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[1] Prigogine, I., Is Future Given?, Singapore: World Scientific Publishing Co., 2003.
[2] Prigogine, I., The End of Certainty: Time, Chaos and the New Laws of Nature, NewYork: Free Press, 1997.
[3] Kaufman, S., At Home in the Universe: The Search for the Laws of Self-Organizationand Complexity, Oxford, NY: Oxford Press, 1995.
[4] Holland, J., Emergence: from Chaos to Order, Oxford, NY: Oxford University Press,1998.
[5] Rzevski, G. & Skobelev, P., Managing Complexity, WIT Press: Southampton, Boston,2014.
[6] Rzevski, G., Soloviev, V., Skobelev, P., & Lakhin, O., Complex adaptive logistics forthe international space station. International Journal of Design & Nature and Ecodynamics,11(3), pp. 459-472, 2016. [Crossref]
[7] Rzevski, G., Knezevic, J., Skobelev, P., Borgest, N. & Lakhin, O., Managing aircraftlifecycle complexity. International Journal of Design & Nature and Ecodynamics,11(2), pp. 77–87, 2016. [Crossref]
[8] Madsen, B., Rzevski, G., Skobelev, P., & Tsarev, A., A strategy for managing complexityof the global market and prototype real-time scheduler for LEGO supply chain,International Journal of Software Innovation, 1(2), pp. 28–39, 2013. [Crossref]
[9] Glaschenko, A., Ivaschenko, A., Rzevski, G. & Skobelev, P., Multi-agent real timescheduling system for taxi companies. Proceedings of 8th International Conferenceon Autonomous Agents and Multiagent Systems (AAMAS 2009), eds. Decker, Sichman,Sierra, and Castelfranchi, Budapest, Hungary, May, 10–15, 2009,
[10] Andreev, S., Rzevski, G., Shveykin, P., Skobelev, P. & Yankov, I., Multi-agent schedulerfor rent-a-car companies. Lecture Notes in Computer Science, Volume 5696, Holonicand Multi-Agent Systems for Manufacturing: Forth International Conference onIndustrialApplications of Holonic and Multi-Agent Systems, HoloMAS 2009, Springer:Linz, Austria, pp. 305–314.
[11] Tormos, P., Lova, A., Barber, F., Ingolotti, L., Abril, M. & Salido, M. A., A genetic algorithmfor railway scheduling problems. Studies in Computational Intelligence (SCI),128, pp. 255–276, 2008. [Crossref]
[12] Cai, X. & Goh, C. J., A fast heuristics for the train scheduling problem. Computers andOperations Research, 50, pp. 738–753, 2002.
[13] Caprara, A., Monaci, M., Toth, P. & Gvida, P., A Lagrangian heuristic algorithm forreal-world train timetabling problem. Discrete Applied Mathematics, 154, pp. 738–753,2006. [Crossref]
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Open Access
Research article

Intelligent Adaptive Schedulers for Railways

G. Rzevski1,
P. Skobelev2
1
The Open University, UK and Multi-Agent Technology Ltd, UK
2
Samara University and Smart Solutions Ltd, Samara, Russia
International Journal of Transport Development and Integration
|
Volume 1, Issue 3, 2017
|
Pages 414-420
Received: N/A,
Revised: N/A,
Accepted: N/A,
Available online: 04-29-2017
View Full Article|Download PDF

Abstract:

Railway operation can be perceived as a complex system consisting of thousands of constituent elements, including demands for transportation and a variety of transportation resources, such as tracks, track blocks, stations, sidings, trains, crews, etc. Overall behaviour of a railway operation is characterized by unpredictable disruptive events such as changes in availability of resources due to failures, weather conditions or human errors. In addition transportation demands tend to change over time whilst changing transportation resources, such as tracks, is not always possible or practical. The key task of railway management is the allocation of transportation resources to transportation demands with a goal of achieving a complete match, ensuring a smooth operation. The difficulty of this task primarily depends on the variability of demand and reliability of resources, in particular, tracks, trains and human resources. The paper describes how to design complex adaptive railway schedulers, which allocate resources to demands in real time and ensure rapid rescheduling in reaction to unpredictable disruptive events.

