Generative deep learning for decision making in gas networks

Please always quote using this 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%.

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Author:Lovis AndersonORCiD, Mark TurnerORCiD, Thorsten KochORCiD
Document Type:ZIB-Report
Date of first Publication:2020/12/18
Series (Serial Number):ZIB-Report (20-38)