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arXiv Machine Learning By Shuze Daniel Liu, David Simchi-Levi, Claire Chen, Chutong Gao, Shangtong Zhang

OR-Transformer: Scaling Real-Time Decision-Making to 1,000 Items

Read the original on arXiv Machine Learning →

OR-Transformer is a deep reinforcement learning framework designed for joint replenishment in supply chain operations with thousands of items. It uses a permutation‑equivariant Transformer architecture and pathwise‑gradient training to handle high‑dimensional observation and action spaces. In tests up to 1,024 items, it outperforms both learning‑based and rolling‑horizon MILP baselines and cuts online decision time by over four million times.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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