arXiv AI

LP Mining with LP2Graph: A Use Case for Railway Rescheduling

arXiv:2607. 11980v1 Announce Type: new Abstract: Like many optimization-driven domains, railway rescheduling relies on Mixed-Integer Linear Programming (MILP), yet the field's modeling knowledge is scattered across hundreds of papers in incompatible notations, and narrative surveys organize it subjectively: they classify models by vocabulary rather than by structure, and reproduce neither.

Hugging Face Trending Papers
Jul 13

LP Mining with LP2Graph: A Use Case for Railway Rescheduling

Like many optimization-driven domains, railway rescheduling relies on Mixed-Integer Linear Programming (MILP), yet the field's modeling knowledge is scattered across hundreds of papers in incompatible notations, and narrative surveys organize it subjectively: they classify models by vocabulary rather than by structure, and reproduce neither. We present LP Mining with LP2Graph, a method that mines the structure of published LP and MILP formulations into a reproducible dataset and an induced taxonomy.

arXiv AI
Aug 11

Improving Constraint Models with LLM Agents

arXiv:2608. 08127v1 Announce Type: new Abstract: The runtime of Constraint Programming (CP) solvers is highly sensitive to modeling choices, such as symmetry breaking, implied constraints, global constraints, constraint reformulation, and variable representation.

By Florentina Voboril, Stefan Szeider
arXiv AI
Aug 5

IR2Solve: Structured Intermediate Representations for Cost-Efficient Optimization Autoformulation

arXiv:2608. 02641v1 Announce Type: cross Abstract: Large language models (LLMs) can translate natural-language optimization problems into solver-ready formulations, but direct code generation is brittle: schema, indexing, and semantic errors can cause compilation failures, infeasible models, or incorrect objectives, while iterative repair, search, and multi-agent workflows increase inference cost.

By Penglin Zhu, Linhai Zhang, Jungang Xu, Xinchi Wei, Xiuqi Wu
arXiv AI
Aug 3

ModelEquivBench: Certifying Multi-Relational Evaluation of LLM-Generated Optimization Models

arXiv:2607. 29431v1 Announce Type: new Abstract: Large language models increasingly generate optimization models from natural language, but existing evaluation often reduces a generated model and its ground truth to a single equivalent/not-equivalent verdict or an execution-success rate--labels that are neither independently checkable nor faithful to the multiple distinct senses in which two formulations can agree.

By Penglin Zhu, Jungang Xu
arXiv AI
Jul 7

Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets

arXiv:2607. 05179v1 Announce Type: cross Abstract: In liberalised railway systems, operators must set prices dynamically in an environment with partial observability, as they retain private information about their objectives and performance, where regulatory constraints prohibit communication or direct information exchange between competitors to prevent explicit collusion.

By Enrique Adrian Villarrubia-Martin, David Mu\~noz-Valero, Luis Rodriguez-Benitez, Giovanni Montana, Luis Jimenez-Linares