Generative deep learning for decision making in gas networks

Lovis Anderson (), Mark Turner () and Thorsten Koch ()
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Lovis Anderson: Zuse Institute Berlin
Mark Turner: Institute of Mathematics, Technische Universität Berlin
Thorsten Koch: Institute of Mathematics, Technische Universität Berlin

Mathematical Methods of Operations Research, 2022, vol. 95, issue 3, No 6, 503-532

Abstract: 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%.

Keywords: Mixed-integer programming; Deep learning; Primal heuristic; Gas networks; Generative modelling (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00186-022-00777-x

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