• Uniqueness (no similar experience)
⁎ Corresponding author at. Industrial Engineering Department, Bu-Ali Sina University, Hamedan, Iran.
E-mail addresses: email@example.com (V. Khodakarami), firstname.lastname@example.org (A. Abdi).
0263-7863/$36.00 © 2014 Elsevier Ltd. APM and IPMA. All rights reserved. http://dx.doi.org/10.1016/j.ijproman.2014.01.001 • Variability (trade-off between performance measure like time, cost, and quality)
• Ambiguity (lack of clarity, data, structure, and bias in estimates)
Cost uncertainty analysis is an important aspect of cost estimation that helps decision makers to understand not only the potential funding exposure but also the nature of risks for a particular project or program and possible responses to them. Without considering the uncertainty involved, there is a high risk that the actual cost of a project exceeds what it was originally anticipated, which in turn causes several other risks such as delays and performance problems (Elkjaer, 2000; Lai et al., 2008).
Several techniques including Regression modeling, Artifi- cial Neural Networks (ANNs), feature-based method (FBM) and case-based reasoning (CBR) are proposed for modeling risk and uncertainty in project cost analysis. Section 2 briefly reviews a number of notable techniques. However none of
1234 V. Khodakarami, A. Abdi / International Journal of Project Management 32 (2014) 1233–1245 these techniques capture the dependencies between project cost items consistent to real word conditions.