On Modeling of Sorted Cost Consensus Negotiation Considering Efficiency and Time Based on the Stochastic Programming
Metadatos
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MDPI
Materia
Stochastic programming Genetic algorithm Group decisions and negotiations Sorted cost consensus Opinion order efficiency
Fecha
2023-01-13Referencia bibliográfica
Zhou, Y... [et al.]. On Modeling of Sorted Cost Consensus Negotiation Considering Efficiency and Time Based on the Stochastic Programming. Mathematics 2023, 11, 445. [https://doi.org/10.3390/math11020445]
Patrocinador
National Natural Science Foundation of China (NSFC) 71971121; Major Project Plan of Philosophy and Social Sciences Research at Jiangsu University 2020SJZDA076; Jiangsu Postgraduate Research and Practice Innovation Program 1484052201074Resumen
In the consensus reaching process (CRP) permitting negotiation, the efficiency of negotiation
is affected by the order of negotiation with decision makers (DMs), the time, and the number of
moderators. In this paper, the sorted negotiation against DMs considering efficiency and time is initiated
into consensus decision making, which can improve the speed and effectiveness of consensus.
Based on the opinion dynamics (opinion evolution), uniform and normal distributions are used to
describe the uncertainty of DMs’ opinions and negotiation time, the opinion order efficiency and cost
coefficient are coined, and the cost-constrained optimal efficiency sorted negotiation model and the
optimal efficiency sorted negotiation model involving multiple moderators and time constraints are
respectively constructed. The optimal solution of the chance-constrained model is obtained in the context
of China’s urban demolition negotiation using an improved genetic algorithm, and an optimum
set of influential individuals based on opinion similarity is introduced so that assessment criteria for
validating the reasonableness of the sorting sequence are determined. Sorted consensus negotiation
combined with complex scenarios such as different representation formats of opinions, characteristics
of DMs, other solving algorithms, Bayesian dynamics, etc. can be included in future works.