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.
By Donato Maragno, Marco Caserta, Alberto Sinigaglia, Komlanvi Ametana, David Corredor Montenegro, Luca D'Angelo
arXiv:2607. 16769v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have emerged as a powerful, differentiable class of learning models for graph-structured systems.
By Tushar Lone, Neha Karanjkar
arXiv:2606. 25362v1 Announce Type: cross Abstract: Sequential contextual stochastic programs model real-time decision systems in which each time epoch commits to an action under uncertainty whose consequences propagate into future decisions.
By Tinghan Ye, Shuaicheng Tong, Changkun Guan, Beste Basciftci, Pascal Van Hentenryck
Automated Algorithm Selection (AS) aims to improve problem-solving performance by selecting, for each problem instance, the most suitable algorithm from a predefined portfolio. This is particularly relevant to the Traveling Salesman Problem (TSP), where solver performance is strongly instance-dependent.
arXiv:2607. 18632v1 Announce Type: new Abstract: Automated Algorithm Selection (AS) aims to improve problem-solving performance by selecting, for each problem instance, the most suitable algorithm from a predefined portfolio.
By Zhaoxuan Li, Jiale Yang, Yifei Lu, Mustafa Misir
The paper introduces a fully differentiable message‑passing neural network (MPNN) designed to approximate the Uniform Facility Location (UniFL) problem. Unlike many learning‑based approaches that require supervision or reinforcement learning, this model incorporates principles from classical approximation algorithms, providing provable approximation guarantees. Empirical results show that it outperforms standard approximation algorithms and reduces the performance gap to integer linear programming solutions.
By Chendi Qian, Christopher Morris, Stefanie Jegelka, Christian Sohler