A Complexity Model for Networks of Collaborating Enterprises
World Congress, Volume # 17 | Part# 1
Authors
Csáji, Balázs Csanád; Monostori, Laszlo
Identifier
10.3182/20080706-5-KR-1001.02342
Index Terms
Collaborative networked organizations principles; Methodologies and tools for analysis of complexity; Multi-agent systems applied to industrial systems
Abstract
Theoretical study of complex systems receives more and more attention as most sciences broaden their perspectives. The paper first briefly overviews a few important complexity approaches, then it presents a triple-level model for describing and analyzing collaborating enterprises. The environment is treated as a stochastic process, the core topology of the collaboration is represented by a graph and, finally, the dynamic behavior of collaborating enterprises is modeled as a Complex Adaptive System (CAS). Complexity measures for the different sub-models are suggested, some complexity drivers are investigated and it is argued that the resulted model can be effectively analyzed by simulation.
References
Barabási, A.-L. (2002). Linked: The New Science of Networks. Perseus, Cambridge. Barabási, A.-L. and R. Albert (1999). Emergence of scaling in random networks. Science 268, 509512. Brochev, D. and D. H. Rouvray (2006). Quantitative measures of network complexity. Complexity in Chemistry, Biology, and Ecology pp. 191235. Choi, T.Y., K.J. Dooley and M. Rungtusanatham (2001). Supply networks and complex adaptive systems: Control versus emergence. Journal of Operations Management 19, 351366. Csáji, B. Cs., L. Monostori and B. Kádár (2006). Reinforcement learning in a distributed market-based production control system. Advanced Engineering Informatics 20, 279288. Holland, J.H. (1992). Complex adaptive systems. Daedalus, pp. 1730. Holland, J.H. (1995). Hidden Order: How Adaptation Builds Complexity. Helix Books, Addison-Wesley. Kotov, V. (1997). Systems-of-systems as communicating structures. Technical report. Hewlett Packard Computer Systems Laboratory. Kurtz, C. F. and D. J. Snowden (2003). The new dynamics of strategy: Sense-making in a complex and complicated world. IBM Systems Journal 32(3), 462483. Lovász, L. and P. Gács (1999). Complexity of Algorithms. Lecture Notes, Boston University, Yale University. Monostori, L. and B. Cs. Csáji (2007). Production structures as complex adaptive systems. In: Proceedings of the 40th CIRP International Seminar on Manufacturing Systems, May 30 - June 1, Liverpool, United Kingdom. p. (CD version is available). Newman, M. E. J. (2003). The structure and function of complex networks. SIAM Review 45(2), 167256. Papoulis, A. and S. U. Pillai (2001). Probability, Random Variables and Stochastic Processes. McGraw-Hill. Schuh, G., A. Sauer and S. D¨oring (2006). Modeling collaborations as complex systems. In: Proceedings of the 4th International Industrial Simulation Conference (ISC), The University of Palermo. pp. 168174. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal 21, 623656. Surana, A., S. Kumara, M. Greaves and U. N. Raghavan (2005). Supply-chain networks: A complex adaptive systems perspective. International Journal of Production Research 43(20), 42354265. Ueda, K., A. Márkus, L. Monostori, H. J. J. Kals and T. Arai (2001). Emergent synthesis methodologies for manufacturing. Annals of the CIRP 50(2), 535551.
