Skip to main content
SpringerLink
Log in

Train Dispatching

  • Chapter
  • First Online:
Handbook of Optimization in the Railway Industry

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 268))

Abstract

Train rescheduling problems have received significant attention in the operations research community during the past 20–30 years. These are complex problems with many aspects and constraints to consider. This chapter defines the problem and summarizes the variety of model types and solution approaches developed over the years, in order to address and solve the train dispatching problem from the infrastructure manager perspective. Despite all the research efforts, it is, however, only very recently that the railway industry has made significant attempts to explore the large potential in using optimization-based decision-support to facilitate railway traffic disturbance management. This chapter reviews state-of-practice and provides a discussion about the observed slow progress in the application of optimization-based methods in practice. A few successful implementations have been identified, but their performance as well as the lessons learned from the development and implementation of those system are unfortunately only partly available to the research community, or potential industry users.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Meet-pass events are central operations in train dispatching and in particular in single-track lines. In such lines, indeed, trains can only meet or pass each other in a station or sections where you have parallel tracks. When trains are delayed, dispatchers must often identify suitable new meet or pass points.

References

  1. Bettinelli A, Santini A, Vigo D (2017) A real-time conflict solution algorithm for the train rescheduling problem. Transp Res B 106:237–265. (cited on page 276)

    Google Scholar 

  2. Borndörfer R, Klug T, Lamorgese L, Mannino C, Reuther M, Schlechte T (2017) Recent success stories on integrated optimization of railway systems. Transp Res C Emerg Technol 74:196–211. https://doi.org/10.1016/j.trc.2016.11.015 (cited on page 277)

    Article  Google Scholar 

  3. Cacchiani V, Toth P (2012) Nominal and robust train timetabling problems. Eur J Oper Res 219(3):727–737. https://doi.org/10.1016/j.ejor.2011.11.003 (cited on page 269)

    Article  Google Scholar 

  4. Cacchiani V, Huisman D, Kidd M, Kroon L, Toth P, Veelenturf L, Wagenaar J (2014) An overview of recovery models and algorithms for realtime railway rescheduling. English. Transp Res B 63:15–37. https://doi.org/10.1016/j.trb.2014.01.009 (cited on pages 267, 277)

  5. Caimi G, Chudak F, Fuchsberger M, Laumanns M, Zenklusen R (2011) A new resource-constrained multicommodity flow model for routing and scheduling. Transp Sci 45(2):212–227. https://doi.org/10.1287/trsc.1100.0349 (cited on page 269)

    Article  Google Scholar 

  6. Caimi G, Fuchsberger M, Laumanns M, Lüthi M (2012) A model predictive control approach for discrete-time rescheduling in complex central railway station areas. Comput Oper Res 39(11):2578–2593. https://doi.org/10.1016/j.cor.2012.01.003 (cited on pages 269, 275)

  7. Corman F, Meng L (2015) A review of online dynamic models and algorithms for railway traffic management. IEEE Trans Intell Transp Syst 16(3):1274–1284. https://doi.org/10.1109/tits.2014.2358392 (cited on page 277)

    Article  Google Scholar 

  8. Corman F, D’Ariano A, Pacciarelli D, Pranzo M (2012) Optimal inter-area coordination of train rescheduling decisions. Transp Res E Log Transp Rev 48(1):71–88. https://doi.org/10.1016/j.sbspro.2011.04.508 (cited on page 276)

    Article  Google Scholar 

  9. Corman F, Ariano AD, Marra AD, Pacciarelli D, Samà M (2016) Integrating train scheduling and delay management in real-time railway traffic control. Transp Res E Log Transp Rev. https://doi.org/10.1016/j.tre.2016.04.007 (cited on page 268)

    Google Scholar 

  10. D’Ariano A, Pacciarelli D, Pranzo M (2007) A branch and bound algorithm for scheduling trains in a railway network. Eur J Oper Res 183(2):643–657. https://doi.org/10.1016/j.ejor.2006.10.034 (cited on pages 271, 274)

  11. Dollevoet T, Corman F, D’Ariano A, Huisman D (2014) An iterative optimization framework for delay management and train scheduling. Flex Serv Manuf J 26(4):490–515. https://doi.org/10.1007/s10696-013-9187-2 (cited on pages 268, 275)

  12. Dollevoet T, Huisman D, Kroon L, Schmidt M, Schöbel A (2014) Delay management including capacities of stations. Transp Sci 49(2):185–203. https://doi.org/10.1287/trsc.2013.0506 (cited on page 275)

