# Train Scheduling with Hybrid Answer Set Programming

arXiv: https://arxiv.org/abs/2003.08598 · HTML: https://mlsanity.com/paper/2003.08598/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [Current and Future Challenges in Knowledge Representation and Reasoning](https://mlsanity.com/paper/2308.04161/) (2023)
- [SM-based Semantics for Answer Set Programs Containing Conditional Literals and Arithmetic](https://mlsanity.com/paper/2511.01753/) (2025)
- [Scope Restriction for Scalable Real-Time Railway Rescheduling: An Exploratory Study](https://mlsanity.com/paper/2305.03574/) (2023)
- [Data Augmentation: a Combined Inductive-Deductive Approach featuring Answer Set Programming](https://mlsanity.com/paper/2310.14413/) (2023)
- [Optimising Dynamic Traffic Distribution for Urban Networks with Answer Set Programming](https://mlsanity.com/paper/2408.07521/) (2024)
- [Declarative Problem Solving in UAM Strategic Deconfliction](https://mlsanity.com/paper/2607.21197/) (2026)
- [ASPaeroFlow: Decomposition Heuristics for Joint Air Traffic Flow & Capacity Management](https://mlsanity.com/paper/2608.09315/) (2026)

## Abstract (arXiv)

We present a solution to real-world train scheduling problems, involving routing, scheduling, and optimization, based on Answer Set Programming (ASP). To this end, we pursue a hybrid approach that extends ASP with difference constraints to account for a fine-grained timing. More precisely, we exemplarily show how the hybrid ASP system clingo[DL] can be used to tackle demanding planning-and-scheduling problems. In particular, we investigate how to boost performance by combining distinct ASP solving techniques, such as approximations and heuristics, with preprocessing and encoding techniques for tackling large-scale, real-world train scheduling instances. Under consideration in Theory and Practice of Logic Programming (TPLP)