A Manufacturing Network Modeling and Evolution Characterizing Approach for Self-Organization Among Distributed MSMEs Under Social Manufacturing Context

In the manufacturing industry, cross-enterprise resource sharing has emerged among micro-and-small-scale manufacturing enterprises (called social manufacturing nodes, SMNs) with similar manufacturing resources. In this context, social manufacturing is proposed to promote resource sharing among SMNs through order sharing in manufacturing communities (MCs) on the network platform. In social manufacturing, SMNs are geographically distributed and peer-to-peer, and MCs are formed by the self-organization among SMNs. However, because of the distributed and peer-to-peer characteristic of SMNs, the efficiency of SMNs self-organizing into MCs is relatively low, and the scope of self-organization is also relatively narrow. After MCs are formed, the structure of MCs is time-varying, and the evolution information of MCs is helpful for the smooth operation of the network platform. For these problems, this paper proposes a manufacturing network modeling and evolution characterizing approach. Firstly, distributed SMNs are clustered into overlapping MCs by the speaker-listener label propagation algorithm. Based on the clustering result, SMNs are recommended to each other as potential partners, by which they can quickly self-organize into MCs. On the other hand, seven fundamental events are defined to characterize the evolution of MCs on the network platform. From the evolution of MCs, the manager of the network platform get useful information for the smooth operation of the network platform. The feasibility of the proposed approach is verified by a simulation case.

[1]  Pingyu Jiang,et al.  Social Manufacturing Paradigm: Concepts, Architecture and Key Enabled Technologies , 2018, Springer Series in Advanced Manufacturing.

[2]  Dongwoo Kang,et al.  An enterprise architecture framework for collaboration of virtual enterprise chains , 2008 .

[3]  Pingyu Jiang Social Manufacturing: Fundamentals and Applications , 2019 .

[4]  Boleslaw K. Szymanski,et al.  Towards Linear Time Overlapping Community Detection in Social Networks , 2012, PAKDD.

[5]  Mansoureh Takaffoli,et al.  Community Evolution Mining in Dynamic Social Networks , 2011 .

[6]  Huaiyu Wan,et al.  A fast and reasonable method for community detection with adjustable extent of overlapping , 2010, 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering.

[7]  Rui Jiang,et al.  From Ontology to Semantic Similarity: Calculation of Ontology-Based Semantic Similarity , 2013, TheScientificWorldJournal.

[8]  Przemyslaw Kazienko,et al.  Influence of the Dynamic Social Network Timeframe Type and Size on the Group Evolution Discovery , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

[9]  Andrea Lancichinetti,et al.  Detecting the overlapping and hierarchical community structure in complex networks , 2008, 0802.1218.

[10]  Boleslaw K. Szymanski,et al.  Overlapping community detection in networks: The state-of-the-art and comparative study , 2011, CSUR.

[11]  Zude Zhou,et al.  Design of agent-based supply chain-oriented virtual enterprise platform , 2011, Kybernetes.

[12]  Vasdev Malhotra,et al.  A framework to enhance agile manufacturing system , 2017 .

[13]  Kai Ding,et al.  Modeling and analyzing of an enterprise relationship network in the context of social manufacturing , 2016 .

[14]  Srinivasan Parthasarathy,et al.  An event-based framework for characterizing the evolutionary behavior of interaction graphs , 2007, KDD '07.

[15]  Réka Albert,et al.  Near linear time algorithm to detect community structures in large-scale networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Max J. Egenhofer,et al.  Determining Semantic Similarity among Entity Classes from Different Ontologies , 2003, IEEE Trans. Knowl. Data Eng..

[17]  Boleslaw K. Szymanski,et al.  LabelRank: A stabilized label propagation algorithm for community detection in networks , 2013, 2013 IEEE 2nd Network Science Workshop (NSW).

[18]  Chai Xu-dong,et al.  Cloud manufacturing:a new service-oriented networked manufacturing model , 2010 .

