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A Decision-Making Model for Professional Drivers Selection: A Hybridized Fuzzy–AROMAN–Fuller Approach

Author

Listed:
  • Marjana Čubranić-Dobrodolac

    (Faculty of Transport and Traffic Engineering, University of Belgrade, 11000 Belgrade, Serbia)

  • Stefan Jovčić

    (Faculty of Transport Engineering, University of Pardubice, 532 10 Pardubice, Czech Republic)

  • Sara Bošković

    (Faculty of Transport Engineering, University of Pardubice, 532 10 Pardubice, Czech Republic)

  • Darko Babić

    (Faculty of Transport and Traffic Sciences, University of Zagreb, 10000 Zagreb, Croatia)

Abstract

Professional drivers play a crucial role in many businesses and the lives of people. They are responsible for transferring people and goods between distant points, enabling safe and efficient flows. The road traffic death rate is from 8.3 to 27.5 per 100,000 inhabitants in the countries globally. Because professional drivers spend a significant amount of time on the road, their appropriate selection may contribute to general traffic safety. In addition, an adequate selection of candidates significantly impacts the financial costs of the employing company. However, the recruitment procedure is a very complex task where multiple criteria should be considered. By its nature, this is a typical multi-criteria decision-making problem. The purpose of this paper is twofold: to contribute to the methodological, as well as to the professional field. Considering the professional, we propose a decision-making tool in the procedure of professional driver selection. There are several methodological contributions. By reviewing the literature, we identified 14 criteria for candidate selection. In the proposed model, by using expert opinion and implementing DEMATEL and Fuller’s pairwise comparisons, we ranked these criteria and determined the seven most important for further calculation procedure. Here, we introduced an original approach for measuring the reliability of obtained answers. Then, to rank the candidates, the fuzzy AROMAN approach is applied for the first time in the literature. The input data were obtained in the form of a survey, where the experts evaluated the importance of criteria and their interrelation. We used MS Excel and MATLAB for data processing. An additional methodological contribution of this study is an advancement of the AROMAN method by the proposal of an algorithm for the calculation of parameter λ used in the final ranking formula. To illustrate the applicability of the proposed model, a case study is provided. Based on the results, we can conclude that the new methodological approaches can be successfully used in the procedure of professional driver selection, as well as in solving other multi-criteria decision-making problems.

Suggested Citation

  • Marjana Čubranić-Dobrodolac & Stefan Jovčić & Sara Bošković & Darko Babić, 2023. "A Decision-Making Model for Professional Drivers Selection: A Hybridized Fuzzy–AROMAN–Fuller Approach," Mathematics, MDPI, vol. 11(13), pages 1-24, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2831-:d:1178142
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    References listed on IDEAS

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    1. Jurijus Zaranka & Robertas Pečeliūnas & Vidas Žuraulis, 2021. "A Road Safety-Based Selection Methodology for Professional Drivers: Behaviour and Accident Rate Analysis," IJERPH, MDPI, vol. 18(23), pages 1-18, November.
    2. Marjana Čubranić-Dobrodolac & Libor Švadlenka & Svetlana Čičević & Aleksandar Trifunović & Momčilo Dobrodolac, 2020. "Using the Interval Type-2 Fuzzy Inference Systems to Compare the Impact of Speed and Space Perception on the Occurrence of Road Traffic Accidents," Mathematics, MDPI, vol. 8(9), pages 1-19, September.
    3. Trond Nordfjærn & Stig Halvard Jørgensen & Torbjørn Rundmo, 2012. "Safety attitudes, behaviour, anxiety and perceived control among professional and non-professional drivers," Journal of Risk Research, Taylor & Francis Journals, vol. 15(8), pages 875-896, September.
    4. Milos Poliak & Lucia Svabova & Jan Benus & Ebru Demirci, 2022. "Driver Response Time and Age Impact on the Reaction Time of Drivers: A Driving Simulator Study among Professional-Truck Drivers," Mathematics, MDPI, vol. 10(9), pages 1-16, April.
    5. Denver M. Y. Brown & Amanda Farias Zuniga & Daanish M. Mulla & Divya Mendonca & Peter J. Keir & Steven R. Bray, 2021. "Investigating the Effects of Mental Fatigue on Resistance Exercise Performance," IJERPH, MDPI, vol. 18(13), pages 1-14, June.
    6. Najah Al-Garawi & Muhammad Abubakar Dalhat & Omer Aga, 2021. "Assessing the Road Traffic Crashes among Novice Female Drivers in Saudi Arabia," Sustainability, MDPI, vol. 13(15), pages 1-11, August.
    7. Ondrej STOPKA & Mária STOPKOVÁ & Vladimír ĽUPTÁK & Srećko KRILE, 2020. "Application Of The Chosen Multi-Criteria Decision-Making Methods To Identify The Autonomous Train System Supplier," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 15(2), pages 45-57, June.
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