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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.

As part of the smartrail 4.0 program, SBB is focusing with the project TMS (Traffic Management System) on algorithmic supported, optimized and integrated capacity planning. For solving this problem, we have experimented with different approaches from the literature and have compared their quality and performance for our specific instances. In this contribution, we present the results of this comparison and discuss how we want to use this for having the best possible solution for our ambitious goal.

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