Researchers have developed a new algorithm called OCSAA to address the complexities of online pricing and inventory management. This algorithm jointly optimizes per-period inventory levels and a uniform price, then manages demand fulfillment through downstream allocation. OCSAA tackles the challenge of demand learning while accounting for how price shifts influence demand and reshape transportation logistics, which can lead to non-convex and non-smooth objectives. The proposed method achieves a high-probability regret guarantee of $\widetilde O(\sqrt T)$ and is supported by a matching information-theoretic lower bound. AI
IMPACT Introduces a novel algorithmic approach for integrating statistical learning with complex operations research problems, potentially improving efficiency in supply chain and e-commerce.
RANK_REASON Academic paper on a novel algorithm for operations research problems. [lever_c_demoted from research: ic=1 ai=0.7]
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