Abstract
The multi-project allocation with constrained resources problems is quite common in manufacturing industry. While relationship and data in enterprise has become complex and bulky along with the leaping development, this makes it far beyond the human experience to optimize the management. Particle Swarm Optimization (PSO) algorithm is then introduced to optimize resources allocation to products. Due to the deficiency of PSO dealing with large scale network, Complex Network theory, good at statistics but not optimization, is firstly introduced to simulate and help analyze the Collaborative Manufacturing Resource network (CMRN) as a complementation. Finally, an optimization is successfully applied to the network with the results presented. Further, these methods could be used for similar researches which integrate PSO with complex network theory.
Supported by NSFC (No. 50805089) and Shanghai Science and Technology Committee(NO. 09DZ1122502).
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References
Li, H., Love, P.E.D., Gunasekaran, A.: A conceptual approach to modeling the procurement process of construction using petri-nets. Journal of Intelligent Manufacturing 10, 347–353 (1999)
Gupta, S.: The effect of bid rigging on prices: a study of the highway construction industry. Review of Industrial Organization 19, 453–467 (2001)
Wang, F.R., Xu, W.W., Xu, H.F.: Solving Nonstandard Job-Shop Scheduling Problem by Efficiency Scheduling Algorithm. J. Computer Integrated Manufacturing Systems 7(7), 12–15 (2001)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proc. IEEE Int. Conf. on Neural Networks, pp. 1942–1948 (1942)
Gavish, B., Pirkul, H.: Algorithms formulti-resource generalized assignment problem. J. Management Science 37(6), 695–713 (1991)
Eberhart, R.C., Shi, Y.: Tracking and optimizing dynamic systemswith particle swarms. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2001), pp. 94–97. IEEE, Seoul (2001)
Cheng, X.M.: Research on Multi-mode Resource Constrained Project Scheduling Problem Base on Particle Swarm Optimization. D. Hefei University of Technology (2007)
Yang, Z.: Solving Robust Flow-Shop Scheduling Problems with Uncertain Processing Times Based on Hybrid Particle Swarm Optimization Algorithm. D. Shangdong University (2008)
Chang, H.J.: Research and application of PSO algorithm on shop scheduling. Qingdao University (2008)
Wu, A.H.: The Multi-Object Ant-Genetic Algorithm and its Application in Regional Water Resource Allocation. D. Hunan University, Hunan (2008)
Li, J., Hu, W.B.: Research on the System of Resource Optimization Allocation Based on Ant Colony Algorithm. J. Journal of zhongyuan university of technology, 06-0008-05 (2008)
Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘Small world’ networks. J. Nature 393(6684), 440–442 (1998)
Barabási, A.L., Albert, R.: Emergence of scaling in random networks. J. Science 286(5439), 509–512 (1999)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proc. IEEE Int. Conf. on Neural Networks, pp. 1942–1948 (1995)
Bell, C.E., Han, J.: A new heuristic solution method in resource- constrained project scheduling. J. Naval Research Logistics 38, 315–331 (1991)
Carlos, A.C.: Handling Multiple Objectives With Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation 8(3), 264–280 (2004)
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Liu, Ll., Shu, Zs., Sun, Xh., Yu, T. (2010). Optimum Distribution of Resources Based on Particle Swarm Optimization and Complex Network Theory. In: Li, K., Fei, M., Jia, L., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science, vol 6329. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15597-0_12
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DOI: https://doi.org/10.1007/978-3-642-15597-0_12
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