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arXiv Machine Learning By Donato Maragno, Marco Caserta, Alberto Sinigaglia, Komlanvi Ametana, David Corredor Montenegro, Luca D'Angelo

Learning-Augmented Optimization for Strategic Two-Echelon Spare Parts Network Design

Read the original on arXiv Machine Learning →

The paper presents a conservative learning‑augmented framework for designing a two‑echelon spare‑parts inventory network. It combines a graph neural network ensemble, variable neighborhood search, and set‑partitioning recombination to select cluster centers while limiting optimistic surrogate errors. In a case study on Amazon’s North American fulfillment network, the method achieves a 30.5% increase in combined savings over an exact‑evaluation baseline while preserving 99.8% service levels.

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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