An iterative problem-driven scenario reduction framework for stochastic optimization with conditional value-at-risk

doi:10.1016/j.epsr.2026.113730

An iterative problem-driven scenario reduction framework for stochastic optimization with conditional value-at-risk

2026-07-10

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DOI：10.1016/j.epsr.2026.113730

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• An iterative problem-driven scenario reduction framework. • Risk-averse optimization structure is embedded to enhance tail-risk representation. • Formulates scenario partitioning and selection as a mixed-integer program. • Achieves near-optimal solution with less than 1% optimality gap. • Demonstrates superior performance over existing scenario reduction methods.

Keywords:

Conditional value-at-risk

Problem-driven

Risk-averse

Scenario reduction

Stochastic optimization

An iterative problem-driven scenario reduction framework for stochastic optimization with conditional value-at-risk

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