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An Interactive Multi-Criteria Decision-Making Approach for Autonomous Vehicles and Distributed Resources Based on Logistic Systems: Challenges for a Sustainable Future

Author

Listed:
  • Abduallah Gamal

    (Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt)

  • Mohamed Abdel-Basset

    (Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt)

  • Ibrahim M. Hezam

    (Department of Statistics & Operations Research, College of Sciences, King Saud University, Riyadh 11451, Saudi Arabia)

  • Karam M. Sallam

    (Faculty of Science and Technology, School of IT and Systems, University of Canberra, Canberra, ACT 2601, Australia)

  • Ibrahim A. Hameed

    (Department of ICT and Natural Sciences, Norwegian University of Science and Technology (NTNU), 7034 Ålesund, Norway)

Abstract

The autonomous vehicle (AV) is one of the emerging technologies of the new age that has the potential to restructure transportation infrastructure. AVs are able to sense their surroundings and move around with control and self-sufficiency. AVs can contribute towards reducing traffic congestion on the roads, improving the quality of life, and achieving the highest levels of traffic safety. Thus, this type of vehicle can be integrated into the logistics industry. Due to the presence of several AVs, selecting a standard and efficient AV for logistics planning is a great challenge. The selection of an AV depends on many conflicting and essential criteria. Given its efficiency and reliability in dealing with conflicting criteria, a comprehensive multi-criteria decision-making (MCDM) approach was applied to solve the problem of selecting the optimal AV. However, the MCDM selection process is based on human judgment, which can be ambiguous. Accordingly, uncertainty was handled using type-2 neutrosophic numbers (T2NN). Initially, the method based on the removal effects of criteria (MEREC) was extended under T2NN and employed to assess and prioritize criteria. Then, the combined compromise solution (CoCoSo) method was extended under T2NN and applied to rank the candidate substitutions. To confirm the feasibility of the applied approach, an illustrative case study of four AVs was introduced. A sensitivity analysis was performed by changing the weights of the criteria and some other parameters to confirm the validity and stability of the proposed approach. In addition, a comparison analysis with other MCDM approaches was conducted to show the effectiveness and reliability of the applied approach. This research provides useful information for policymakers in the field of logistics. Finally, the results indicate that the velocity of AVs criterion is the most influential criterion in the selection of an intelligent AV.

Suggested Citation

  • Abduallah Gamal & Mohamed Abdel-Basset & Ibrahim M. Hezam & Karam M. Sallam & Ibrahim A. Hameed, 2023. "An Interactive Multi-Criteria Decision-Making Approach for Autonomous Vehicles and Distributed Resources Based on Logistic Systems: Challenges for a Sustainable Future," Sustainability, MDPI, vol. 15(17), pages 1-26, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12844-:d:1224634
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    References listed on IDEAS

    as
    1. Mladen Krstić & Giulio Paolo Agnusdei & Pier Paolo Miglietta & Snežana Tadić & Violeta Roso, 2022. "Applicability of Industry 4.0 Technologies in the Reverse Logistics: A Circular Economy Approach Based on COmprehensive Distance Based RAnking (COBRA) Method," Sustainability, MDPI, vol. 14(9), pages 1-30, May.
    2. Morteza Yazdani & Pascale Zaraté & Edmundas Kazimieras Zavadskas & Zenonas Turskis, 2019. "A Combined Compromise Solution (CoCoSo) method for multi-criteria decision-making problems," Post-Print hal-02879091, HAL.
    3. Raj, Alok & Kumar, J. Ajith & Bansal, Prateek, 2020. "A multicriteria decision making approach to study barriers to the adoption of autonomous vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 122-137.
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