Title :
An Improved Method for Project Duration Forecasting
Author :
Chen, X.X. ; Liu, L. ; Li, Y.
Author_Institution :
Sch. of Econ. & Manage., Beihang Univ., Beijing, China
Abstract :
There are many factors affect the accuracy of project duration forecasting, the lack of relative information and the complexity of the project are two major aspects. To overcome these constraints and establish a feasible forecasting model, this paper presents an improved method to forecast the project duration, which combines the earned schedule and artificial neural network. We adopt the artificial neural network because of its ability to model complex nonlinear relationship without a priori assumption of the nature of the relationship. The performance of the developed models was evaluated. The results show that the artificial neural network can improve the accuracy of the project duration forecasting significantly in terms of the error evaluation measurements.
Keywords :
forecasting theory; neural nets; project management; artificial neural network; project complexity; project duration forecasting; project management; relative information; Artificial neural networks; Forecasting; Indexes; Mathematical model; Neurons; Predictive models; Schedules; artificial neural network; earned schedule; project duration forecasting;
Conference_Titel :
E-Business and E-Government (ICEE), 2010 International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
978-0-7695-3997-3
DOI :
10.1109/ICEE.2010.668