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Generative deep learning for decision making in gas networks

under review
  • 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%.
Metadaten
Author:Lovis AndersonORCiD, Mark TurnerORCiD, Thorsten KochORCiD
Document Type:Article
Parent Title (English):Mathematical Methods of Operations Research
Issue:Special Issue on Energy Networks
Year of first publication:2020
Preprint:urn:nbn:de:0297-zib-81103