in 2.5s, use it as a warm-start solution, and thereby decrease global optimal solution solve time by 60.5%.

Volltext Dateien herunterladen

Metadaten exportieren

Metadaten
Verfasserangaben:Lovis AndersonORCiD, Mark TurnerORCiD, Thorsten KochORCiD
Dokumentart:ZIB-Report
MSC-Klassifikation:90-XX OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING
Datum der Erstveröffentlichung:18.12.2020
Schriftenreihe (Bandnummer):ZIB-Report (20-38)
ISSN:1438-0064
Accept ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.