将铁路调度的数学规划模型结构化，自动生成可复现的分类体系。
LP Mining with LP2Graph: A Use Case for Railway Rescheduling
- 通过语法统一建模，将论文中的线性规划公式转为变量-方程图结构。
- 在3个求解器上验证，每类模型的代表例均能复现原文最优解。
- 适合自动化建模研究者与铁路调度优化工程师参考使用。
铁路调度依赖混合整数线性规划（MILP），但领域知识分散在数百篇论文中，且现有综述采用主观词汇分类，无法复现。本文提出LP Mining with LP2Graph，将已发表的线性规划与混合整数规划公式结构化为可复现的数据集与诱导分类体系。其核心方法LP2Graph将每个公式按标准语法规则表示为带类型的变量-方程图；一旦源文档被解析至此标准模型，后续过程全为确定性操作。各来源经解析、同源化后，分层次聚类（变量→约束与目标→整体结构），并独立按应用领域与求解方法分类；聚类结果由规则种子驱动、自我更新的分类器标注。通过重构每类代表性模型为独立LaTeX格式，并在CBC、HiGHS和Gurobi中重新求解，与原始论文报告的最优解对比，验证了表示的有效性。最终获得客观、可重复的变量、约束与模型类型分类体系，为自动化铁路调度模型开发提供坚实基础。
原文摘要 · Abstract (English)
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.
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