Descrizione del progetto
Un innovativo quadro di riferimento basato sull’apprendimento automatico per il processo decisionale
Negli ultimi anni sono aumentate le sfide in relazione alla realizzazione di investimenti in quanto è incrementata la complessità delle relative decisioni, a causa dei numerosi parametri visibili e nascosti esistenti. Queste difficoltà hanno inciso in modo significativo sul processo decisionale per gli investimenti a lungo termine, in particolare nel contesto delle decisioni legate alla transizione verde. Il progetto DECIDE, finanziato dal CER, mira a creare un innovativo quadro di riferimento per le decisioni di investimento. Questo framework sfrutterà l’apprendimento automatico e la ricerca operativa per affrontare la crescente incertezza in tal ambito avvalendosi di un processo iterativo e utilizzando modelli generativi profondi per generare e risolvere nuovi scenari, fornendo agli utenti una vasta gamma di opzioni di investimento.
Obiettivo
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.
Campo scientifico
Parole chiave
Programma(i)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Argomento(i)
Meccanismo di finanziamento
HORIZON-ERC - HORIZON ERC GrantsIstituzione ospitante
2800 Kongens Lyngby
Danimarca