Generative deep learning for decision making in gas networks
| Autor: | Lovis Anderson, Mark Turner, Thorsten Koch |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
mixed-integer programming Computer Science - Artificial Intelligence General Mathematics deep learning generative modelling 510 Mathematik Management Science and Operations Research Artificial Intelligence (cs.AI) Optimization and Control (math.OC) FOS: Mathematics ddc:510 gas networks Mathematics - Optimization and Control Software primal heuristic |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|