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
The paper introduces a constraint‑aware conditional generative framework for creating synthetic origin‑destination demand data in hierarchical logistics networks. By modeling demand as a conditional distribution over destinations given each origin, the method incorporates differentiable operational constraints directly into the generative objective, allowing topology‑aware synthesis that remains operationally feasible. Experiments on industrial fulfillment and transportation networks show a 16% performance gain over graph neural network baselines, 87% operational compliance, and efficient cold‑start adaptation, supporting capacity planning, network design evaluation, and routing optimization.
By Leian Chen
The paper presents a method for reducing the size of Quadratic Unconstrained Binary Optimization (QUBO) models used to solve the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) on quantum annealers. It introduces adaptive penalty calibration to improve constraint satisfaction and replaces hand‑tuned merge heuristics with a graph neural network (GNN) that consistently achieves higher feasibility across Solomon benchmark families. Experiments on simulated annealing and a D‑Wave Advantage2 processor show significant reductions in constraint violations and improved feasibility rates, with the QUBO size remaining 5–6 times smaller.
By Youssef Kamel Rezk, Pawe{\l} Gora
arXiv:2507. 10834v4 Announce Type: replace Abstract: Assortment optimization seeks to select a subset of substitutable products, subject to constraints, to maximize expected revenue.
By Guokai Li, Pin Gao, Stefanus Jasin, Zizhuo Wang
The paper introduces a novel end‑to‑end, size‑agnostic graph reinforcement learning framework for the one‑dimensional bin packing problem (1D‑BPP). It models packing as a Markov decision process on an item‑compatibility graph, where a graph neural network actor‑critic policy learns to merge compatible partial bins. Empirical results on the BPPLIB benchmark show that the learned policy reduces the mean optimality gap of a constructive heuristic from 2.66 % to 2.31 %, performs competitively against other learned methods, and outperforms a state‑of‑the‑art learned solver on the hardest benchmark family.
By M. Asl{\i} Ayd{\i}n
arXiv:2608. 06668v1 Announce Type: new Abstract: As an important component of the supply chain industry, transportation has experienced rapid development in the past decade with the assistance of digital platforms and intelligent algorithms.
By Siliang Lu, Dan Hu, Lili Wu
arXiv:2608. 13212v1 Announce Type: new Abstract: Networked systems, from power grids to traffic networks and cloud clusters, carry loads across nodes with limited capacity.
By Orkun Irsoy, Leman Akoglu, Osman Yagan