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Train Scheduling with Hybrid Answer Set Programming

Published online by Cambridge University Press:  27 April 2020

DIRK ABELS ,
JULIAN JORDI ,
MAX OSTROWSKI ,
TORSTEN SCHAUB Open the ORCID record for TORSTEN SCHAUB [Opens in a new window] ,
AMBRA TOLETTI  and
PHILIPP WANKO Open the ORCID record for PHILIPP WANKO [Opens in a new window]
DIRK ABELS
Affiliation:
SBB, Switzerland
JULIAN JORDI
Affiliation:
SBB, Switzerland
MAX OSTROWSKI
Affiliation:
Potassco Solutions, Germany
TORSTEN SCHAUB
Affiliation:
Potassco Solutions, Germany and University of Potsdam, Germany Simon Fraser University, Canada and Griffith University, Australia
AMBRA TOLETTI
Affiliation:
SBB, Switzerland
PHILIPP WANKO
Affiliation:
Potassco Solutions, Germany and University of Potsdam, Germany, (e-mail: [email protected])
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Abstract

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.

Keywords

constraintsknowledge representation and nonmonotonic reasoninglogic programming methodology and application
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 21 , Issue 3 , May 2021 , pp. 317 - 347
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Copyright
© The Author(s), 2020. Published by Cambridge University Press

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Footnotes

*

This is a substantially extended and revised version of Abels et al. (2019). This work was partially funded by DFG grants SCHA 550/9 and 11.

References

Abels, D., Jordi, J., Ostrowski, M., Schaub, T., Toletti, A. and Wanko, P. 2019. Train scheduling with hybrid ASP. In Proceedings of the Fifteenth International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR’19), Balduccini, M., Lierler, Y. and Woltran, S., Eds. Lecture Notes in Artificial Intelligence, Vol. 11481. Springer-Verlag, 3–17.CrossRefGoogle Scholar
Andres, B., Kaufmann, B., Matheis, O. and Schaub, T. 2012. Unsatisfiability-based optimization in clasp. In Technical Communications of the Twenty-eighth International Conference on Logic Programming (ICLP’12), Dovier, A. and Santos Costa, V., Eds., Vol. 17. Leibniz International Proceedings in Informatics (LIPIcs), 212–221.Google Scholar
Baptiste, P., Pape, C. L. and Nuijten, W. 2012. Constraint-Based Scheduling: Applying Constraint Programming to Scheduling Problems, Vol. 39. Springer Science+Business Media.Google Scholar
Bofill, M., Palahí, M., Suy, J. and Villaret, M. 2012. Solving constraint satisfaction problems with sat modulo theories. Constraints 17, 3, 273–303.CrossRefGoogle Scholar
Caprara, A., Fischetti, M. and Toth, P. 2002. Modeling and solving the train timetabling problem. Operations Research 50, 851–861.CrossRefGoogle Scholar
Gebser, M., Harrison, A., Kaminski, R., Lifschitz, V. and Schaub, T. 2015a. Abstract Gringo. Theory and Practice of Logic Programming 15, 4–5, 449–463. https://arxiv.org/abs/1507.06576.CrossRefGoogle Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Lindauer, M., Ostrowski, M., Romero, J., Schaub, T. and Thiele, S. 2015b. Potassco User Guide, 2nd ed. University of Potsdam.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T. and Wanko, P. 2016. Theory solving made easy with clingo 5. In Technical Communications of the Thirty-Second International Conference on Logic Programming (ICLP’16), Carro, M. and King, A., Eds. OpenAccess Series in Informatics (OASIcs), Vol. 52. Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, 2:1–2:15.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B. and Schaub, T. 2019. Multi-shot ASP solving with clingo. Theory and Practice of Logic Programming 19, 1, 27–82.CrossRefGoogle Scholar
Gebser, M., Kaufmann, B., Otero, R., Romero, J., Schaub, T. and Wanko, P. 2013. Domain-specific heuristics in answer set programming. In Proceedings of the Twenty-Seventh National Conference on Artificial Intelligence (AAAI’13), des Jardins, M. and Littman, M., Eds. AAAI Press, 350–356.Google Scholar
Janhunen, T., Kaminski, R., Ostrowski, M., Schaub, T., Schellhorn, S. and Wanko, P. 2017. Clingo goes linear constraints over reals and integers. Theory and Practice of Logic Programming 17, 5-6, 872–888.CrossRefGoogle Scholar
Janhunen, T., Liu, G. and Niemelä, I. 2011. Tight integration of non-ground answer set programming and satisfiability modulo theories. In Proceedings of the First Workshop on Grounding and Transformation for Theories with Variables (GTTV’11), Cabalar, P., Mitchell, D., Pearce, D. and Ternovska, E., Eds. 1–13.Google Scholar
Lifschitz, V. 1999. Answer set planning. In Proceedings of the International Conference on Logic Programming (ICLP’99), de Schreye, D., Ed. MIT Press, 23–37.Google Scholar
Liu, G., Janhunen, T. and Niemelä, I. 2012. Answer set programming via mixed integer programming. In Proceedings of the Thirteenth International Conference on Principles of Knowledge Representation and Reasoning (KR’12), Brewka, G., Eiter, T. and McIlraith, S., Eds. AAAI Press, 32–42.Google Scholar
Oliveira, E. and Smith, B. 2000. A job-shop scheduling model for the single-track railway scheduling problem. LU SCS RR 21, School of Computer Studies, University of Leeds.Google Scholar
Rodriguez, J. 2007. A constraint programming model for real-time train scheduling at junctions. Transportation Research Part B: Methodological 41, 2, 231–245.CrossRefGoogle Scholar
Taillard, E. 1993. Benchmarks for basic scheduling problems. European Journal of Operational Research 64, 2, 278–285.CrossRefGoogle Scholar
Törnquist, J. 2006. Computer-based decision support for railway traffic scheduling and dispatching: A review of models and algorithms. In Proceedings of Fifth Workshop on Algorithmic Methods and Models for Optimization of Railways (ATMOS’05), Kroon, L. G. and Möhring, R. H., Eds. OpenAccess Series in Informatics (OASIcs), Vol. 2. Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik.Google Scholar