Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015, Article ID 689383, 10 pages
http://dx.doi.org/10.1155/2015/689383
Research Article

Agent Behavior-Based Simulation Study on Mass Collaborative Product Development Process

1School of Economics and Management, North China Electric Power University, Beijing 102206, China
2Dongling School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China

Received 23 January 2015; Revised 1 March 2015; Accepted 2 March 2015

Academic Editor: Chin-Chia Wu

Copyright © 2015 Shuo Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. Tapscott and A. D. Williams, Wikinomics: How Mass Collaboration Changes Everything, Portfolio, Penguin Group, 2006.
  2. Y. Li, C.-H. Tan, and H.-H. Teo, “Leadership characteristics and developers' motivation in open source software development,” Information and Management, vol. 49, no. 5, pp. 257–267, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. C. Santos, G. Kuk, F. Kon, and J. Pearson, “The attraction of contributors in free and open source software projects,” The Journal of Strategic Information Systems, vol. 22, no. 1, pp. 26–45, 2013. View at Publisher · View at Google Scholar · View at Scopus
  4. M. D. Gallego, P. Luna, and S. Bueno, “User acceptance model of open source software,” Computers in Human Behavior, vol. 24, no. 5, pp. 2199–2216, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. V. Midha and P. Palvia, “Factors affecting the success of Open Source Software,” Journal of Systems and Software, vol. 85, no. 4, pp. 895–905, 2012. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Wang, “Survival factors for Free Open Source Software projects: a multi-stage perspective,” European Management Journal, vol. 30, no. 4, pp. 352–371, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. T. van der Valk, M. M. H. Chappin, and G. W. Gijsbers, “Evaluating innovation networks in emerging technologies,” Technological Forecasting and Social Change, vol. 78, no. 1, pp. 25–39, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. S. L. Toral, M. R. Martínez-Torres, and F. Barrero, “Analysis of virtual communities supporting OSS projects using social network analysis,” Information and Software Technology, vol. 52, no. 3, pp. 296–303, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. M. D. König, S. Battiston, M. Napoletano, and F. Schweitzer, “Recombinant knowledge and the evolution of innovation networks,” Journal of Economic Behavior & Organization, vol. 79, no. 3, pp. 145–164, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Ye and A. Kankanhalli, “Exploring innovation through open networks: a review and initial research questions,” IIMB Management Review, vol. 25, no. 2, pp. 69–82, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. M. R. Martínez-Torres, “Application of evolutionary computation techniques for the identification of innovators in open innovation communities,” Expert Systems with Applications, vol. 40, no. 7, pp. 2503–2510, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. H. Wi, S. Oh, and M. Jung, “Virtual organization for open innovation: semantic web based inter-organizational team formation,” Expert Systems with Applications, vol. 38, no. 7, pp. 8466–8476, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Bianchi, A. Cavaliere, D. Chiaroni, F. Frattini, and V. Chiesa, “Organisational modes for Open Innovation in the bio-pharmaceutical industry: an exploratory analysis,” Technovation, vol. 31, no. 1, pp. 22–33, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. H. Choi, S.-H. Kim, and J. Lee, “Role of network structure and network effects in diffusion of innovations,” Industrial Marketing Management, vol. 39, no. 1, pp. 170–177, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. C. Raasch, V. Lee, S. Spaeth, and C. Herstatt, “The rise and fall of interdisciplinary research: the case of open source innovation,” Research Policy, vol. 42, no. 5, pp. 1138–1151, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. W. Scacchi, “Issues and experiences in modeling open source software development processes,” in Proceedings of the 3rd ICSE Workshop on Open Source Software Engineering, pp. 121–125, May 2003.
  17. Q. Le and J. H. Panchal, “Building smaller sized surrogate models of complex bipartite networks based on degree distributions,” IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, vol. 42, no. 5, pp. 1152–1166, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. W. Sack, F. Détienne, N. Ducheneaut, J.-M. Burkhardt, D. Mahendran, and F. Barcellini, “A methodological framework for socio-cognitive analyses of collaborative design of open source software,” Computer Supported Cooperative Work, vol. 15, no. 2-3, pp. 229–250, 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. F. Barcellini, F. Détienne, J.-M. Burkhardt, and W. Sack, “A socio-cognitive analysis of online design discussions in an Open Source Software community,” Interacting with Computers, vol. 20, no. 1, pp. 141–165, 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. C. Haythornthwaite, “Crowds and communities: light and heavyweight models of peer production,” in Proceedings of the 42nd Hawaii International Conference on System Sciences, pp. 1–10, IEEE Computer Society, Los Alamitos, Calif, USA, January 2009, https://www.ideals.uiuc.edu/handle/2142/9457. View at Publisher · View at Google Scholar
