IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i5p4179-d1080439.html
   My bibliography  Save this article

Fuzzy-Logic Approach to Estimating the Fleet Efficiency of a Road Transport Company: A Case Study of Agricultural Products Deliveries in Kazakhstan

Author

Listed:
  • Igor Taran

    (Department of Roads and Bridges, Rzeszow University of Technology, Powstańców Warszawy Ave. 12, 35959 Rzeszow, Poland)

  • Asem Karsybayeva

    (Faculty of Logistics and Management, Academy of Logistics and Transport, Shevchenko Str. 97, Almaty 050022, Kazakhstan)

  • Vitalii Naumov

    (Faculty of Civil Engineering, Cracow University of Technology, Warszawska Str. 24, 31155 Krakow, Poland)

  • Kenzhegul Murzabekova

    (Faculty of Logistics and Management, Academy of Logistics and Transport, Shevchenko Str. 97, Almaty 050022, Kazakhstan)

  • Marzhan Chazhabayeva

    (Faculty of Engineering, Yessenov University, Microdistrict 24, Building 2, Aktau 130000, Kazakhstan)

Abstract

The estimation of the efficiency of road transport vehicles remains a significant problem for contemporary transport companies, as numerous stochastic impacts, such as demand stochasticity, road conditions uncertainty, transport market fluctuations, etc., influence the technological process. A fuzzy-logic approach is proposed to consider the uncertainty relating to estimating vehicle fleet efficiency. According to the developed approach, vehicle efficiency is described based on a membership function, whereas the efficiency of the whole vehicle fleet is evaluated as a fuzzy set. To demonstrate the developed approach, a case study is depicted for using cargo vehicles to deliver agricultural products in the Republic of Kazakhstan. The numeric results are presented for the selected models of vehicles that a transport company uses to service a set of clients located in Northern Kazakhstan: the transport services provided for each of the clients are characterized by numeric demand parameters—the consignment weight and the delivery distance. The completed calculations allowed us to obtain the membership functions for the alternative vehicle models and to present the transport company’s vehicle fleet as a fuzzy set.

