Description du projet
Un cadre innovant d’apprentissage automatique pour faciliter la prise de décision
Ces dernières années, les investissements ont été confrontés à des défis croissants, car les décisions sont devenues plus complexes à prendre en raison des nombreux paramètres visibles et cachés qui entrent en jeu. Cette complexité a eu un impact significatif sur le processus de prise de décision pour les dépenses en capital de long terme, en particulier dans le contexte des questions relatives à la transition écologique. Le projet DECIDE, financé par le CER, vise à créer un cadre innovant pour la prise de décision en matière d’investissement. Ce cadre s’appuiera sur l’apprentissage automatique et la recherche opérationnelle pour répondre à l’incertitude croissante. Le projet emploiera un processus itératif, utilisant des modèles génératifs profonds pour générer et résoudre de nouveaux scénarios, et fournira aux utilisateurs une gamme d’options d’investissement.
Objectif
Many important decisions are taken under uncertainty since we do not know the development of various parameters. In particular the ongoing green transition requires large and urgent societal investments in new energy modes, infrastructure and technology. The decisions are spanning over a very long time-horizon, and there are large uncertainty towards energy prices, demand of energy, and production from renewable sources. Such problem can be described as two-stage stochastic optimization problems, where we first decide which facilities to establish, and then we have to schedule the production/transportation for a stochastic demand, using the given facilities. If the decision variables are discrete, such problems are extremely difficult to solve. In this project we will develop a new framework for investment decision making under uncertainty based on a combination of machine learning and operations research. Instead of solving a complex stochastic optimization problem defined on a fixed set of forecasted scenarios, we propose to use an iterative process: We repeatedly generate new scenarios, solve them using advanced optimization methods, and find the corresponding investment solutions. Our novel way of optimization will use deep generative models (DGMs) to generate small sets of scenarios matching the real distribution, and use a guided local search process to select scenarios that properly reflect properties of the full set of scenarios. The outcome of the iterative process is a palette of near-optimal solutions, which can be analyzed using data science methods to extract associations in investments, outrank dominated choices, and organize investments according to urgency. Knowing the full spectrum of possible choices opens up for a much broader discussion of investments, while allowing soft constraints to also be taken into account. This will enable a more transparent and inclusive decision process, while ensuring well-founded and more robust investment decisions.
Mots‑clés
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Thème(s)
Régime de financement
HORIZON-ERC - HORIZON ERC GrantsInstitution d’accueil
2800 Kongens Lyngby
Danemark