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Information System and Technology Optimization as a Tool for Ensuring the Competitiveness of a Railway Undertaking—Case Study

Author

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  • Juraj Čamaj

    (Department of Railway Transport, Faculty of Operation and Economics of Transport and Communications, University of Zilina, 010 26 Žilina, Slovakia)

  • Eva Brumerčíková

    (Department of Railway Transport, Faculty of Operation and Economics of Transport and Communications, University of Zilina, 010 26 Žilina, Slovakia)

  • Michal Petr Hranický

    (Department of Railway Transport, Faculty of Operation and Economics of Transport and Communications, University of Zilina, 010 26 Žilina, Slovakia)

Abstract

Information and communication technologies are becoming an increasingly important part of everyday life, as they facilitate many activities, mainly in the world of work, but also in scientific research and education. At present, informatics is one of the fastest growing sectors of the national economy. This development has had a significant impact on improving the quality of transport and transportation processes. The article is focused on the railway transport. It deals with the possibilities of planning the shifts of the train personnel and circulation of the vehicles. It describes the background of the topic. The scientific acquittance lies on the methodology proposed by authors. It presents a new idea of creating the shifts and circulations while being based on the current state and mathematical methods.

Suggested Citation

  • Juraj Čamaj & Eva Brumerčíková & Michal Petr Hranický, 2020. "Information System and Technology Optimization as a Tool for Ensuring the Competitiveness of a Railway Undertaking—Case Study," Sustainability, MDPI, vol. 12(21), pages 1-23, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:21:p:8915-:d:435459
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    References listed on IDEAS

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    1. Eva Nedeliaková & Vladimíra Stefancová & Adrián Kuka, 2018. "Innovative Methodology For Quality And Risk Management In Logistics Processes Of Transport Undertakings," Business Logistics in Modern Management, Josip Juraj Strossmayer University of Osijek, Faculty of Economics, Croatia, vol. 18, pages 41-53.
    2. Laurent Guihéry, 2014. "Competition in regional passenger rail transport in Germany (Leipzig) and lessons to be drawn for France," Post-Print halshs-01474047, HAL.
    3. Dennis Huisman & Leo G. Kroon & Ramon M. Lentink & Michiel J. C. M. Vromans, 2005. "Operations Research in passenger railway transportation," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 59(4), pages 467-497, November.
    4. Niklas Johansson & Eva Roth & Wiebke Reim, 2019. "Smart and Sustainable eMaintenance: Capabilities for Digitalization of Maintenance," Sustainability, MDPI, vol. 11(13), pages 1-19, June.
    5. Minwu Chen & Yinyu Chen & Mingchi Wei, 2019. "Modeling and Control of a Novel Hybrid Power Quality Compensation System for 25-kV Electrified Railway," Energies, MDPI, vol. 12(17), pages 1-23, August.
    6. Jean-François Cordeau & Paolo Toth & Daniele Vigo, 1998. "A Survey of Optimization Models for Train Routing and Scheduling," Transportation Science, INFORMS, vol. 32(4), pages 380-404, November.
    7. Alberto Caprara & Matteo Fischetti & Paolo Toth, 2002. "Modeling and Solving the Train Timetabling Problem," Operations Research, INFORMS, vol. 50(5), pages 851-861, October.
    8. Cacchiani, Valentina & Toth, Paolo, 2012. "Nominal and robust train timetabling problems," European Journal of Operational Research, Elsevier, vol. 219(3), pages 727-737.
    9. Guihéry, Laurent, 2014. "Competition in regional passenger rail transport in Germany (Leipzig) and lessons to be drawn for France," Research in Transportation Economics, Elsevier, vol. 48(C), pages 298-304.
    10. Eva Nedeliakova & Maria Hudakova & Matej Masar & Lenka Lizbetinova & Renata Stasiak-Betlejewska & Peter Šulko, 2020. "Sustainability of Railway Undertaking Services with Lean Philosophy in Risk Management—Case Study," Sustainability, MDPI, vol. 12(13), pages 1-28, June.
    11. Kah Huo Leong & Hamzah Abdul-Rahman & Chen Wang & Chiu Chuen Onn & Siaw-Chuing Loo, 2016. "Bee Inspired Novel Optimization Algorithm and Mathematical Model for Effective and Efficient Route Planning in Railway System," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-24, December.
    12. Zimny-Schmitt, Daniel & Goetz, Andrew R., 2020. "An investigation of the performance of urban rail transit systems on the corridor level: A comparative analysis in the American west," Journal of Transport Geography, Elsevier, vol. 88(C).
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    1. László Erdei & Péter Tamás & Béla Illés, 2023. "Improving the Efficiency of Rail Passenger Transportation Using an Innovative Operational Concept," Sustainability, MDPI, vol. 15(6), pages 1-23, March.
    2. Katarzyna Łukiewska & Małgorzata Juchniewicz, 2021. "Identification of the Relationships between Competitive Potential and Competitive Position of the Food Industry in the European Union," Sustainability, MDPI, vol. 13(8), pages 1-13, April.

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