Train Scheduling with Hybrid Answer Set Programming
This work addresses train scheduling optimization for railway systems, but it appears incremental as it builds on existing ASP methods.
The paper tackles real-world train scheduling problems by using a hybrid Answer Set Programming approach with difference constraints, achieving performance improvements through combined solving techniques for large-scale instances.
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)