Learning-Augmented Optimization for Strategic Two-Echelon Spare Parts Network Design
We study the strategic design of a two-echelon spare-parts inventory network where evaluating each candidate topology requires an expensive inventory optimization model. The design partitions hundreds of sites into feasible clusters and selects a central replenishment site for each cluster to reduce costs while maintaining service levels. Because the optimizer favors candidates with high predicted savings, it can exploit optimistic surrogate errors. We develop a conservative framework combining a graph neural network ensemble, variable neighborhood search, and set-partitioning recombination. The surrogate is trained on exact cluster evaluations, while a lower quantile of ensemble-predicte...
| Authors | Donato Maragno, Marco Caserta, Alberto Sinigaglia et al. |
|---|---|
| Primary category | math.OC |
| Published | 11 September 2026 |
| Last updated at source | 11 September 2026 |
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