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A trust-based large-scale group decision making consensus reaching framework for intercity railway public–private partnership model selection

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Abstract

Public–private partnership is a form of contract that the government regards private sectors as long-term partners in infrastructures and public services. With increasing intercity travel passengers, the intercity railway PPP projects have attracted more and more attention. The PPP model selection is a complex decision problem for PPP project construction. It usually requires many experts from different fields to be involved in project process, which belongs to a large-scale group decision making (LSGDM) problem. Therefore, a trust-based LSGDM consensus reaching framework is proposed for intercity railway PPP model selection. Firstly, this paper develops a new clustering algorithm by considering the direct trust relationship and the opinion deviation. Based on the trust value between experts within subgroups, a trust consensus model to obtain stable trust relationships is built. Then, the weights associated with experts and subgroups are calculated. Furthermore, minimum adjustment models for different opinion consensus levels are built to improve the opinion consensus of subgroups. Finally, Hangzhou-Shaoxing-Taizhou intercity railway PPP model selection problem is offered to show the effectiveness of the new approach, and comparative analysis is conducted.

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Acknowledgements

This work was supported by the Major Project for National Natural Science Foundation of China (No. 72091515).

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Authors and Affiliations

  1. School of Business, Central South University, Changsha, 410083, China

    Fanyong Meng, Bicong Chen & Zongrun Wang

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  1. Fanyong Meng
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  2. Bicong Chen
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  3. Zongrun Wang
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Appendices

Appendix 1

See Table 16.

Table 16 The initial incomplete trust-relationships between experts
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Appendix 2

See Table 17.

Table 17 The LDADM of each expert
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Meng, F., Chen, B. & Wang, Z. A trust-based large-scale group decision making consensus reaching framework for intercity railway public–private partnership model selection. Neural Comput & Applic 34, 19091–19115 (2022). https://doi.org/10.1007/s00521-022-07462-4

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