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

• Published in: Technological forecasting & social change 2022-02, Vol.175, p.121414, Article 121414
• Main Authors: Zeng, Shouzhen, Zhang, Na, Zhang, Chonghui, Su, Weihua, Carlos, Llopis-Albert
• Format: Article
• Language: English
• Subjects: Agglomeration; Comparative analysis; Confidence; Credibility; Decision making; Economic development; Fuzzy logic; Fuzzy sets; Machinery; Multi-criteria decision-making; Multiple criterion; Pythagorean fuzzy set; Self-confidence; Sensitivity analysis; Social network; Social networks; Trust; Trust propagation; Unmanned ground delivery vehicle
• ISSN: 0040-1625; 1873-5509
• DOI: 10.1016/j.techfore.2021.121414

•A multi-criteria comprehensive evaluation system for UGDVs is constructed.•A social trust propagation and aggregation mechanism to yield expert weights is proposed.•Two self-confidence Pythagorean fuzzy aggregation operators are proposed to overcome the existing defects.•A social network MCDM framework for UGDV selection under PFS environment is presented.•A comparative analysis and sensitivity analysis of the model are conducted to ensure the robustness. 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.