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Generative deep learning for decision making in gas networks

Author

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  • Lovis Anderson

    (Zuse Institute Berlin)

  • Mark Turner

    (Institute of Mathematics, Technische Universität Berlin
    Zuse Institute Berlin)

  • Thorsten Koch

    (Institute of Mathematics, Technische Universität Berlin
    Zuse Institute Berlin)

Abstract

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. The trained generative neural network produces a feasible solution in 2.5s, and when used as a warm start solution, decreases global optimal solution time by 60.5%.

Suggested Citation

  • Lovis Anderson & Mark Turner & Thorsten Koch, 2022. "Generative deep learning for decision making in gas networks," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 95(3), pages 503-532, June.
    • Handle: RePEc:spr:mathme:v:95:y:2022:i:3:d:10.1007_s00186-022-00777-x
    DOI: 10.1007/s00186-022-00777-x
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    References listed on IDEAS

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    1. Ruiz, Ruben & Maroto, Concepcion & Alcaraz, Javier, 2004. "A decision support system for a real vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 153(3), pages 593-606, March.
    2. Friedrich Kunz & Mario Kendziorski & Wolf-Peter Schill & Jens Weibezahn & Jan Zepter & Christian von Hirschhausen & Philipp Hauser & Matthias Zech & Dominik Möst & Sina Heidari & Björn Felten & Christ, 2017. "Electricity, Heat and Gas Sector Data for Modelling the German System," Data Documentation 92, DIW Berlin, German Institute for Economic Research.
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    1. Yilmaz, Dogacan & Büyüktahtakın, İ. Esra, 2024. "An expandable machine learning-optimization framework to sequential decision-making," European Journal of Operational Research, Elsevier, vol. 314(1), pages 280-296.

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