Abstract
This paper seeks to address an approach called the social network analysis method (SNAM) to evaluate the effect of resource scalability on networked manufacturing system. Considering the case of networked manufacturing mode, we have proposed a framework of SNAM for generating the collaborative networks. The collaborative networks have been obtained by transferring the input data in the form of an affiliation matrix to the UCINET and Netdraw software packages. Subsequently, we have conducted various tests to analyze the collaborative networks for finding the network structure, size, complexity and its functional properties. In this paper, a social network based greedy k-plex algorithm has been applied to evaluate the scalability effect on different data sets of networked manufacturing system. Experimental studies have been conducted and comparisons have been made to demonstrate the efficiency of the proposed approach.
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References
Crovella ME, Bestavros (1996) A self-similarity in world wide web traffic: evidence and possible causes. In: Gaither BE, Reed DA (eds) Proceedings of the ACM SIGMETRICS conference on measurement and modeling of computer systems. Association of Computing Machinery, New York, pp 148–159
Heddaya AS (2002) An economically scalable internet. IEEE Comput 35:93–95
Koren Y (2010) The global manufacturing revolution – product–process–business integration and reconfigurable systems, vol 80. Wiley, Hoboken
Liu F, Liu J, Lei Q (2002) The Connotation and research development trend of networked manufacturing. In: Proceedings of China mechanical engineering annual conference, Beijing, pp 22–27
Lu ET, Hamilton RJ (1991) Avalanches of the distribution of solar flares. Astrophys J 380:89–92
Mendes R, Kennedy J, Neves J (2004) The fully informed partical swarm: simpler, maybe better. IEEE Tran Evol Comput 8:3
Neukum G, Ivanov BA (1994) Crater size distributions and impact probabilities on Earth from lunar, terrestialplanet, and asteroid cratering data. In: Gehrels T (ed) Hazards due to comets and asteroids. University of Arizona Press, Tucson, pp 359–416
Newman MEJ (2002) Assortative mixing in networks. Phys Rev Lett 89:208701
Newman MEJ (2005) Powerlaws: Pareto distributions and Zipf’s law. Contemp Phys 46:323–351
Newman ME, Park J (2003) Why social networks are different from other types of networks. Phys Rev E 68(3):036122
Okino N (1993) Bionic manufacturing systems. In: Peklenik J (ed) Proceedings of the CIRP seminar on flexible manufacturing systems past-present-future, Bled, pp 73–95
Putnik G, Sluga A, ElMaraghy H, Teti R, Koren Y, Tolio T, Hon T (2013) Scalability in manufacturing systems design and operation: state-of-the-art and future developments roadmap. CIRP Ann Manuf Technol 62(2):751–774
Roberts DC, Turcotte DL (1998) Fractality and selforganized criticality of wars. Fractals 6:351–357
Ueda N (1993) A generic approach toward future manufacturing system. In: Peklenik J (ed) Proceedings of the CIRP seminar on flexible manufacturing systems past-present-future, Bled, pp 211–228
Valckenaers P, Bonneville F, Brussel H, Brussel V, Bongaerts L, Wyns J (1994) Results of the holonic control system benchmark at the K.U. Leuven. In: Proceedings of the computer integrated manufacturing and automation conference, Rensselaer Polytechnic Institute, Troy, pp 128–133
Wasserman S (1994) Social network analysis: methods and applications (Vol. 8). Cambridge University Press
Watts DS, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442
Wiendahl HP, ElMaraghy H, Nyhuis P, Zah MF, Wiendahl HH, Duffie N, Brieke M (2007) Changeable manufacturing – classification, design and operation. CIRP Ann Manuf Technol 56(2):783–809
Zhang WY, Zhang S, Chen YG, Pan XW (2013) Combining social network and collaborative filtering for personalised manufacturing survive recommendation. Int J Prod Res 51(22):6702–6719
Zhou GH, Xiao Z, Jiang PY, Huang GQ (2010) A game-theoretic approach to generating optimal process plans of multiple jobs in networked manufacturing. Int J Comput Int Manuf 23:1118–1132
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© 2013 Springer-Verlag London
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Manupati, V.K., Putnik, G., Tiwari, M.K. (2013). Resource Scalability in Networked Manufacturing System: Social Network Analysis Based Approach. In: Nee, A. (eds) Handbook of Manufacturing Engineering and Technology. Springer, London. https://doi.org/10.1007/978-1-4471-4976-7_116-1
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DOI: https://doi.org/10.1007/978-1-4471-4976-7_116-1
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