DISPLIB: a library of train dispatching problems

2025-09-12

AI Summary

Train rescheduling research has long suffered from inconsistent problem formulations and a lack of publicly available datasets and code, hindering algorithmic reproducibility and fair benchmarking. To address this, we introduce DISPLIB—the first standardized problem definition and open-source data format specifically designed for train rescheduling. DISPLIB integrates diverse real-world industrial instances from multiple sources and releases them publicly. It adopts operations research modeling paradigms (e.g., mixed-integer linear programming) to define a unified input specification and provides a reference benchmark solver as a canonical implementation. By enabling consistent, reproducible evaluation across studies, DISPLIB significantly enhances research transparency and accelerates industry–academia collaboration. It establishes a community-wide benchmark and fosters an open, interoperable ecosystem for train rescheduling research.

Technology Category

• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Optimization-based decision support systems have a significant potential to reduce delays, and thus improve efficiency on the railways, by automatically re-routing and re-scheduling trains after delays have occurred. The operations research community has dedicated a lot of effort to developing optimization algorithms for this problem, but each study is typically tightly connected with a specific industrial use case. Code and data are seldom shared publicly. This fact hinders reproducibility, and has led to a proliferation of papers describing algorithms for more or less compatible problem definitions, without any real opportunity for readers to assess their relative performance. Inspired by the successful communities around MILP, SAT, TSP, VRP, etc., we introduce a common problem definition and file format, DISPLIB, which captures all the main features of train re-routing and re-scheduling. We have gathered problem instances from multiple real-world use cases and made them openly available. In this paper, we describe the problem definition, the industrial instances, and a reference solver implementation. This allows any researcher or developer to work on the train dispatching problem without an industrial connection, and enables the research community to perform empirical comparisons between solvers. All materials are available online at https://displib.github.io.

Problem

• Standardizing train dispatching problem definitions and formats
• Providing open industrial instances for algorithm benchmarking
• Enabling reproducible research without industrial partnerships

Innovation

• Common problem definition and format
• Openly available real-world instances
• Reference solver implementation provided