8110
eng
reportzib
0
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2020-12-18
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Generative deep learning for decision making in gas networks
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%.
1438-0064
urn:nbn:de:0297-zib-81103
Lovis Anderson
Mark Turner
Mark Turner
Thorsten Koch
ZIB-Report
20-38
OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING
Koch, Thorsten
MODAL-GasLab
Anderson, Lovis
MODAL-Gesamt
Turner, Mark Ruben
Mathematical Algorithmic Intelligence
Applied Algorithmic Intelligence Methods
MODAL-EnergyLab
UNSEEN
https://opus4.kobv.de/opus4-zib/files/8110/paper.pdf
https://opus4.kobv.de/opus4-zib/files/8110/ZIB-Report-20-38.pdf