Solving Integrated Periodic Railway Timetabling with Satisfiability Modulo Theories: A Scalable Approach to Routing and Vehicle Circulation

TL;DR

This paper presents a scalable SMT-based method for integrated railway timetabling, routing, and vehicle circulation that outperforms traditional models and reduces fleet size using real-world data.

Contribution

It introduces the VCR-PESP model and demonstrates SMT's effectiveness for large-scale, integrated railway planning problems, overcoming discretisation limitations.

Findings

SMT supports continuous time, avoiding discretisation artifacts.

Integrated planning reduces fleet size compared to sequential approaches.

The approach scales effectively to complex, real-world networks.

Abstract

This paper introduces a novel approach for jointly solving the periodic Train Timetabling Problem (TTP), train routing, and Vehicle Circulation Problem (VCP) through a unified optimization model. While these planning stages are traditionally addressed sequentially, their interdependencies often lead to suboptimal vehicle usage. We propose the VCR-PESP, an integrated formulation that minimizes fleet size while ensuring feasible and infrastructure-compliant periodic timetables. We present the first Satisfiability Modulo Theories (SMT)-based method for the VCR-PESP to solve the resulting large-scale instances. Unlike the Boolean Satisfiability Problem (SAT), which requires time discretisation, SMT supports continuous time via difference constraints, eliminating the trade-off between temporal precision and encoding size. Our approach avoids rounding artifacts and scales effectively, outperforming both SAT and Mixed Integer Program (MIP) models across non-trivial instances. Using real-world data from the Swiss narrow-gauge operator RhB, we conduct extensive experiments to assess the impact of time discretisation, vehicle circulation strategies, route flexibility, and planning integration. We show that discrete models inflate vehicle requirements and that fully integrated solutions substantially reduce fleet needs compared to sequential approaches. Our framework consistently delivers high-resolution solutions with tractable runtimes, even in large and complex networks. By combining modeling accuracy with scalable solver technology, this work establishes SMT as a powerful tool for integrated railway planning. It demonstrates how relaxing discretisation and solving across planning layers enables more efficient and implementable timetables.