IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i5p2210-d1079754.html
   My bibliography  Save this article

Modelling the Operation Process of Light Utility Vehicles in Transport Systems Using Monte Carlo Simulation and Semi-Markov Approach

Author

Listed:
  • Mateusz Oszczypała

    (Institute of Mechanics and Computational Engineering, Faculty of Mechanical Engineering, Military University of Technology, Gen. Sylwestra Kaliskiego Street 2, 00-908 Warsaw, Poland)

  • Jarosław Ziółkowski

    (Institute of Mechanics and Computational Engineering, Faculty of Mechanical Engineering, Military University of Technology, Gen. Sylwestra Kaliskiego Street 2, 00-908 Warsaw, Poland)

  • Jerzy Małachowski

    (Institute of Mechanics and Computational Engineering, Faculty of Mechanical Engineering, Military University of Technology, Gen. Sylwestra Kaliskiego Street 2, 00-908 Warsaw, Poland)

Abstract

This research paper presents studies on the operation process of the Honker 2000 light utility vehicles that are part of the Polish Armed Forces transport system. The phase space of the process was identified based on the assumption that at any given moment the vehicle remains in one of four states, namely, task execution, awaiting a transport task, periodic maintenance, or repair. Vehicle functional readiness and technical suitability indices were adopted as performance measures for the technical system. A simulation model based on Monte Carlo methods was developed to determine the changes in the operational states. The occurrence of the periodic maintenance state is strictly determined by a planned and preventive strategy of operation applied within the analysed system. Other states are implementations of stochastic processes. The original source code was developed in the MATLAB environment to implement the model. Based on estimated probabilistic characteristics, the authors validated 16 simulation models resulting from all possible cumulative distribution functions (CDFs) that satisfied the condition of a proper match to empirical data. Based on the simulated operation process for a sample of 19 vehicles over the assumed 20-year forecast horizon, it was possible to determine the functional readiness and technical suitability indices. The relative differences between the results of all simulation models and the results obtained through the semi-Markov model did not exceed 6%. The best-fit model was subjected to sensitivity analysis in terms of the dependence between functional readiness and technical suitability indices on vehicle operation intensity. As a result, the proposed simulation system based on Monte Carlo methods turned out to be a useful tool in analysing the current operation process of means of transport in terms of forecasts related to a current environment, as well as when attempting its extrapolation.

Suggested Citation

  • Mateusz Oszczypała & Jarosław Ziółkowski & Jerzy Małachowski, 2023. "Modelling the Operation Process of Light Utility Vehicles in Transport Systems Using Monte Carlo Simulation and Semi-Markov Approach," Energies, MDPI, vol. 16(5), pages 1-31, February.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2210-:d:1079754
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/5/2210/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/5/2210/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Redmer, Adam, 2009. "Optimisation of the exploitation period of individual vehicles in freight transportation companies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(6), pages 978-987, November.
    2. Wang, Wei & Wu, Zhiying & Xiong, Junlin & Xu, Yaofeng, 2018. "Redundancy optimization of cold-standby systems under periodic inspection and maintenance," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 394-402.
    3. Park, Minjae & Jung, Ki Mun & Park, Dong Ho, 2018. "Optimization of periodic preventive maintenance policy following the expiration of two-dimensional warranty," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 1-9.
    4. Li Zhao & Kun Li & Wu Zhao & Han-Chen Ke & Zhen Wang, 2022. "A Sticky Sampling and Markov State Transition Matrix Based Driving Cycle Construction Method for EV," Energies, MDPI, vol. 15(3), pages 1-19, January.
    5. Torii, André Jacomel & Novotny, Antonio André, 2021. "A priori error estimates for local reliability-based sensitivity analysis with Monte Carlo Simulation," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    6. Igor Betkier & Elżbieta Macioszek, 2022. "Characteristics of Parking Lots Located along Main Roads in Terms of Cargo Truck Requirements: A Case Study of Poland," Sustainability, MDPI, vol. 14(23), pages 1-21, November.
    7. Angus, John & Ding, Yujia, 2020. "On the ratio of current age to total life for null recurrent renewal processes," Statistics & Probability Letters, Elsevier, vol. 162(C).
    8. Shepero, Mahmoud & Munkhammar, Joakim, 2018. "Spatial Markov chain model for electric vehicle charging in cities using geographical information system (GIS) data," Applied Energy, Elsevier, vol. 231(C), pages 1089-1099.
    9. Marek Stawowy & Adam Rosiński & Mirosław Siergiejczyk & Krzysztof Perlicki, 2021. "Quality and Reliability-Exploitation Modeling of Power Supply Systems," Energies, MDPI, vol. 14(9), pages 1-16, May.
    10. Kallen, M.J., 2011. "Modelling imperfect maintenance and the reliability of complex systems using superposed renewal processes," Reliability Engineering and System Safety, Elsevier, vol. 96(6), pages 636-641.
    11. Iversen, Emil B. & Morales, Juan M. & Madsen, Henrik, 2014. "Optimal charging of an electric vehicle using a Markov decision process," Applied Energy, Elsevier, vol. 123(C), pages 1-12.
    12. Xiaobo Zhang & Zhenzhou Lu & Kai Cheng & Yanping Wang, 2020. "A novel reliability sensitivity analysis method based on directional sampling and Monte Carlo simulation," Journal of Risk and Reliability, , vol. 234(4), pages 622-635, August.
    13. Edward F. Brayer, 1957. "Calculating the Standard Error of a Proportion," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 6(1), pages 67-68, March.
    Full references (including those not matched with items on IDEAS)

