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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
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