云制造资源模糊分类与智能优选研究

发布时间：2018-04-22 14:04

本文选题：云制造 + 云制造资源　；参考：《长春工业大学》2017年硕士论文

【摘要】：近年来我国的互联网技术和制造业快速发展,推动制造业由传统的制造模式向网络化、智能化、人性化和便捷化发展。在此背景下,新型的制造模式“云制造”应运而生,云制造能够解决当前我国大批制造型企业技术相对落后,专业人才严重缺乏以及先进信息资源过少等因素导致的产品研发能力不足的问题。云制造资源是云制造研究的重要基础,在云制造平台下,实现云制造资源的快速搜索与优选具有重要意义。鉴于此,本文在国家自然科学基金项目“云制造环境下可制造性评价理论与方法研究”的支持下,重点研究了云制造资源的模糊分类和云方案智能优选问题。首先,通过阅读和调查相关文献,提出和阐述本文的研究背景和意义,进而提出本文的研究内容。其次,提出云环境下制造资源模糊分类问题,采用模糊c-均值聚类算法构建了云制造资源模糊分类数学模型;为求解该问题,提出了一种基于遗传模拟退火的模糊分类方法,该方法包括内层循环和外层循环,外层循环可以获得云制造资源模糊分类的最佳分类数,内层循环可以获得云制造资源最佳分类下对应的模糊分类结果,并对该方法进行了实例仿真分析,验证了该方法的有效性,实现了制造资源的快速模糊分类。最后,提出云方案智能优选问题,建立了云方案优选评价指标体系,构建了云方案智能优选数学模型,模型包含生产成本目标函数、生产时间目标函数、加工质量目标函数和其它评价指标函数;为求解该问题,提出了一种基于层次分析法和熵权法相结合的细菌觅食混合优化方法,该方法采用层次分析法和熵权法获取各个评价指标的权重,利用细菌觅食优化理论对云方案进行智能优选,并对该方法进行了实例仿真分析,验证了该方法的有效性。
[Abstract]:In recent years, the rapid development of Internet technology and manufacturing industry in China has promoted the development of manufacturing industry from traditional manufacturing mode to network, intelligence, humanization and convenience. In this context, a new manufacturing model "cloud manufacturing" emerges as the times require. Cloud manufacturing can solve the problem that a large number of manufacturing enterprises in China are relatively backward in technology. Lack of professional personnel and lack of advanced information resources lead to insufficient R & D capability. Cloud manufacturing resource is an important foundation of cloud manufacturing research. It is of great significance to quickly search and select cloud manufacturing resources under cloud manufacturing platform. In view of this, this paper focuses on the fuzzy classification of cloud manufacturing resources and the intelligent selection of cloud schemes, supported by the project of National Natural Science Foundation of China, "Research on the theory and method of manufacturability evaluation in cloud manufacturing environment". First of all, by reading and investigating the relevant literature, this paper puts forward and expounds the research background and significance, and then puts forward the research content of this paper. Secondly, the fuzzy classification problem of manufacturing resources in cloud environment is proposed, and the fuzzy classification mathematical model of cloud manufacturing resources is constructed by using fuzzy c-means clustering algorithm, and a fuzzy classification method based on genetic simulated annealing is proposed to solve the problem. The method includes inner cycle and outer cycle. The outer cycle can obtain the optimal number of fuzzy classification of cloud manufacturing resources, and the inner loop can obtain the corresponding fuzzy classification results under the best classification of cloud manufacturing resources. The simulation results show that the method is effective and fast fuzzy classification of manufacturing resources is realized. Finally, the problem of cloud scheme intelligent optimal selection is put forward, the evaluation index system of cloud scheme optimization is established, and the mathematical model of cloud scheme intelligent optimal selection is constructed. The model includes production cost objective function and production time objective function. In order to solve this problem, a hybrid optimization method for bacterial foraging based on AHP and entropy weight method was proposed. In this method, AHP and entropy weight method are used to obtain the weights of each evaluation index, and the optimization theory of bacterial foraging is used to select the cloud scheme intelligently. An example is given to verify the effectiveness of this method.
【学位授予单位】：长春工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP393.09

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