    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. Mateusz Oszczypała & Jarosław Ziółkowski & Jerzy Małachowski, 2022. "Analysis of Light Utility Vehicle Readiness in Military Transportation Systems Using Markov and Semi-Markov Processes," Energies, MDPI, vol. 15(14), pages 1-24, July.
    2. Zhang, Fengxia & Shen, Jingyuan & Ma, Yizhong, 2020. "Optimal maintenance policy considering imperfect repairs and non-constant probabilities of inspection errors," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    3. Li, Xiaohui & Wang, Zhenpo & Zhang, Lei & Sun, Fengchun & Cui, Dingsong & Hecht, Christopher & Figgener, Jan & Sauer, Dirk Uwe, 2023. "Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview," Energy, Elsevier, vol. 268(C).
    4. Natascia Andrenacci & Roberto Ragona & Antonino Genovese, 2020. "Evaluation of the Instantaneous Power Demand of an Electric Charging Station in an Urban Scenario," Energies, MDPI, vol. 13(11), pages 1-19, May.
    5. Andrzej Żyluk & Mariusz Zieja & Justyna Tomaszewska & Mariusz Michalski & Krzysztof Kordys, 2022. "Service Life Prediction for Rotating Electrical Machines on Aircraft in Terms of Temperature Loads," Energies, MDPI, vol. 16(1), pages 1-15, December.
    6. Yagcitekin, Bunyamin & Uzunoglu, Mehmet, 2016. "A double-layer smart charging strategy of electric vehicles taking routing and charge scheduling into account," Applied Energy, Elsevier, vol. 167(C), pages 407-419.
    7. Jingrong Tan & Lin Chen, 2022. "Spatial Effect of Digital Economy on Particulate Matter 2.5 in the Process of Smart Cities: Evidence from Prefecture-Level Cities in China," IJERPH, MDPI, vol. 19(21), pages 1-20, November.
    8. Legros, Benjamin & Fransoo, Jan C., 2023. "Admission and pricing optimization of on-street parking with delivery bays," Other publications TiSEM 6d41ee5c-27dc-4d34-aff1-4, Tilburg University, School of Economics and Management.
    9. Julia Vopava & Christian Koczwara & Anna Traupmann & Thomas Kienberger, 2019. "Investigating the Impact of E-Mobility on the Electrical Power Grid Using a Simplified Grid Modelling Approach," Energies, MDPI, vol. 13(1), pages 1-23, December.
    10. Kuang, Yanqing & Chen, Yang & Hu, Mengqi & Yang, Dong, 2017. "Influence analysis of driver behavior and building category on economic performance of electric vehicle to grid and building integration," Applied Energy, Elsevier, vol. 207(C), pages 427-437.
    11. Mellal, Mohamed Arezki & Zio, Enrico, 2020. "System reliability-redundancy optimization with cold-standby strategy by an enhanced nest cuckoo optimization algorithm," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    12. Asadi, Amin & Nurre Pinkley, Sarah, 2021. "A stochastic scheduling, allocation, and inventory replenishment problem for battery swap stations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 146(C).
    13. Marek Stawowy & Adam Rosiński & Jacek Paś & Stanisław Duer & Marta Harničárová & Krzysztof Perlicki, 2023. "The Reliability and Exploitation Analysis Method of the ICT System Power Supply with the Use of Modelling Based on Rough Sets," Energies, MDPI, vol. 16(12), pages 1-18, June.
    14. Lin, Boliang & Zhao, Yinan, 2021. "Synchronized optimization of EMU train assignment and second-level preventive maintenance scheduling," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    15. Mitra, Amitava, 2021. "Warranty parameters for extended two-dimensional warranties incorporating consumer preferences," European Journal of Operational Research, Elsevier, vol. 291(2), pages 525-535.
    16. Wu, Hui & Li, Yan-Fu & Bérenguer, Christophe, 2020. "Optimal inspection and maintenance for a repairable k-out-of-n: G warm standby system," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    17. García-Villalobos, J. & Zamora, I. & Knezović, K. & Marinelli, M., 2016. "Multi-objective optimization control of plug-in electric vehicles in low voltage distribution networks," Applied Energy, Elsevier, vol. 180(C), pages 155-168.
    18. Milad Akbari & Morris Brenna & Michela Longo, 2018. "Optimal Locating of Electric Vehicle Charging Stations by Application of Genetic Algorithm," Sustainability, MDPI, vol. 10(4), pages 1-14, April.
    19. Schücking, Maximilian & Jochem, Patrick & Fichtner, Wolf & Wollersheim, Olaf & Stella, Kevin, 2017. "Charging strategies for economic operations of electric vehicles in commercial applications," MPRA Paper 91599, University Library of Munich, Germany.
    20. Xiang, Yue & Liu, Junyong & Li, Ran & Li, Furong & Gu, Chenghong & Tang, Shuoya, 2016. "Economic planning of electric vehicle charging stations considering traffic constraints and load profile templates," Applied Energy, Elsevier, vol. 178(C), pages 647-659.

    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:jeners:v:16:y:2023:i:5:p:2210-:d:1079754. 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.