AI Research
Position Paper: Post-Solve Robustness in Decision Engines: Feasible Regions and Smoothness Under Perturbations
Mixed-Integer Linear Programming (MILP) decision engines routinely output nominally optimal plans for high-stakes industrial systems. Yet deployment rarely matches solve-time assumptions: small perturbations in costs, demands, or resource availability can invalidate feasibility o...
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ArXiv CS.AI
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ArXiv CS.AI
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Ensuring decision engine outputs remain valid and reliable when faced with real-world data variability is critical for high-stakes industrial adoption. This research addresses the 'post-solve robustness gap,' proposing a unified framework to quantify and improve how trustworthily existing solutions perform under perturbations.
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