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Deep generative scenario search for stochastic optimization
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DOI：10.1016/j.ejor.2026.06.029.png)
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Abstract
En 中文
• A combined scenario generation and solution framework for two-stage SOPs is presented. • The algorithm functions as a novel local search heuristic in the scenario space. • DGSS is an instance-wise method generating small sets of synthetic scenarios that condense critical properties. • The framework adopts a black-box perspective making the approach agnostic to specific properties. • Benchmarked against 25 baseline methods from the literature DGSS provides best solution quality.
Keywords:
Combinatorial optimization
Stochastic optimization
Deep generative models
Scenario search
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W
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Cited Papers