Suggested Citation

  • Igor Taran & Asem Karsybayeva & Vitalii Naumov & Kenzhegul Murzabekova & Marzhan Chazhabayeva, 2023. "Fuzzy-Logic Approach to Estimating the Fleet Efficiency of a Road Transport Company: A Case Study of Agricultural Products Deliveries in Kazakhstan," Sustainability, MDPI, vol. 15(5), pages 1-14, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4179-:d:1080439
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/5/4179/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/5/4179/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andrés, Lidia & Padilla, Emilio, 2015. "Energy intensity in road freight transport of heavy goods vehicles in Spain," Energy Policy, Elsevier, vol. 85(C), pages 309-321.
    2. Fang, Da & Guo, Yan, 2022. "Flow of goods to the shock of COVID-19 and toll-free highway policy: Evidence from logistics data in China," Research in Transportation Economics, Elsevier, vol. 93(C).
    3. Petering, Matthew E.H., 2011. "Decision support for yard capacity, fleet composition, truck substitutability, and scalability issues at seaport container terminals," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(1), pages 85-103, January.
    4. Abate, Megersa & de Jong, Gerard, 2014. "The optimal shipment size and truck size choice – The allocation of trucks across hauls," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 262-277.
    5. Viri, Riku & Mäkinen, Johanna & Liimatainen, Heikki, 2021. "Modelling car fleet renewal in Finland: A model and development speed-based scenarios," Transport Policy, Elsevier, vol. 112(C), pages 63-79.
    6. Llopis-Albert, Carlos & Rubio, Francisco & Valero, Francisco, 2019. "Fuzzy-set qualitative comparative analysis applied to the design of a network flow of automated guided vehicles for improving business productivity," Journal of Business Research, Elsevier, vol. 101(C), pages 737-742.
    7. Militão, Aitan M. & Tirachini, Alejandro, 2021. "Optimal fleet size for a shared demand-responsive transport system with human-driven vs automated vehicles: A total cost minimization approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 151(C), pages 52-80.
    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. Wang, Shuaian & Meng, Qiang, 2012. "Liner ship route schedule design with sea contingency time and port time uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 46(5), pages 615-633.
    2. Jing Bai & Chuang Tu & Jiming Bai, 2024. "Measuring and decomposing Beijing’s energy performance: an energy- and exergy-based perspective," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(7), pages 17617-17633, July.
    3. Román-Collado, Rocío & Colinet, María José, 2018. "Are labour productivity and residential living standards drivers of the energy consumption changes?," Energy Economics, Elsevier, vol. 74(C), pages 746-756.
    4. Usman Ahmed & Matthew J. Roorda, 2023. "Joint and sequential models for freight vehicle type and shipment size choice," Transportation, Springer, vol. 50(5), pages 1613-1629, October.
    5. Matthew E. H. Petering & Yong Wu & Wenkai Li & Mark Goh & Robert Souza & Katta G. Murty, 2017. "Real-time container storage location assignment at a seaport container transshipment terminal: dispersion levels, yard templates, and sensitivity analyses," Flexible Services and Manufacturing Journal, Springer, vol. 29(3), pages 369-402, December.
    6. Rubio, Francisco & Llopis-Albert, Carlos & Valero, Francisco, 2021. "Multi-objective optimization of costs and energy efficiency associated with autonomous industrial processes for sustainable growth," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    7. Pani, Agnivesh & Mishra, Sabya & Sahu, Prasanta, 2022. "Developing multi-vehicle freight trip generation models quantifying the relationship between logistics outsourcing and insourcing decisions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).
    8. Hatzenbühler, Jonas & Jenelius, Erik & Gidófalvi, Gyözö & Cats, Oded, 2023. "Modular vehicle routing for combined passenger and freight transport," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    9. Jan Blachowski & Anna Buczyńska, 2020. "Analysis of Rock Raw Materials Transport and its Implications for Regional Development and Planning. Case Study of Lower Silesia (Poland)," Sustainability, MDPI, vol. 12(8), pages 1-14, April.
    10. Baihui Jin & Wei Li, 2023. "External Factors Impacting Residents’ Participation in Waste Sorting Using NCA and fsQCA Methods on Pilot Cities in China," IJERPH, MDPI, vol. 20(5), pages 1-21, February.
    11. Megersa Abate & Inge Vierth & Rune Karlsson & Gerard Jong & Jaap Baak, 2019. "A disaggregate stochastic freight transport model for Sweden," Transportation, Springer, vol. 46(3), pages 671-696, June.
    12. Gharehgozli, A.H. & Roy, D. & de Koster, M.B.M., 2014. "Sea Container Terminals," ERIM Report Series Research in Management ERS-2014-009-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    13. Hartmann, Sönke, 2013. "Scheduling reefer mechanics at container terminals," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 51(C), pages 17-27.
    14. Paweł Piotrowski & Dariusz Baczyński & Marcin Kopyt, 2022. "Medium-Term Forecasts of Load Profiles in Polish Power System including E-Mobility Development," Energies, MDPI, vol. 15(15), pages 1-27, August.
    15. Jiayi Li & Zhaocheng He & Jiaming Zhong, 2022. "The Multi-Type Demands Oriented Framework for Flex-Route Transit Design," Sustainability, MDPI, vol. 14(15), pages 1-23, August.
    16. Wang, Juan & Hu, Mingming & Rodrigues, João F.D., 2018. "The evolution and driving forces of industrial aggregate energy intensity in China: An extended decomposition analysis," Applied Energy, Elsevier, vol. 228(C), pages 2195-2206.
    17. Yang, Yang & Liu, Qing & Chang, Chia-Hsun, 2023. "China-Europe freight transportation under the first wave of COVID-19 pandemic and government restriction measures," Research in Transportation Economics, Elsevier, vol. 97(C).
    18. Debjit Roy & René De Koster & René Bekker, 2020. "Modeling and Design of Container Terminal Operations," Operations Research, INFORMS, vol. 68(3), pages 686-715, May.
    19. Peter Shobayo & Edwin van Hassel, 2019. "Container barge congestion and handling in large seaports: a theoretical agent-based modeling approach," Journal of Shipping and Trade, Springer, vol. 4(1), pages 1-26, December.
    20. Edyta Sidorczuk-Pietraszko, 2020. "Spatial Differences in Carbon Intensity in Polish Households," Energies, MDPI, vol. 13(12), pages 1-21, 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:15:y:2023:i:5:p:4179-:d:1080439. 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.