发表机构
TU Dresden（德累斯顿工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对铁路重新调度领域建模知识分散且主观的问题，提出LP Mining with LP2Graph方法，通过LP2Graph将公式结构挖掘成可重现数据集和分类法，经多步骤处理和验证，得到客观可重复的分类法，为铁路重新调度模型开发奠定基础。
AI 中文摘要
与许多优化驱动领域一样，铁路重新调度依赖混合整数线性规划（MILP），但该领域的建模知识分散在数百篇符号不兼容的论文中，叙述性综述组织方式主观。我们提出了LP Mining with LP2Graph方法，它将已发表的线性规划（LP）和MILP公式的结构挖掘到一个可重现的数据集中并生成诱导分类法。其核心LP2Graph将每个由其规范语法认可的公式表示为从单个规范模型派生的类型化变量 - 方程图。每个源被解析到该模型中，进行同源化并自下而上聚类，还按应用领域和解决方案方法聚类，结果组由规则种子、自我更新的分类器标记。我们通过重新生成独立的LaTeX并在CBC、HiGHS和Gurobi上针对源论文中报告的最优解重新求解来验证表示。结果是一个关于变量、约束和模型类型的客观、可重复的分类法，是我们自动铁路重新调度模型开发的raiLPminer系列的原则基础。
英文摘要
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.
Comments22 pages, 2 figures. Work in progress, not yet submitted to a journal; comments welcome. Companion preprint to a talk at IFORS 2026, Vienna
