Consensus: The Minimum Cost Model based Robust Optimization

Yanling Lu, Yejun Xu
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Abstract

In decision making, there are opinion conflict for the people involved it. In order to eliminate the opinion conflict, the minimum cost consensus model is proposed to coordinate the opinion of experts. However, in the exiting minimum cost model, the unit adjustment cost of experts is supposed to be fixed. It is hard to get and can be uncertain. Therefore, the purpose of this paper is to propose a consensus model based on robust optimization. In the presented model, it mainly solves the worst-case consensus problem. Subsequently, the detailed consensus feedback adjustment is presented involving two aspects: construct robust counterpart of worst-case consensus and estimate the unit adjustment cost of expert. Finally, through numerical example and comparative analysis, the validity and superiority of the presented consensus model are verified.
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共识:基于最小成本模型的鲁棒优化
在决策过程中,参与其中的人会有意见冲突。为了消除意见冲突,提出了最小成本共识模型来协调专家意见。然而,在现有的最小成本模型中,专家的单位调整成本是固定的。它很难获得,而且可能不确定。因此,本文的目的是提出一个基于鲁棒优化的共识模型。该模型主要解决最坏情况下的共识问题。在此基础上,从构造最坏共识的鲁棒对偶和估计专家的单位调整成本两个方面给出了详细的共识反馈调整。最后,通过数值算例和对比分析,验证了所提出的共识模型的有效性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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