Machine learning for decision making under uncertainty

Introduction

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.

Long-Term Decision Making

The decisions are spanning over a very long time horizon, and there is large uncertainty regarding:

1. Energy prices
2. Demand for energy
3. Production from renewable sources

Such problems 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.

Challenges in Optimization

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.

Proposed Methodology

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.
• We solve them using advanced optimization methods.
• We 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. We will also use a guided local search process to select scenarios that properly reflect properties of the full set of scenarios.

Expected Outcomes

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
• 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.

Conclusion

This will enable a more transparent and inclusive decision process, while ensuring well-founded and more robust investment decisions.