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Minimum cost consensus models based on random opinions
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AI
DOI：10.1016/j.eswa.2017.07.035.png)
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摘要
En 中文
In some complex group decision making cases, the opinions of decision makers (DMs) present random characteristic. However, it is difficult to determine the range of opinions by knowing only their probability distributions. In this paper, we construct cost consensus models with random opinions. The objective function is obtaining the minimum consensus budget under a certain confidence level. Nonetheless, the constraints restrict the upper limit of the consensus cost, the lower limit of DMs' compensations, and the opinions deviation between DMs and the moderator. As such, probabilistic planning based on a genetic algorithm is designed to resolve the minimum cost consensus models based on China's urban demolition negotiation, which can better simulate the consensus decision-making process and obtain a satisfactory solution for the random optimization consensus models. The proposed models generalize both Ben-Arieh's minimum cost consensus model and Gong's consensus model with uncertain opinions. Considering that the opinions of DMs and the moderator obey various distributions, the models simulate the opinion characteristics more effectively. In the case analysis, a sensitivity analysis method is adopted to obtain the minimum budget, and probabilistic planning based on genetic algorithm to obtain a satisfactory solution that is closer to reality. (C) 2017 Elsevier Ltd. All rights reserved.
Keyword:
Group decision making
Consensus
Probability distribution
Probabilistic planning
Genetic algorithm
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期刊
IF:
7.5
论文数:
2.9W
被引数:
10.2W
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