IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i21p8915-d435459.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/21/8915/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/21/8915/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. Cacchiani, Valentina & Toth, Paolo, 2012. "Nominal and robust train timetabling problems," European Journal of Operational Research, Elsevier, vol. 219(3), pages 727-737.
    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. 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.
    9. Laurent Guihéry, 2014. "Competition in regional passenger rail transport in Germany (Leipzig) and lessons to be drawn for France," Post-Print halshs-01474047, HAL.
    10. 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.
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cacchiani, Valentina & Furini, Fabio & Kidd, Martin Philip, 2016. "Approaches to a real-world Train Timetabling Problem in a railway node," Omega, Elsevier, vol. 58(C), pages 97-110.
    2. Wang, Dian & D’Ariano, Andrea & Zhao, Jun & Zhong, Qingwei & Peng, Qiyuan, 2022. "Integrated rolling stock deadhead routing and timetabling in urban rail transit lines," European Journal of Operational Research, Elsevier, vol. 298(2), pages 526-559.
    3. Zhang, Yongxiang & Peng, Qiyuan & Yao, Yu & Zhang, Xin & Zhou, Xuesong, 2019. "Solving cyclic train timetabling problem through model reformulation: Extended time-space network construct and Alternating Direction Method of Multipliers methods," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 344-379.
    4. Xiaoming Xu & Keping Li & Lixing Yang & Ziyou Gao, 2019. "An efficient train scheduling algorithm on a single-track railway system," Journal of Scheduling, Springer, vol. 22(1), pages 85-105, February.
    5. Mo, Pengli & D’Ariano, Andrea & Yang, Lixing & Veelenturf, Lucas P. & Gao, Ziyou, 2021. "An exact method for the integrated optimization of subway lines operation strategies with asymmetric passenger demand and operating costs," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 283-321.
    6. David Schindl & Nicolas Zufferey, 2015. "A learning tabu search for a truck allocation problem with linear and nonlinear cost components," Naval Research Logistics (NRL), John Wiley & Sons, vol. 62(1), pages 32-45, February.
    7. Bach, Lukas & Gendreau, Michel & Wøhlk, Sanne, 2015. "Freight railway operator timetabling and engine scheduling," European Journal of Operational Research, Elsevier, vol. 241(2), pages 309-319.
    8. Harrod, Steven & Schlechte, Thomas, 2013. "A direct comparison of physical block occupancy versus timed block occupancy in train timetabling formulations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 54(C), pages 50-66.
    9. Zhou, Leishan & Tong, Lu (Carol) & Chen, Junhua & Tang, Jinjin & Zhou, Xuesong, 2017. "Joint optimization of high-speed train timetables and speed profiles: A unified modeling approach using space-time-speed grid networks," Transportation Research Part B: Methodological, Elsevier, vol. 97(C), pages 157-181.
    10. Jiang, Feng & Cacchiani, Valentina & Toth, Paolo, 2017. "Train timetabling by skip-stop planning in highly congested lines," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 149-174.
    11. Martin-Iradi, Bernardo & Ropke, Stefan, 2022. "A column-generation-based matheuristic for periodic and symmetric train timetabling with integrated passenger routing," European Journal of Operational Research, Elsevier, vol. 297(2), pages 511-531.
    12. Kang, Liujiang & Meng, Qiang, 2017. "Two-phase decomposition method for the last train departure time choice in subway networks," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 568-582.
    13. Yang, Xin & Chen, Anthony & Ning, Bin & Tang, Tao, 2017. "Bi-objective programming approach for solving the metro timetable optimization problem with dwell time uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 97(C), pages 22-37.
    14. Kang, Liujiang & Zhu, Xiaoning & Sun, Huijun & Puchinger, Jakob & Ruthmair, Mario & Hu, Bin, 2016. "Modeling the first train timetabling problem with minimal missed trains and synchronization time differences in subway networks," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 17-36.
    15. Cacchiani, Valentina & Toth, Paolo, 2012. "Nominal and robust train timetabling problems," European Journal of Operational Research, Elsevier, vol. 219(3), pages 727-737.
    16. Pellegrini, Paola & Rodriguez, Joaquin, 2013. "Single European Sky and Single European Railway Area: A system level analysis of air and rail transportation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 57(C), pages 64-86.
    17. Barrena, Eva & Canca, David & Coelho, Leandro C. & Laporte, Gilbert, 2014. "Single-line rail rapid transit timetabling under dynamic passenger demand," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 134-150.
    18. Yin, Jiateng & D’Ariano, Andrea & Wang, Yihui & Yang, Lixing & Tang, Tao, 2021. "Timetable coordination in a rail transit network with time-dependent passenger demand," European Journal of Operational Research, Elsevier, vol. 295(1), pages 183-202.
    19. Ralf Borndörfer & Torsten Klug & Thomas Schlechte & Armin Fügenschuh & Thilo Schang & Hanno Schülldorf, 2016. "The Freight Train Routing Problem for Congested Railway Networks with Mixed Traffic," Transportation Science, INFORMS, vol. 50(2), pages 408-423, May.
    20. Jiateng Yin & Lixing Yang & Xuesong Zhou & Tao Tang & Ziyou Gao, 2019. "Balancing a one‐way corridor capacity and safety‐oriented reliability: A stochastic optimization approach for metro train timetabling," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(4), pages 297-320, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:21:p:8915-:d:435459. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.