基于用户心理行为的云制造知识服务优选方法
首发时间:2018-10-31
摘要:为提高云制造环境下知识服务的选择质量,提出一种基于用户心理行为的云制造知识服务优化选择方法。基于云制造知识服务过程及其特性的分析,建立云制造知识服务优选评价指标体系;运用粗糙集理论,对各评价指标进行初始权重分配,并根据用户多属性偏好对初始权重进行调整,以确保权重分配的合理性;在此基础上,结合用户在决策过程中的心理行为分析和后悔理论,构建云制造知识服务的感知价值矩阵,并计算各知识服务的综合感知价值,以获得最优云制造知识服务。最后,通过一个应用实例,验证此方法的有效性和实用性。
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Optimization method of cloud manufacturing knowledge service based on psychological behavior of users
Abstract:In order to improve the selection quality of knowledge service in cloud manufacturing environment, a cloud manufacturing knowledge service optimization method based on users\' psychological behavior was proposed. Based on the analysis of cloud manufacturing knowledge service process and its characteristics, the optimization evaluation index system of cloud manufacturing knowledge service was set up. The rough set theory was used to assign initial weights to each evaluation index, and the initial weights were adjusted according to the multiple-attribute preferences of users\' to ensure the rationality of weight distribution. On this basis, combining with the users\' psychological behavior analysis in the decision-making process and regret theory,the perceived value matrix of cloud manufacturing knowledge service was constructed, and the comprehensive perceived value of each knowledge service was calculated by integrating the above index weights to obtain the optimal cloud manufacturing knowledge service. Finally, an application example was used to verify the effectiveness and practicality of this method.
Keywords: cloud manufacturing knowledge service regret aversion psychological behavior multiple-attribute preferences
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