LP Mining with LP2Graph: A Use Case for Railway Rescheduling

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[Submitted on 13 Jul 2026]

Title:LP Mining with LP2Graph: A Use Case for Railway Rescheduling

View a PDF of the paper titled LP Mining with LP2Graph: A Use Case for Railway Rescheduling, by J\"orn Maurischat and 2 other authors

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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. 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. Its core, LP2Graph, represents each formulation admitted by its canonical grammar as a typed variable--equation graph derived from a single canonical model; once a source is extracted into that model, everything downstream is deterministic. Each source is parsed into this model, homologized, and clustered bottom-up (over variables, then constraints and the objective, then whole-model structure) and, separately, by application domain and solution approach; the resulting groups are labeled by a rule-seeded, self-updating classifier. We validate the representation rather than assume it: per-cluster representatives are regenerated as independent LaTeX and re-solved across CBC, HiGHS and Gurobi against the optimum reported in the source paper. The outcome is an objective, repeatable taxonomy of variables, constraints and model types: the principled foundation on which our raiLPminer line of automated railway-rescheduling model development builds.

Comments: 22 pages, 2 figures. Work in progress, not yet submitted to a journal; comments welcome. Companion preprint to a talk at IFORS 2026, Vienna

Subjects:

Artificial Intelligence (cs.AI)

Cite as: arXiv:2607.11980 [cs.AI]

(or arXiv:2607.11980v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.11980

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Jörn Maurischat [view email] [v1] Mon, 13 Jul 2026 07:25:02 UTC (56 KB)

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