PROJECT TIME AND COST FORECASTING USING MONTE CARLO SIMULATION AND ARTIFICIAL NEURAL NETWORKS

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AKHBARI MAHDIEH

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Journal Paper

Paper Information

Journal: International Journal of Industrial Engineering & Production Research Year:2018 | Volume:29 | Issue:2 Page(s): 231-239

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Title

PROJECT TIME AND COST FORECASTING USING MONTE CARLO SIMULATION AND ARTIFICIAL NEURAL NETWORKS

Author(s)

AKHBARI MAHDIEH | Issue Writer Certificate

Keywords

PROJECT MANAGEMENT

EARNED VALUE MANAGEMENT

ESTIMATE AT COMPLETION (EAC)

MONTE CARLO SIMULATION

ARTIFICIAL NEURAL NETWORK

Abstract

The aim of this study is to present a new method to predict project time and cost under uncertainty. Assuming that what happens in projects implementation which is expressed in the form of EARNED VALUE MANAGEMENT (EVM) indicators is primarily related to the nature of randomness or unreliability, in this study, by using MONTE CARLO SIMULATION and assuming a specific distribution for the time and cost of project activities, a significant number of predicting scenarios will be simulated. According to the data, an ARTIFICIAL NEURAL NETWORK is used as an efficient data mining method to estimate the project time and cost at completion.

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APA: Copy

AKHBARI, MAHDIEH. (2018). PROJECT TIME AND COST FORECASTING USING MONTE CARLO SIMULATION AND ARTIFICIAL NEURAL NETWORKS. INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION RESEARCH (IJIE) (ENGLISH), 29(2), 231-239. SID. https://sid.ir/paper/706601/en

Vancouver: Copy

AKHBARI MAHDIEH. PROJECT TIME AND COST FORECASTING USING MONTE CARLO SIMULATION AND ARTIFICIAL NEURAL NETWORKS. INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION RESEARCH (IJIE) (ENGLISH)[Internet]. 2018;29(2):231-239. Available from: https://sid.ir/paper/706601/en

IEEE: Copy

MAHDIEH AKHBARI, “PROJECT TIME AND COST FORECASTING USING MONTE CARLO SIMULATION AND ARTIFICIAL NEURAL NETWORKS,” INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION RESEARCH (IJIE) (ENGLISH), vol. 29, no. 2, pp. 231–239, 2018, [Online]. Available: https://sid.ir/paper/706601/en

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