Social Learning Evolution (SLE): Computational Experiment-Based Modeling Framework of Social Manufacturing
Social Learning Evolution (SLE): Computational Experiment-Based Modeling Framework of Social Manufacturing
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DOI：
10.1109/tii.2018.2871167
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发表时间：
2019-06
影响因子：
12.3
通讯作者：
Xiao Xue;Shufang Wang;Lejun Zhang;Zhiyong Feng;Yaodan Guo
中科院分区：
文献类型：
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作者：
Xiao Xue;Shufang Wang;Lejun Zhang;Zhiyong Feng;Yaodan Guo
作为互联网时代制造业的新形态，社会制造具有其固有的“社会-网络”复杂性：制造服务的来源是社会性的，而这种社会性加剧了服务供给的多样性、不确定性和动态性。这对供需双方的服务匹配提出了新的挑战。为了应对这一挑战，有必要对社会制造进行复杂性分析。传统的研究主要依靠数据统计和宏观分析，难以清晰地识别各种影响因素与宏观演化现象之间的联系。为了改变这一现状，从社会学习进化的角度提出了社会制造的建模框架，包括个体进化模型、组织学习模型和社会学习模型。基于SLE框架，构建了相应的计算实验系统，对社会制造的复杂性进行了分析。通过对社会制造中几种演化机制的仿真和比较，说明了SLE框架的应用。结果表明，我们的方法有很大的承诺。
As a new form of manufacturing industry in the Internet era, social manufacturing has its inherent “social-cyber” complexity: the source of manufacturing service is social, and such sociality aggravates the diversity, uncertainty, and dynamics of service supply. This poses new challenges to the service matching between supply-side and demand-side. In order to meet this challenge, it is necessary to conduct a complexity analysis of social manufacturing. Traditional researches mainly rely on data statistics and macro analysis, in which there are difficulties in clearly identifying the links between various impact factors and macro evolution phenomena. In order to change such a situation, this paper proposes a modeling framework of social manufacturing from the aspect of social learning evolution (SLE), including individual evolution model, organizational learning model, and social learning model. Based on the SLE framework, the corresponding computational experiment system is built to analyze the complexity of social manufacturing. The performance of several evolution mechanisms in social manufacturing is simulated and compared as a case study to present the application of SLE framework. The results demonstrate that our method has a substantial promise.