Research of WBS Construction Technology for Large Aircraft Based on Artificial Neural Network

Abstract:

Article Preview

To ensure development work breakdown comprehensive and thorough for large aircraft product, this paper put forward a WBS decomposition technique based on artificial neural network. On the basis of analysis of the neural network model and work breakdown structure (WBS), project control work breakdown structure (PCWBS), functional work breakdown structure (FWBS), relational work breakdown structure (RWBS), I set up a model which could get PCWBS, FWBS, RWBS and then get WBS according to the knowledge of the similar aircraft development WBS decomposition, so as to realize the automatic acquisition of WBS by input the general project attribute, which replaced the traditional state of depends on the personnel’s experience, and improve efficiency. Based on this, a prototype system is developed, and has been validated by a large aircraft WBS’s generation.

Info:

Periodical:

Edited by:

Zhengyi Jiang, Yugui Li, Xiaoping Zhang, Jianmei Wang and Wenquan Sun

Pages:

812-818

Citation:

K. F. Wang et al., "Research of WBS Construction Technology for Large Aircraft Based on Artificial Neural Network", Applied Mechanics and Materials, Vols. 220-223, pp. 812-818, 2012

Online since:

November 2012

Export:

Price:

$41.00

[1] Greg Indelicato, A guide to the project management body of knowledge. Project Management Journal, Volume 40, Issue 2, page 104, June (2009).

[2] Miao He and Haicheng Yang, in: Decomposition Method of WBS Based on PBS for Complex Products. China Mechanical Engineering, Volume 22, Issue 16, page 1960, August (2011).

[3] Bing Feng and Shuofang Zheng in: WBS Building Methods of Large Civil Airliner Development Project. Project Management Technology, Volume 8, Issue 1, page 66, January (2010).

[4] Heng He and Jiazhi Deng, in: WBS Supporting Complex Product Development. Aeronautical Manufacturing Technology, Issue 12, page 74, (2009).

[5] Taotao Pang, Jianbing Yao and Liming Du, in: Artificial Neural Networks for the Identification of Infrared Spectra of Ilex Kudingcha. Spectroscopy and Spectral Analysis, Volume 27, Issue 7, page 1336, July (2007).

[6] Yongsheng Cao, in: Spectrophotometric Simultaneous Determination of Phenol, Resorcinol and m-Aminophenol in Environmental Wastewater by BP-ANN. Spectroscopy and Spectral Analysis, Volume 25, Issue 8, page 1230, August (2005).