In the realm of railway rescheduling, Mixed-Integer Linear Programming (MILP) serves as a critical tool, yet the modeling knowledge is scattered across numerous papers with incompatible notations. We introduce LP Mining with LP2Graph, a method that mines the structure of published LP and MILP formulations into a reproducible dataset and taxonomy. The core component, LP2Graph, represents each formulation as a typed variable-equation graph derived from a single canonical model, ensuring that once a source is extracted into that model, all downstream processes are deterministic.
Each source is parsed into this model, homologized, and clustered bottom-up (first by variables, then constraints and objectives, and finally whole-model structure), and separately by application domain and solution approach; resulting groups are labeled by a rule-seeded, self-updating classifier. We validate this 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, forming the principled foundation for our raiLPminer line of automated railway-rescheduling model development.
Blogger's Review: The introduction of LP2Graph not only addresses the fragmentation of modeling knowledge in railway rescheduling but also enhances the reproducibility of models through automation. This method showcases the innovative potential within the optimization domain and warrants further exploration and application.