Generative deep learning for decision making in gas networks

TL;DR

This paper introduces a generative neural network approach to efficiently produce feasible solutions for gas network decision problems, significantly reducing optimization time by leveraging deep learning and MILP solvers.

Contribution

The paper presents a novel generative neural network design trained with a MILP solver as an oracle for decision making in gas network optimization.

Findings

Feasible solutions generated in 2.5 seconds.

Reduced global solve time by 60.5%.

Effective warm-start solutions for MILP problems.

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. 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%.