Inventory Optimization Under Uncertainty: Broad random shocks trained a tougher inventory planner than a clever AI adversary did

Article overview

A simulated distribution center becomes a laboratory for testing inventory policies against supply disruptions. A language model manages the experimental loop. Broad random shocks produced a directional advantage over targeted adversarial training, with the article explicitly noting that five runs do not establish statistical significance. The documented research process and its self-audit are central to the work.

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