Keywords: adaptability, complexity, railway schedules, real-time schedulers, self-organization
Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References
[1] Prigogine, I., Is Future Given?, Singapore: World Scientific Publishing Co., 2003.
[2] Prigogine, I., The End of Certainty: Time, Chaos and the New Laws of Nature, NewYork: Free Press, 1997.
[3] Kaufman, S., At Home in the Universe: The Search for the Laws of Self-Organizationand Complexity, Oxford, NY: Oxford Press, 1995.
[4] Holland, J., Emergence: from Chaos to Order, Oxford, NY: Oxford University Press,1998.
[5] Rzevski, G. & Skobelev, P., Managing Complexity, WIT Press: Southampton, Boston,2014.
[6] Rzevski, G., Soloviev, V., Skobelev, P., & Lakhin, O., Complex adaptive logistics forthe international space station. International Journal of Design & Nature and Ecodynamics,11(3), pp. 459-472, 2016. [Crossref]
[7] Rzevski, G., Knezevic, J., Skobelev, P., Borgest, N. & Lakhin, O., Managing aircraftlifecycle complexity. International Journal of Design & Nature and Ecodynamics,11(2), pp. 77–87, 2016. [Crossref]
[8] Madsen, B., Rzevski, G., Skobelev, P., & Tsarev, A., A strategy for managing complexityof the global market and prototype real-time scheduler for LEGO supply chain,International Journal of Software Innovation, 1(2), pp. 28–39, 2013. [Crossref]
[9] Glaschenko, A., Ivaschenko, A., Rzevski, G. & Skobelev, P., Multi-agent real timescheduling system for taxi companies. Proceedings of 8th International Conferenceon Autonomous Agents and Multiagent Systems (AAMAS 2009), eds. Decker, Sichman,Sierra, and Castelfranchi, Budapest, Hungary, May, 10–15, 2009,
[10] Andreev, S., Rzevski, G., Shveykin, P., Skobelev, P. & Yankov, I., Multi-agent schedulerfor rent-a-car companies. Lecture Notes in Computer Science, Volume 5696, Holonicand Multi-Agent Systems for Manufacturing: Forth International Conference onIndustrialApplications of Holonic and Multi-Agent Systems, HoloMAS 2009, Springer:Linz, Austria, pp. 305–314.
[11] Tormos, P., Lova, A., Barber, F., Ingolotti, L., Abril, M. & Salido, M. A., A genetic algorithmfor railway scheduling problems. Studies in Computational Intelligence (SCI),128, pp. 255–276, 2008. [Crossref]
[12] Cai, X. & Goh, C. J., A fast heuristics for the train scheduling problem. Computers andOperations Research, 50, pp. 738–753, 2002.
[13] Caprara, A., Monaci, M., Toth, P. & Gvida, P., A Lagrangian heuristic algorithm forreal-world train timetabling problem. Discrete Applied Mathematics, 154, pp. 738–753,2006. [Crossref]
Cite this:
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Rzevski, G. & Skobelev, P. (2017). Intelligent Adaptive Schedulers for Railways. Int. J. Transp. Dev. Integr., 1(3), 414-420. https://doi.org/10.2495/TDI-V1-N3-414-420
G. Rzevski and P. Skobelev, "Intelligent Adaptive Schedulers for Railways," Int. J. Transp. Dev. Integr., vol. 1, no. 3, pp. 414-420, 2017. https://doi.org/10.2495/TDI-V1-N3-414-420
@research-article{Rzevski2017IntelligentAS,
title={Intelligent Adaptive Schedulers for Railways},
author={G. Rzevski and P. Skobelev},
journal={International Journal of Transport Development and Integration},
year={2017},
page={414-420},
doi={https://doi.org/10.2495/TDI-V1-N3-414-420}
}
G. Rzevski, et al. "Intelligent Adaptive Schedulers for Railways." International Journal of Transport Development and Integration, v 1, pp 414-420. doi: https://doi.org/10.2495/TDI-V1-N3-414-420
G. Rzevski and P. Skobelev. "Intelligent Adaptive Schedulers for Railways." International Journal of Transport Development and Integration, 1, (2017): 414-420. doi: https://doi.org/10.2495/TDI-V1-N3-414-420
RZEVSKI G， SKOBELEV P. Intelligent Adaptive Schedulers for Railways[J]. International Journal of Transport Development and Integration， 2017， 1(3)： 414-420. https://doi.org/10.2495/TDI-V1-N3-414-420