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A Reliability-Based Consensus Model for Multiattribute Group Decision-Making with Analytically Evidential Reasoning Approach

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  • Yuan-Wei Du
  • Ning Yang
  • Wen Zhou
  • Chang-Xing Li

Abstract

Expert reliability is the ability to make unmistakable evaluations on attributes for the performance of an alternative in multiattribute group decision making (MAGDM). It has a significant effect on the group consensus calculation and group decision-making; unfortunately the reliability has not been considered in the consensus-reaching model yet. This study focuses on providing a reliability-based consensus model for MAGDM with analytically evidential reasoning (analytical ER for short) approach. The basic probability assignment (BPA) function which can be discounted by expert reliability is introduced to describe the performance judgments of each expert, by combining which of the group judgments could be determined with analytical ER rule. Then the consensus degrees of three levels (attribute level, alternative level, and expert level) are defined by Jousselme distance to identify the experts who should revise their judgments and point out revised suggestions, based on which a decision-making method within interaction is proposed to determine the effective BPA functions of all experts and make final decision-making. Finally, a numerical case study is carried out to illustrate the effectiveness of the method.

Suggested Citation

  • Yuan-Wei Du & Ning Yang & Wen Zhou & Chang-Xing Li, 2018. "A Reliability-Based Consensus Model for Multiattribute Group Decision-Making with Analytically Evidential Reasoning Approach," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-14, October.
  • Handle: RePEc:hin:jnlmpe:1651857
    DOI: 10.1155/2018/1651857
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    Cited by:

    1. Qin Yu & Yun Xiao & Guangmin Wang & Di Cui, 2024. "Sustainable Development of the China Railway Express under the Belt and Road Initiative: Focusing on Infrastructure Reliability and Trade Facilitation," Sustainability, MDPI, vol. 16(18), pages 1-17, September.

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