Research of Intelligent Warehouse Layout Base on Genetic Algorithm and Dynamic Programming

Article Preview

Abstract:

Intelligent warehouse management can effectively improve the work efficiency and decreasing operating costs for a company, and the reasonableness of warehouse layout has played a very important role in it. This paper will aim at the warehouse layout problem, analyze the superiority of genetic algorithm and dynamic programming for the problem, and base on the genetic algorithm, integrating the thoughts of dynamic programming to put forward an optimization strategy. It can improve the accuracy and efficiency of genetic algorithm by a modified encoding scheme and fitness function, in order to put forward a more optimal scheme of warehouse layout, provide the information base for following operation of warehouse management, and improve the intelligent level of warehouse management system.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

1547-1550

Citation:

Online since:

July 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] J.H. Holland: Adaptation in Natural and Artificial Systems (University of Michigan Press, US 1975).

Google Scholar

[2] Zhang Wenxiu, Liang Yi: Mathematics Foundation of Genetic Algorithm (Xi'an Jiaotong University, Xi'an 2000).

Google Scholar

[3] Qiao Jianzhong, Lei Weimin, Li Benren and Teng Hongfei: Journal of Chinese Computer Systems Vol. 19-12 (1998), p.14.

Google Scholar

[4] Xu Zongben, Zhang Jiangshe and Zheng Yalin: Bionics in Computational Intelligence: Theory and Algorithm (Science Press, Beijing 2003).

Google Scholar

[5] Wang Junxiang: Computer Knowledge and Technology Vol. 36 (2006), p.150.

Google Scholar

[6] M. Srinvivas and L.M. Patnaik: IEEE Trans on Systems, Man, and Cybernetics Vol. 24-4 (1994), p.656.

Google Scholar

[7] C.J. Potts and Terrid: IEEE Trans on Systems, Man, and Cybernetics Vol. 24-1 (1994), p.73.

Google Scholar

[8] D.E. Goldberg: Communications of the ACM Vol. 37-3 (1994), p.113.

Google Scholar

[9] J.R. Koza: Genetic Programming: On the Programming of Computers by Means of Natural Selection (M IT Press, UK 1992).

Google Scholar

[10] Tang Fei and Teng Hongfei: Journal Of Software Vol. 10-10 (1999), p.1096.

Google Scholar

[11] Cha Zhiqin, Gao Bo and Zheng Chengzeng: Journal of Computer Applications Vol. 23-7 (2003), p.137.

Google Scholar