关键词: 云制造, 知识服务, 后悔规避, 心理行为, 多属性偏好

Abstract: In order to improve the selection quality of knowledge service in cloud manufacturing environment, a cloud manufacturing knowledge service optimization method based on user’s psychological behavior is proposed. Based on the analysis of cloud manufacturing knowledge service characteristics, the optimization evaluation index system of cloud manufacturing knowledge service is set up. The rough set theory is used to assign initial weights to each evaluation index, and the initial weights are adjusted according to the multiple-attribute preferences of user’s 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 is constructed, and the comprehensive perceived value of each knowledge service is calculated by integrating the above index weights to obtain the optimal cloud manufacturing knowledge service. Finally, an application example is used to verify the effectiveness and practicality of this method.

Key words: cloud manufacturing, knowledge service, regret aversion, psychological behavior, multiple-attribute preference