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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
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    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    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.
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