TY - JOUR
A1 - Anderson, Lovis
A1 - Turner, Mark
A1 - Koch, Thorsten
T1 - Generative deep learning for decision making in gas networks
T2 - Mathematical Methods of Operations Research
N2 - 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%.
Y1 - 2020
UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/8151
IS - Special Issue on Energy Networks
ER -