Cost estimation and sensitivity analysis on cost factors: a case study on Taylor Kriging, regression and artificial neural networks.: An article from: Engineering Economist

This digital document is an article from Engineering Economist, published by Institute of Industrial Engineers, Inc. (IIE) on July 1, 2010. The length of the article is 8116 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available immediately after purchase. You can view it with any web browser.

From the author: This article applies Taylor kriging (TK) to cost estimation. The partial differentiation equation of TK is developed and used to assist in sensitivity analysis on cost factors. The capabilities of cost estimation and sensitivity analysis of TK are compared with those of regression and an artificial neural network (ANN) through application in a case study. The results show that TK can provide more accurate cost estimates than those of regression but worse than those of ANN, and both TK and regression are able to effectively find sensitive cost factors.

Citation Details
Title: Cost estimation and sensitivity analysis on cost factors: a case study on Taylor Kriging, regression and artificial neural networks.
Author: Heping Liu
Publication:Engineering Economist (Magazine/Journal)
Date: July 1, 2010
Publisher: Institute of Industrial Engineers, Inc. (IIE)
Volume: 55 Issue: 3 Page: 201(24)

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