  21. Z. H. Li and N. Wang, “Modeling of the dynamical mechanism of peer production based on system dynamics,” Studies in Science of Science, vol. 30, no. 2, pp. 232–240, 2012. View at Google Scholar
  22. J. Yang, C. Yao, W. Ma, and G. Chen, “A study of the spreading scheme for viral marketing based on a complex network model,” Physica A: Statistical Mechanics and Its Applications, vol. 389, no. 4, pp. 859–870, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. R. Badawy, B. Hirsch, and S. Albayrak, “Agent-based coordination techniques for matching supply and demand in energy networks,” Integrated Computer-Aided Engineering, vol. 17, no. 4, pp. 373–382, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. F. Ponci, L. Cristaldi, M. Faifer, and M. Riva, “Multi agent systems: an example of power system dynamic reconfiguration,” Integrated Computer-Aided Engineering, vol. 17, no. 4, pp. 359–372, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. J. Li and Z. Sheng, “A multi-agent model for the reasoning of uncertainty information in supply chains,” International Journal of Production Research, vol. 49, no. 19, pp. 5737–5753, 2011. View at Publisher · View at Google Scholar · View at Scopus
  26. H. Y. K. Lau and S. O. Woo, “An agent-based dynamic routing strategy for automated material handling systems,” International Journal of Computer Integrated Manufacturing, vol. 21, no. 3, pp. 269–288, 2008. View at Publisher · View at Google Scholar · View at Scopus
  27. N. Ruiz, A. Giret, V. Botti, and V. Feria, “Agent-supported simulation environment for intelligent manufacturing and warehouse management systems,” International Journal of Production Research, vol. 49, no. 5, pp. 1469–1482, 2011. View at Publisher · View at Google Scholar · View at Scopus
  28. F. C. Yáñez, J.-M. Frayret, F. Léger, and A. Rousseau, “Agent-based simulation and analysis of demand-driven production strategies in the timber industry,” International Journal of Production Research, vol. 47, no. 22, pp. 6295–6319, 2009. View at Publisher · View at Google Scholar · View at Scopus
  29. J. W. Yin, W. Y. Zhang, and M. Cai, “Weaving an agent-based Semantic Grid for distributed collaborative manufacturing,” International Journal of Production Research, vol. 48, no. 7, pp. 2109–2126, 2010. View at Publisher · View at Google Scholar · View at Scopus
  30. P. Renna, “Multi-agent based scheduling in manufacturing cells in a dynamic environment,” International Journal of Production Research, vol. 49, no. 5, pp. 1285–1301, 2011. View at Publisher · View at Google Scholar · View at Scopus
  31. J.-S. Lin, C. Ou-Yang, and Y.-C. Juan, “Towards a standardised framework for a multi-agent system approach for cooperation in an original design manufacturing company,” International Journal of Computer Integrated Manufacturing, vol. 22, no. 6, pp. 494–514, 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. M. Niazi and A. Hussain, “Agent-based computing from multi-agent systems to agent-based models: a visual survey,” Scientometrics, vol. 89, no. 2, pp. 479–499, 2011. View at Publisher · View at Google Scholar · View at Scopus
  33. R. E. Levitt, “Overview of the Virtual Design Team (VDT): A Computational Model of Project Teams,” 2009.
  34. P. Faas, S. Swindler, J. Lyons, R. Levitt, M. Ramsey, and P. Vincent, Organizational Modeling and Simulation in a Planning Organization, Air Force Research Laboratory, Wright-Patterson Air Force Base, Dayton, Ohio, USA, 2009.
  35. J. Martínez-Miranda and J. Pavón, “An agent-based simulation tool to support work teams formation,” in International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI '08), vol. 50 of Advances in Soft Computing, pp. 80–89, Springer, Berlin, Germany, 2009. View at Publisher · View at Google Scholar
  36. R. Garcia, “Uses of agent-based modeling in innovation/new product development research,” Journal of Product Innovation Management, vol. 22, no. 5, pp. 380–398, 2005. View at Publisher · View at Google Scholar · View at Scopus
  37. J. X. Wang, M. X. Tang, L. N. Song, and S. Q. Jiang, “Design and implementation of an agent-based collaborative product design system,” Computers in Industry, vol. 60, no. 7, pp. 520–535, 2009. View at Publisher · View at Google Scholar · View at Scopus
  38. J. H. Panchal, “Agent-based modeling of mass-collaborative product development processes,” Journal of Computing and Information Science in Engineering, vol. 9, no. 3, pp. 1–12, 2009. View at Publisher · View at Google Scholar · View at Scopus
  39. Y.-Z. Li, S. Zhang, X.-D. Zhang, and T. Wang, “Agent model for mass collaborative product development process and its simulation application,” Computer Integrated Manufacturing Systems, vol. 19, no. 8, pp. 1948–1956, 2013. View at Google Scholar · View at Scopus
  40. R. Rudek, “Computational complexity and solution algorithms for flowshop scheduling problems with the learning effect,” Computers & Industrial Engineering, vol. 61, no. 1, pp. 20–31, 2011. View at Publisher · View at Google Scholar · View at Scopus