arXiv AI

Sequence Variables: A Constraint Programming Computational Domain for Routing and Sequencing

arXiv AI
Sep 4

Counterfactual Routing Using Integer Programming with Constraint Generation

The paper "Counterfactual Routing Using Integer Programming with Constraint Generation" presents a solution to the IJCAI 2025 Counterfactual Routing Competition. The authors model the problem as an integer program and iteratively add constraints until an exact solution is found. In evaluation on held‑out test instances, their method ranked fourth in solution quality and was the fastest, averaging 9.0 seconds versus 118.8 seconds for the next‑fastest submission.

By Dani\"el Vos, Sterre Lutz
arXiv Machine Learning
Sep 4

Learning Constraints-Based Adaptive Hypergraph Neural Networks for Solving Vehicle Routing Problems

The paper presents an end‑to‑end framework that uses constraint‑oriented hypergraphs and reinforcement learning to solve vehicle routing problems. It introduces a dynamic hyperedge reconstruction strategy for better hypergraph representation and a double‑pointer attention decoder for iterative solution generation. Experiments on benchmark datasets show that the method removes the need for complex heuristic operators while improving solution quality.

By Zhenwei Wang, Tiehua Zhang, Jing Liu, Heng Yu, Kaizhu Huang, Ruibin Bai
arXiv Machine Learning
Sep 7

A Constraint-Aware Generative Framework for Synthetic Origin-Destination Demand in Logistics Networks

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
arXiv AI
Jul 24

Declarative Problem Solving in UAM Strategic Deconfliction

arXiv:2607. 21197v1 Announce Type: cross Abstract: The growing demand for Urban Air Mobility (UAM) introduces significant challenges in airspace management, particularly within densely populated metropolitan regions.

By Gioacchino Sterlicchio (DMMM, Polytechnic University of Bari, Bari, Italy), Angelo Oddi (ISTC-CNR, Rome, Italy), Riccardo Rasconi (ISTC-CNR, Rome, Italy), Francesca Alessandra Lisi (DIB,CILA, University of Bari Aldo Moro, Bari, Italy)