Descripción del proyecto
Innovador marco de aprendizaje automático para la toma de decisiones
En los últimos años, las inversiones se han enfrentado a retos cada vez mayores, ya que las decisiones de inversión se han vuelto más complejas debido a los numerosos parámetros visibles y ocultos que intervienen. Esta complejidad ha tenido un impacto significativo en el proceso de toma de decisiones para las inversiones a largo plazo, especialmente en el contexto de las decisiones de transición ecológica. El equipo del proyecto DECIDE, financiado por el Consejo Europeo de Investigación, pretende crear un marco innovador para la toma de decisiones de inversión. Dicho marco aprovechará el aprendizaje automático y la investigación operativa para hacer frente a la creciente incertidumbre. El equipo del proyecto empleará un proceso iterativo, utilizando modelos generativos profundos para generar y resolver nuevos escenarios, proporcionando a los usuarios una gama de opciones de inversión.
Objetivo
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.
Palabras clave
Programa(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Régimen de financiación
HORIZON-ERC - HORIZON ERC GrantsInstitución de acogida
2800 Kongens Lyngby
Dinamarca