[19]  Xiaoming Liu,et al.  SLPA: Uncovering Overlapping Communities in Social Networks via a Speaker-Listener Interaction Dynamic Process , 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

[20]  João Gama,et al.  Dynamic communities in evolving customer networks: an analysis using landmark and sliding windows , 2014, Social Network Analysis and Mining.

[21]  Jie Gao,et al.  Service-oriented manufacturing: a new product pattern and manufacturing paradigm , 2011, J. Intell. Manuf..

[22]  Wei Chen,et al.  A game-theoretic framework to identify overlapping communities in social networks , 2010, Data Mining and Knowledge Discovery.

[23]  Giuseppe Pirrò,et al.  A semantic similarity metric combining features and intrinsic information content , 2009, Data Knowl. Eng..

[24]  Steve Gregory,et al.  Finding overlapping communities in networks by label propagation , 2009, ArXiv.

[25]  Timo R. Nyberg,et al.  Removing barriers to sustainability research on personal fabrication and social manufacturing , 2018 .

[26]  David Sánchez,et al.  Towards the estimation of feature-based semantic similarity using multiple ontologies , 2014, Knowl. Based Syst..

[27]  A. Barabasi,et al.  Quantifying social group evolution , 2007, Nature.

[28]  Biqing Huang,et al.  Cloud manufacturing service platform for small- and medium-sized enterprises , 2012, The International Journal of Advanced Manufacturing Technology.

[29]  David Sánchez,et al.  Semantic similarity estimation from multiple ontologies , 2012, Applied Intelligence.

[30]  Hoa A. Nguyen,et al.  A Cluster-Based Approach for Semantic Similarity in the Biomedical Domain , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[31]  Derek Greene,et al.  Tracking the Evolution of Communities in Dynamic Social Networks , 2010, 2010 International Conference on Advances in Social Networks Analysis and Mining.

[32]  Kirsi Niinimäki,et al.  Social Manufacturing in the Fashion sector: New value creation through alternative design strategies? , 2018 .

[33]  Pingyu Jiang,et al.  Towards a cyber-physical-social-connected and service-oriented manufacturing paradigm: Social Manufacturing , 2016 .

[34]  Georgios Paliouras,et al.  Predicting the evolution of communities in social networks using structural and temporal features , 2017, SMAP.

[35]  Santo Fortunato,et al.  Finding Statistically Significant Communities in Networks , 2010, PloS one.

[36]  Andrew Y. C. Nee,et al.  Advanced manufacturing systems: socialization characteristics and trends , 2015, Journal of Intelligent Manufacturing.

[37]  Xiao Hua Chen,et al.  A WordNet-based semantic similarity measurement combining edge-counting and information content theory , 2015, Eng. Appl. Artif. Intell..

[38]  Pietro Liò,et al.  Towards real-time community detection in large networks. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[39]  Gang Xiong,et al.  Social manufacture cloud mode in high-end apparel, footwear and hats , 2014, Proceeding of the 11th World Congress on Intelligent Control and Automation.

[40]  Yun Chi,et al.  Analyzing communities and their evolutions in dynamic social networks , 2009, TKDD.

[41]  Babak Mohajeri Paradigm Shift from Current Manufacturing to Social Manufacturing , 2015 .

[42]  Boleslaw K. Szymanski,et al.  Community detection using a neighborhood strength driven Label Propagation Algorithm , 2011, 2011 IEEE Network Science Workshop.

[43]  P. Jiang,et al.  Shared factory: A new production node for social manufacturing in the context of sharing economy , 2019, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture.

[44]  Ling Chen,et al.  MLPA: Detecting overlapping communities by multi-label propagation approach , 2013, 2013 IEEE Congress on Evolutionary Computation.

[45]  Peihua Gu,et al.  Social manufacturing as a sustainable paradigm for mass individualization , 2016 .

[46]  T. Vicsek,et al.  Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.

[47]  David Sánchez,et al.  Ontology-based semantic similarity: A new feature-based approach , 2012, Expert Syst. Appl..

[48]  Euripides G. M. Petrakis,et al.  X-Similarity: Computing Semantic Similarity between Concepts from Different Ontologies , 2006, J. Digit. Inf. Manag..