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Knowledge structure-based consensus-reaching method for large-scale multiattribute group decision-making
Knowledge-Based Systems ( IF 8.8 ) Pub Date : 2021-02-20 , DOI: 10.1016/j.knosys.2021.106885
Yuan-Wei Du , Qun Chen , Ya-Lu Sun , Chun-Hao Li

Large-scale multiattribute group decision-making (LMGDM) requires a large number of participants with different knowledge structures. This study proposed an LMGDM consensus-reaching method in which the experts’ knowledge structures are fully considered. An information extraction mechanism is constructed to extract incomplete inference information with the form of belief distribution (BD), and the Dempster–Shafer theory of evidence is adopted to make discounting and combinations for the BDs. To reduce their number, the experts are classified into different clusters by using the extended K-means approach, and two levels of consensus measures are both calculated to determine whether the experts involved in each cluster have reached a satisfactory level of consensus. If that consensus level is not reached, a feedback mechanism is activated to advise the identified experts to adjust their assessments, which allows them to change clusters during the consensus-reaching process. Through repeating the feedback mechanism, the assessments are improved until the satisfactory consensus levels are reached. A multi-objective linear programming method is established to obtain the optimal solution that satisfies all clusters as much as possible. Finally, a numerical comparison and discussion are undertaken to demonstrate the superiority of the proposed method.



中文翻译:

大规模多属性群体决策的基于知识结构的共识达成方法

大型多属性小组决策(LMGDM)需要大量具有不同知识结构的参与者。这项研究提出了一种LMGDM达成共识的方法,其中充分考虑了专家的知识结构。构建了一种信息提取机制,以信念分布(BD)的形式提取不完整的推理信息,并采用Dempster-Shafer证据理论对BD进行了折现和组合。为了减少他们的人数,使用扩展的K均值方法将专家分为不同的组,并计算两个级别的共识性度量,以确定每个集群中涉及的专家是否已达到令人满意的共识级别。如果未达到共识水平,激活了反馈机制,以建议已确定的专家调整他们的评估,这使他们可以在达成共识的过程中更改类别。通过重复反馈机制,评估将得到改善,直到达到令人满意的共识水平。建立了一种多目标线性规划方法,以获得尽可能满足所有聚类的最优解。最后,进行了数值比较和讨论,以证明所提出方法的优越性。建立了一种多目标线性规划方法,以获得尽可能满足所有聚类的最优解。最后,进行了数值比较和讨论,以证明所提出方法的优越性。建立了一种多目标线性规划方法,以获得尽可能满足所有聚类的最优解。最后,进行了数值比较和讨论,以证明所提出方法的优越性。

更新日期:2021-03-03
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