arXiv:2502.05349v2 Announce Type: replace-cross Abstract: Two-stage stochastic programs (2SPs) are widely used for decision-making under uncertainty, but their practical deployment is often limited by the large number of scenarios needed to approximate the conditional distribution of uncertain outcomes. We study contextual scenario generation: given contextual information, learn to produce a small, user-specified set of surrogate scenarios that, when used as input into the 2SP, lead to high-quality 2SP decisions. Existing scenario generation methods either ignore contextual information or are
The increasing complexity of decision-making under uncertainty, driven by larger datasets and more dynamic environments, necessitates more sophisticated and efficient optimization techniques.
Improving stochastic programming by integrating contextual information will significantly enhance decision-making in diverse real-world applications, leading to more robust and efficient resource allocation.
The ability to generate a small, user-specified set of surrogate scenarios for two-stage stochastic programs will make these powerful optimization tools more practical and widely deployable, especially in complex, data-rich environments.
- · Logistics and Supply Chain Management
- · Energy Sector (grid optimization)
- · Financial Services (portfolio optimization)
- · AI/ML Developers
- · Traditional stochastic optimization methods
- · Industries reliant on suboptimal decision-making
- · Manual scenario planning
More efficient and resilient operational planning in industries facing high uncertainty.
Reduced operational costs and improved resource utilization across a wide array of sectors.
Accelerated adoption of advanced AI/ML techniques for strategic and tactical decision-making due to improved practicality.
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