A Dynamic Re-Configuration and Order Optimization Model and Optimization Algorithm in Complex System

Abstract:

In order to make the complex assessment system become more intelligent and efficient for the application and realization technology in real system, the enterprise alliance and its business reconfiguration model for complex application system are processed. The enterprise alliance with dynamic reconfiguration and part feature is established by constructing the information platform The system can realize the informationize in enterprise and between enterprise. The cooperation between enterprises can also be supported. The order assignment problem in the enterprise with directed graph model is presented. Simulation results show that the model and the algorithm are effective to the problem.

• Periodical: Applied Mechanics and Materials (Volumes 26-28)
• Pages: 1159-1162
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.26-28.1159
• Online since: June 2010
• Authors: Ya Hong Yang, Ying Wang
• Keywords: Complex System, Dynamic Reconfiguration, Order Optimization

References

[1] Zhou, Bing-Hai, Wang, Shi-Jin, Xi, Li-Feng. Agent-based decision support system for dynamic scheduling of a flexible manufacturing system. International Journal of Computer Applications in Technology, 32(1): 47-62, (2008).

DOI: 10.1504/ijcat.2008.019489

[2] Srivastava, Sharad Chandra, Choudhary, Alok Kumar, Kumar, Surendra. Development of an intelligent agent-based AGV controller for a flexible manufacturing system. International Journal of Advanced Manufacturing Technology, 36(7-8): 780-797. Rao Y, Li P, Shao X, etal. A CORBA- and MAS-based architecture for agile collaborative manufacturing systems. International Journal of Computer Integrated Manufacturing, vol. 19, no. 8, pp.815-832, (2006).

DOI: 10.1080/09511920500399540

[3] Zhang, Changsheng, Sun, Jigui, Zhu, Xingjun et al. An improved particle swarm optimization algorithm for flowshop scheduling problem. Information Processing Letters, 108(4): 204-209, (2008).

DOI: 10.1016/j.ipl.2008.05.010

[4] Zhang, Guohui, Shao, Xinyu, Li, Peigen et al. An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Computers and Industrial Engineering, 56(4): 1309-1318, (2009).

DOI: 10.1016/j.cie.2008.07.021

[5] Berrichi, A., Amodeo, L., Yalaoui, F. et al. Bi-objective optimization algorithms for joint production and maintenance scheduling: application to the parallel machine problem. Journal of Intelligent Manufacturing, 20(4): 389-400, (2009).

DOI: 10.1007/s10845-008-0113-5

[6] Gupta, A.K., Sivakumar, A.I. Optimization of due-date objectives in scheduling semiconductor batch manufacturing. International Journal of Machine Tools and Manufacture, 46(12-13): 1671-1679.

DOI: 10.1016/j.ijmachtools.2005.08.017

[7] Zhang, Changsheng, Sun, Jigui. An alternate two phases particle swarm optimization algorithm for flow shop scheduling problem. Expert Systems with Applications, 36(1): 5162-5167, (2009).

DOI: 10.1016/j.eswa.2008.06.036

[8] Azadani, E. Nasr, Hosseinian, S.H., Moradzadeh, B. Generation and reserve dispatch in a competitive market using constrained particle swarm optimization. International Journal of Electrical Power and Energy Systems, 32(1): 79-86, (2010).

DOI: 10.1016/j.ijepes.2009.06.009