References
- Abdel Azeem SA, Hossam EH, Ahmed HI. 2014a. Forecasting project schedule performance using probabilistic and deterministic models. Housing Build Res Center J. 10(1):35–42.
- Abdel Azeem SA, Hossam EH, Ahmed HI. 2014b. Probabilistic time forecasting using beta distribution. IOSR J Mech Civil Eng. 11(5):106–117.
- Abdelazeem AS, Ibrahim AH, Hosny HE. 2016. Probabilistic forecasting of schedule performance using polynomial function. Int J Inform Decis Sci. 8(4):358–377.
- Abdelazeem SA. 2019. Forecasting of project cost and schedule performance using probabilistic and deterministic models [Ph.D. dissertation]. Faculty of Engineering, Zagazig University, Zagazig, Egypt.
- Akther T, Ahmad SU. 1970. A computational method for fuzzy arithmetic operations. Daffodil Int Uni J Sci Technol. 4(1):18–22.
- Anbari F. 2003. Earned value method and extensions. Project Manage J. 34(4):12–23.
- Association for the Advancement of Cost Engineering (AACE). 2005. Cost estimate classification system—as applied in engineering, procurement, and construction for the process industries. AACE International, International Recommended Practice 18R-97.
- Barraza GA, Back WE, Mata F. 2000. Probabilistic monitoring of project performance using SS-curves. J Constr Eng Manage. 126(2):142–148.
- Barraza GA, Back WE, Mata F. 2004. Probabilistic forecasting of project performance using stochastic S curves. J Constr Eng Manage. 130(1):25–32.
- Bullinaria JA. 2015. Bias and variance, under-fitting and over-fitting. Neural Computation: Lecture 9, p. 16. https://www.cs.bham.ac.uk/∼jxb/inc.html.
- Chao LC, Chien CF. 2009. Estimating project S-curves using polynomial function and neural networks. J Constr Eng Manage. 135(3):169–177.
- Cheng M-Y, Chang Y-H, Korir D. 2019. Novel approach to estimating schedule to completion in construction projects using sequence and non-sequence learning. J Constr Eng Manage. 145(11):04019072.
- Choksi B, Venkitaraman A, Mali S. 2017. Finding best fit for hand-drawn curves using polynomial regression. Int J Comput Appl. 174(5):20–23.
- Christensen DS, Heise SR. 1993. Cost performance index stability. Natl Contract Manage J. 25(1):7–15.
- Dubois D, Prade H. 1980. Fuzzy sets and systems: theory and applications. New York: Academic Press.
- Jacob D. 2003. Forecasting project schedule completion with earned value metrics. The Measurable News, March, 7–9.
- Jacob DS, Kane M. 2004. Forecasting schedule completion using earned value metrics revisited. The Measurable News, Summer, 11–7.
- Keefer DL, Bodily SE. 1983. Three-point approximations for continuous random variables. Manage Sci. 29(5):595–609.
- Khamooshi H, Abdi A. 2017. Project duration forecasting using earned duration management with exponential smoothing techniques. J Manage Eng. 33(1):04016032.
- Kim BC, Reinschmidt KF. 2009. Probabilistic forecasting of project duration using Bayesian inference and the beta distribution. J Constr Eng Manage. 135(3):178–186.
- Kim BC, Reinschmidt KF. 2010. Probabilistic forecasting of project duration using Kalman filter and the earned value method. J Constr Eng Manage. 136(8):834–832.
- Kim BC, Reinschmidt KF. 2011. Combination of project cost forecasts in earned value management. J Constr Eng Manage. 137(11):958–966.
- Liang Y, Ashuri B, Sun W. 2020. Analysis of the variability of project cost and schedule performance in the design-build environment. J Constr Eng Manage. 146(6):04020060.
- Lipke WH. 2003. Schedule is different. The Measurable News, Summer, 31–34.
- Mathcentre. 2009. Polynomial functions. www.mathcentre.ac.uk.
- Matlab R. 2009. User guides of Matlab software. MathWorks, License Number, 161051.
- Mortaji STH, Bagherpour M, Noori S. 2013. Fuzzy earned value management using L-R fuzzy numbers. J Intell Fuzzy Syst. 24 (2):323–332.
- Naeni LM, Shadrokh S, Salehipour A. 2011. A fuzzy approach for the earned value management. Int J Project Manage. 29(6):764–772.
- Navon R. 1996. Cash flow forecasting and updating for building projects. Project Manage J. 27(2):14–23.
- Oberlender GD, Trost SM. 2001. Predicting accuracy of early cost estimates based on estimate quality. J Constr Eng Manage. 127(3):173–182.
- Perry C, Greig ID. 1975. Estimating the mean and variance of subjective distributions in PERT and decision analysis. Manage Sci. 21(12):1477–1480.
- Project Management Institute (PMI). 2008. A guide to the project management body of knowledge (PMBOK). 4th ed. Newtown Square, PA: Project Management Institute.
- Ross TG. 2004. Fuzzy logic with engineering applications. 2nd ed. Chichester, West Sussex: John Wiley & Sons, Ltd.
- Salari M, Bagherpour M, Kamyabniya A. 2014. Fuzzy extended earned value management: a novel perspective. J Intell Fuzzy Syst. 27(3):1393–1406.
- Schexnayder CJ, Mayo R. 2003. Construction management fundamentals. Boston: McGraw-Hill.
- Skitmore M. 1992. Parameter prediction for cash flow forecasting models. J Constr Manage Econ. 10(5):397–413.
- Sutrisna M, Pellicer E, Torres-Machi C, Picornell M. 2020. Exploring earned value management in the Spanish construction industry as a pathway to competitive advantage. Int J Constr Manage. 20(1):1–12.
- Tran TTK, Lee T, Shin J, Kim J, Kamruzzaman M. 2020. Deep learning-based maximum temperature forecasting assisted with meta-learning for hyperparameter optimization. Atmosphere 11(5):487–508. DOI: 10.3390/atmos11050487.
- Vandevoorde S, Vanhoucke M. 2006. A comparison of different project duration forecasting methods using earned value metrics. Int J Project Manage. 24(4):289–302.
- Vanhoucke M. 2012. Project management with dynamic scheduling: baseline scheduling, risk analysis and project control. 2nd ed. Springer.
- Zadeh L. 1965. Fuzzy sets. Inf Control. 8(3):338–353.
- Zwikael O, Globerson S, Raz T. 2000. Evaluation of models for forecasting the final cost of a project. Project Manage J. 31(1):53–57.