    Article  Google Scholar 

  13. Fang W, Yang S, Yao X (2015) A survey on problem models and solution approaches to rescheduling in railway networks. IEEE Trans Intell Transp Syst 16(6):2997–3016. https://doi.org/10.1109/TITS.2015.2446985 (cited on page 269)

    Article  Google Scholar 

  14. Ghaemi N, Goverde RMP (2015) Review of railway disruption management practice and literature. In: 6th international conference on railway operations modelling and analysis-RailTokyo2015. UUID: 9f911a03-155e-4dd3-a64e-3416fd603aa1 (cited on page 267)

    Google Scholar 

  15. Hansen IA (2010) State-of-the-art of railway operations research. In: Computers in Railways X. Timetable planning and information quality, Chap A 4. WIT Press, Boston, pp 35–47. https://doi.org/10.2495/CR060561 (cited on page 268)

  16. Harrod S, Schlechte T (2013) A direct comparison of physical block occupancy versus timed block occupancy in train timetabling formulations. Transp Res E Log Transp Rev 54:50–66. ISSN: 1366-5545. https://doi.org/10.1016/j.tre.2013.04.003 (cited on page 269)

    Article  Google Scholar 

  17. Kecman P, Corman F, D’Ariano A, Goverde RMP (2013) Rescheduling models for railway traffic management in large-scale networks. Public Transp 5(1–2):95–123 (cited on page 270)

    Google Scholar 

  18. Lamorgese L, Mannino C (2015) An exact decomposition approach for the real-time train dispatching problem. Oper Res 63:48–64. https://doi.org/10.1287/opre.2014.1327 (cited on pages 269, 275, 279)

  19. Lamorgese L, Mannino C (2016) A non-compact formulation for job-shop scheduling problems in transportation. In: Algorithmic methods for optimization in public transport (Dagstuhl Seminar 16171) Dagstuhl Reports, vol 6(4). Schloss Dagstuhl, Wadern. https://doi.org/10.4230/DagRep.6.4.139 (cited on page 275)

  20. Lamorgese L, Mannino C, Piacentini M (2016) Optimal train dispatching by benders’-like reformulation. Transp Sci 50(3):910–925. https://doi.org/10.1287/trsc.2015.0605 (cited on pages 275, 279)

  21. Lusby RM, Larsen J, Ehrgott M, Ryan D (2011) Railway track allocation: models and methods. OR Spectrum 33(4):843–883. https://doi.org/10.1007/s00291-009-0189-0 (cited on page 269)

    Article  Google Scholar 

  22. Lusby R, Larsen J, Ryan D, Ehrgott M (2011) Routing trains through railway junctions: a new set-packing approach. Transp Sci 45(2):228–245. https://doi.org/10.1287/trsc.1100.0362 (cited on page 270)

    Article  Google Scholar 

  23. Lusby RM, Larsen J, Ehrgott M, Ryan DM (2013) A set packing inspired method for real-time junction train routing. Comput Oper Res 40(3):713–724. https://doi.org/10.1016/j.cor.2011.12.004 (cited on page 275)

    Article  Google Scholar 

  24. Mannino C (2011) Real-time traffic control in railway systems. In: Caprara A, Kontogiannis S (eds) Proceedings of the 11th workshop on algorithmic approaches for transportation modelling, optimization, and systems (ATMOS 2011). OpenAccess Series in Informatics (OASIcs), vol 20. Schloss Dagstuhl, Wadern, pp 1–14. ISBN: 978-3-939897-33-0. https://doi.org/10.4230/OASIcs.ATMOS.2011.1 (cited on pages 269, 278)

  25. Mannino C, Mascis A (2009) Optimal real-time traffic control in metro stations. English. Oper Res 57:1026–1039. https://doi.org/10.1287/opre.1080.0642 (cited on pages 271, 274, 278)

  26. Mannino C, Mascis A (2010) Fast track to fixing rail delays-award-winning automated rail re-routing system saves time and money. OR MS Today 37(2):28 (cited on page 278)

    Google Scholar 

  27. Mascis A, Pacciarelli D (2002) Job shop scheduling with blocking and no-wait constraints. English. Eur J Oper Res 143(3):498–517. https://doi.org/10.1016/s0377-2217(01)00338-1 (cited on pages 269, 271)

  28. Meng L, Zhou X (2014) Simultaneous train rerouting and rescheduling on an N-track network: a model reformulation with network-based cumulative flow variables. Transp Res B Methodol 67:208–234. https://doi.org/10.1016/j.trb.2014.05.005 (cited on pages 269, 275)

  29. Nielsen LK, Kroon L, Maróti G (2012) A rolling horizon approach for disruption management of railway rolling stock. Eur J Oper Res 220(2):496–509. https://doi.org/10.1016/j.ejor.2012.01.037 (cited on page 275)

