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Discrete Dynamics in Nature and Society
Volume 2013 (2013), Article ID 537675, 7 pages
An Inventory Controlled Supply Chain Model Based on Improved BP Neural Network
Research Center of Cluster and Enterprise Development, School of Business Administration, Jiangxi University of Finance & Economics, Nanchang 330013, China
Received 22 June 2013; Revised 8 September 2013; Accepted 10 October 2013
Academic Editor: Zhigang Jiang
Copyright © 2013 Wei He. 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.
- P. W. Balsmeier and W. J. Voisin, “Supply chain management: a time-based strategy,” Industrial Management, vol. 38, no. 5, pp. 24–27, 1996.
- S. Minner, “Multiple-supplier inventory models in supply chain management: a review,” International Journal of Production Economics, vol. 81-82, pp. 265–279, 2003.
- K. Bansal, S. Vadhavkar, and A. Gupta, “Brief application description. A neural networks based forecasting techniques for inventory control applications,” Data Mining and Knowledge Discovery, vol. 2, no. 1, pp. 97–102, 1998.
- J. Shanmugasundaram, M. V. N. Prasad, S. Vadhavkar, and A. Gupta, “Use of recurrent neural networks for strategic data mining of sales information,” MIT Sloan 4347-02; Eller College Working Paper 1029-05, 2002.
- C. C. Reyes-Aldasoro, A. R. Ganguly, G. Lemus, and A. Gupta, “A hybrid model based on dynamic programming, neural networks, and surrogate value for inventory optimisation applications,” Journal of the Operational Research Society, vol. 50, no. 1, pp. 85–94, 1999.
- S. R. Hong, S. T. Kim, and C. O. Kim, “Neural network controller with on-line inventory feedback data in RFID-enabled supply chain,” International Journal of Production Research, vol. 48, no. 9, pp. 2613–2632, 2010.
- F. Y. Partovi and M. Anandarajan, “Classifying inventory using an artificial neural network approach,” Computers and Industrial Engineering, vol. 41, no. 4, pp. 389–404, 2002.
- J. Li, Y. Li, J. Xu, and J. Zhang, “Parallel training algorithm of BP neural networks,” in Proceedings of the 3rd World Congress on Intelligent Control and Automation, vol. 2, pp. 872–876, July 2000.
- D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning internal representations by error propagation,” in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, D. E. Rumelhart and J. L. McClelland, Eds., vol. 1, chapter 8, MIT Press, Cambridge, Mass, USA, 1986.
- N. Ampazis and S. J. Perantonis, “Two highly efficient second-order algorithms for training feedforward networks,” IEEE Transactions on Neural Networks, vol. 13, no. 5, pp. 1064–1074, 2002.
- K. Zhang, J. Xu, and J. Zhang, “A new adaptive inventory control method for supply chains with non-stationary demand,” in Proceedings of the 25th Control and Decision Conference (CCDC '13), pp. 1034–1038, Guiyang , China, May 2013.
- W. P. Wang, “A neural network model on the forecasting of inventory risk management of spare parts,” in Proceedings of the International Conference on Information Technology and Management Science (ICITMS '12), pp. 295–302, Springer, 2012.
- A. Mansur and T. Kuncoro, “Product inventory predictions at small medium enterprise using market basket analysis approach-neural networks,” Procedia Economics and Finance, vol. 4, pp. 312–320, 2012.
- Y. Huang, D. X. Sun, G. P. Xing, and H. Chang, “Criticality evaluation for spare parts based on BP neural network,” in Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence (AICI '10), vol. 1, pp. 204–206, October 2010.
- Z. Zheng, “Review on development of BP neural network,” Shanxi Electronic Technology, no. 2, pp. 90–92, 2008.
- H. Yu, W. Q. Wu, and L. Cao, “Improved BP algorithm and its application,” Computer Knowledge and Technology, vol. 19, no. 5, pp. 5256–5258, 2009.
- D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986.
- T. P. Vogl, J. K. Mangis, A. K. Rigler, W. T. Zink, and D. L. Alkon, “Accelerating the convergence of the back-propagation method,” Biological Cybernetics, vol. 59, no. 4-5, pp. 257–263, 1988.
- M. Riedmiller and H. Braun, “Direct adaptive method for faster backpropagation learning: the RPROP Algorithm,” in Proceedings of the IEEE International Conference on Neural Networks (ICNN '93), vol. 1, pp. 586–591, San Francisco, Calif, USA, April 1993.
- C. Charalambous, “Conjugate gradient algorithm for efficient training of artificial neural networks,” IEE Proceedings G, vol. 139, no. 3, pp. 301–310, 1992.
- M. F. Møller, “A scaled conjugate gradient algorithm for fast supervised learning,” Neural Networks, vol. 6, no. 4, pp. 525–533, 1993.
- F. D. Foresee and M. T. Hagan, “Gauss-Newton approximation to Bayesian learning,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1930–1935, June 1997.
- R. Battiti, “First and second order methods for learning: between steepest descent and newton's method,” Neural Computation, vol. 4, no. 2, pp. 141–166, 1992.
- Y. Gao, “Study on optimization algorithm of BP neural network,” Computer Knowledge and Technology, vol. 29, no. 5, pp. 8248–8249, 2009.
- S. Shah and F. Palmieri, “MEKA-A fast, local algorithm for training feed forward neural networks,” in Proceedings of the International Joint Conference on Neural Networks, pp. 41–46, June 1990.
- X. P. Wang, Y. Shi, J. B. Ruan, and H. Y. Shang, “Study on the inventory forecasting in supply chains based on rough set theory and improved BP neural network,” in Advances in Intelligent Decision Technologies Smart Innovation, Systems and Technologies, vol. 4, pp. 215–225, Springer, Berlin, Germany, 2010.
- H. Lican, Z. Yuhong, X. Xin, and F. Fan, “Prediction of investment on inventory clearance based on improved BP neural network,” in Proceedings of the 1st International Conference on Networking and Distributed Computing (ICNDC '10), pp. 73–75, Hangzhou, China, October 2010.