  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%.