    Article  Google Scholar 

  30. Pellegrini P, Marlière J, Rodriguez D (2014) Optimal train routing and scheduling for managing traffic perturbations in complex junctions. Transp Res B 59:58–80. https://doi.org/10.1016/j.trb.2013.10.013 (cited on page 269)

    Article  Google Scholar 

  31. Roy B, Sussmann B (1964) Les problèmes d’ordonnancement avec contraintes disjonctives, Note D.S. No. 9 bis. French. Tech. rep. SEMA, France (cited on page 271)

    Google Scholar 

  32. Şahin G, Ahuja RK, Cunha CB (2010) Integer programming based solution approaches for the train dispatching problem. Tech. rep. Sabanci University (cited on page 275)

    Google Scholar 

  33. Schlechte T, Borndörfer R, Erol B, Graffagnino T, Swarat E (2011) Micro-macro transformation of railway networks. English. J Rail Transp Plann Manage 1(1):38–48. https://doi.org/10.1016/j.jrtpm.2011.09.001 (cited on page 275)

    Google Scholar 

  34. Toletti A, Weidmann U (2016) Modelling customer inconvenience in train rescheduling. In: 16th Swiss transport research conference (STRC), May 2016. HDL: 20.500.11850/117003 (cited on page 270)

    Google Scholar 

  35. Törnquist J (2006) Computer-based decision support for railway traffic scheduling and dispatching: a review of models and algorithms. In: Kroon LG, Möhring RH (eds) Proceedings of the 5th workshop on algorithmic methods and models for optimization of railways (ATMOS’05). OpenAccess series in informatics (OASIcs), vol 2. Schloss Dagstuhl, Wadern. ISBN: 978-3-939897-00-2. https://doi.org/10.4230/OASIcs.ATMOS.2005.659 (cited on page 269)

  36. Törnquist J (2007) Railway traffic disturbance management: an experimental analysis of disturbance complexity, management objectives and limitations in planning horizon. Transp Res A Policy Pract 41(3):249–266. https://doi.org/10.1016/j.tra.2006.05.003 (cited on page 275)

    Article  Google Scholar 

  37. Zhan S, Kroon LG, Veelenturf LP, Wagenaar JC (2015) Real-time high-speed train rescheduling in case of a complete blockage. Transp Res B Methodol 78:182–201. https://doi.org/10.1016/j.trb.2015.04.001 (cited on page 275)

    Article  Google Scholar 

  38. Zhan S, Kroon LG, Zhao J, Peng Q (2016) A rolling horizon approach to the high speed train rescheduling problem in case of a partial segment blockage. Transp Res E Log Transp Rev 95:32–61. https://doi.org/10.1016/j.tre.2016.07.015 (cited on pages 275, 277)

Download references

Author information

Authors and Affiliations

  1. Optrail, Rome, Italy

    Leonardo Lamorgese

  2. SINTEF DIGITAL & University of Oslo, Oslo, Norway

    Carlo Mannino

  3. Università degli Studi Roma Tre, Dipartimento di Ingegneria, Rome, Italy

    Dario Pacciarelli

  4. BTH - Blekinge Institute of Technology, Karlskrona, Sweden

    Johanna Törnquist Krasemann

Authors
  1. Leonardo Lamorgese
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carlo Mannino
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dario Pacciarelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Johanna Törnquist Krasemann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Johanna Törnquist Krasemann .

Editor information

Editors and Affiliations

  1. Zuse Institute Berlin & Freie Universität Berlin, Berlin, Germany

    Ralf Borndörfer

  2. Department of Optimization, Zuse Institute Berlin, Berlin, Germany

    Torsten Klug

  3. Optrail, Rome, Italy

    Leonardo Lamorgese

  4. SINTEF DIGITAL & University of Oslo, Oslo, Norway

    Carlo Mannino

  5. LBW Optimization GmbH & Zuse Institute Berlin, Berlin, Germany

    Markus Reuther

  6. LBW Optimization GmbH & Zuse Institute Berlin, Berlin, Germany

    Thomas Schlechte

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Lamorgese, L., Mannino, C., Pacciarelli, D., Krasemann, J.T. (2018). Train Dispatching. In: Borndörfer, R., Klug, T., Lamorgese, L., Mannino, C., Reuther, M., Schlechte, T. (eds) Handbook of Optimization in the Railway Industry. International Series in Operations Research & Management Science, vol 268. Springer, Cham. https://doi.org/10.1007/978-3-319-72153-8_12

Download citation

Publish with us

Policies and ethics

Search

Navigation