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Social network multiple-criteria decision-making approach for evaluating unmanned ground delivery vehicles under the Pythagorean fuzzy environment

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  • Zeng, Shouzhen
  • Zhang, Na
  • Zhang, Chonghui
  • Su, Weihua
  • Carlos, Llopis-Albert

Abstract

With the rapid development of instant delivery, the shrinking labor population and prevailing contact-free economy, companies have launched unmanned ground delivery vehicles (UGDVs) to replace human distribution with machines. To meet the requirements for selecting UGDVs and achieve better applications in community delivery, a multi-criteria decision-making (MCDM) framework, combining the self-confidence aggregation approach and social trust network, is proposed in this study. Based on the internal characteristics of UGDVs, a multi-criteria comprehensive evaluation system for UGDVs is constructed. Then, a trust propagation and aggregation mechanism to yield expert weights based on a social trust network is suggested. Further, a self-confidence Pythagorean fuzzy aggregation operator is proposed to enhance the credibility of the decision results and compensate for the defects of existing methods. Finally, a practical case is considered to demonstrate the complete process of the MCDM model and to conduct a comparative analysis and sensitivity analysis of the model.

Suggested Citation

  • Zeng, Shouzhen & Zhang, Na & Zhang, Chonghui & Su, Weihua & Carlos, Llopis-Albert, 2022. "Social network multiple-criteria decision-making approach for evaluating unmanned ground delivery vehicles under the Pythagorean fuzzy environment," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    • Handle: RePEc:eee:tefoso:v:175:y:2022:i:c:s0040162521008453
    DOI: 10.1016/j.techfore.2021.121414
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    1. D'Adamo, Idiano & Falcone, Pasquale Marcello & Imbert, Enrica & Morone, Piergiuseppe, 2020. "A Socio-economic Indicator for EoL Strategies for Bio-based Products," Ecological Economics, Elsevier, vol. 178(C).
    2. Thomas L. Saaty, 1987. "Risk—Its Priority and Probability: The Analytic Hierarchy Process," Risk Analysis, John Wiley & Sons, vol. 7(2), pages 159-172, June.
    3. Guy Desaulniers & Fausto Errico & Stefan Irnich & Michael Schneider, 2016. "Exact Algorithms for Electric Vehicle-Routing Problems with Time Windows," Operations Research, INFORMS, vol. 64(6), pages 1388-1405, December.
    4. Ghenai, Chaouki & Albawab, Mona & Bettayeb, Maamar, 2020. "Sustainability indicators for renewable energy systems using multi-criteria decision-making model and extended SWARA/ARAS hybrid method," Renewable Energy, Elsevier, vol. 146(C), pages 580-597.
    5. Boysen, Nils & Schwerdfeger, Stefan & Weidinger, Felix, 2018. "Scheduling last-mile deliveries with truck-based autonomous robots," European Journal of Operational Research, Elsevier, vol. 271(3), pages 1085-1099.
    6. Lu, Xiaonong & Zhang, Qiang & Peng, Zhanglin & Shao, Zhen & Song, Hao & Wang, Wanying, 2020. "Charging and relocating optimization for electric vehicle car-sharing: An event-based strategy improvement approach," Energy, Elsevier, vol. 207(C).
    7. Mustafa Hamurcu & Tamer Eren & Ming-Sheng Liu, 2020. "Selection of Unmanned Aerial Vehicles by Using Multicriteria Decision-Making for Defence," Journal of Mathematics, Hindawi, vol. 2020, pages 1-11, June.
    8. Boysen, Nils & Schwerdfeger, Stefan & Weidinger, Felix, 2018. "Scheduling last-mile deliveries with truck-based autonomous robots," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 126189, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    9. Hannan, M.A. & Hoque, M.M. & Mohamed, A. & Ayob, A., 2017. "Review of energy storage systems for electric vehicle applications: Issues and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 771-789.
    10. Lemardelé, Clément & Estrada, Miquel & Pagès, Laia & Bachofner, Mónika, 2021. "Potentialities of drones and ground autonomous delivery devices for last-mile logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    11. Rezaei, Jafar, 2015. "Best-worst multi-criteria decision-making method," Omega, Elsevier, vol. 53(C), pages 49-57.
    12. Harish Garg, 2017. "Confidence levels based Pythagorean fuzzy aggregation operators and its application to decision-making process," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 546-571, December.
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    7. Zhang, Chonghui & Jiang, Nanyue & Su, Tiantian & Chen, Ji & Streimikiene, Dalia & Balezentis, Tomas, 2022. "Spreading knowledge and technology: Research efficiency at universities based on the three-stage MCDM-NRSDEA method with bootstrapping," Technology in Society, Elsevier, vol. 68(C).

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