Study to Cost of Air Spares Support Based on IPSO

Xiaohua Wang; Aiqin Mu; Fuhong Wang; Zhongbing Tang

doi:10.4236/jtts.2012.21009

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Journal of Transportation Technologies > Vol.2 No.1, January 2012

Study to Cost of Air Spares Support Based on IPSO ()

Xiaohua Wang, Aiqin Mu, Fuhong Wang, Zhongbing Tang

DOI: 10.4236/jtts.2012.21009 PDF HTML 3,723 Downloads 6,460 Views

Abstract

Air spares support is general term of using and repairing of aircrafts which is the material foundation of aero technical support, its effectiveness influences operational effectiveness and equipments of aircrafts directly. Based on particle swarm optimization algorithm, a new model is proposed to optimize the distribution of the cost of air spares, it take the funds as resource and the improvement of performance efficiency as objective and deduces the expressions to get the best distribution plan. The results of experiments indicate that this model can make full use of the limited funds and obtain the highest efficiency of air spares support.

Keywords

Air Spares Support; Fund; Particle Swarm Optimization

Share and Cite:

X. Wang, A. Mu, F. Wang and Z. Tang, "Study to Cost of Air Spares Support Based on IPSO," Journal of Transportation Technologies, Vol. 2 No. 1, 2012, pp. 75-77. doi: 10.4236/jtts.2012.21009.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	X. C. Han, “The Air Spares Management,” Blue Sky Press, Beijing, 2003.
[2]	B. S. Blanchard “Logistics Engineering and Management,” Prentice Hall, New York, 1998.
[3]	R. C. Zhang and S. Z. Zhao, “Study to Optimize and Dis- tribute the Cost of Air Spares Support,” Journal of Bei- jing University of Aeronautics and Astronautics, Vol. 31, No. 1, 2005, pp. 102-104.
[4]	F. Guo, C. Y. Liu and X. X. Guo, “Optimal Distribution of Aircraft-Spares Support Cost Based on Dynamic Programming Algorithm,” Value Engineering, Vol. 25, No. 10, 2010, pp. 63-64.
[5]	H. D. Zhu and Y. Zhong, “Evaluation of Air Materials Support Effectiveness Based on Utility Function,” Logistics Technology, Vol. 26, No. 8, 2007, pp. 246-248.
[6]	J. Kennedy and R. C. Eberhart, “Particle Swarm Optimi- zation,” IEEE International Conference on Neural Net- work, Perth, 1995, pp. 1942-1948. doi:10.1109/ICNN.1995.488968
[7]	Y. Shi and R. C. Eberhart, “A Modified Particle Swarm Optimizer,” Proceedings of Congress on Evolutionary Computation, Indianapolis, 4-9 May 1998, pp. 79-83.