Volume 29, Issue 2 (IJIEPR 2018)                   IJIEPR 2018, 29(2): 231-239 | Back to browse issues page

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akhbari M. Project Time and Cost Forecasting using Monte Carlo simulation and Artificial Neural Networks. IJIEPR. 2018; 29 (2) :231-239
URL: http://ijiepr.iust.ac.ir/article-1-793-en.html
Department of Industrial Engineering, E-Branch, Islamic Azad University, Tehran, Iran , m_akhbari@iauec.ac.ir
Abstract:   (1363 Views)
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 efficient data mining methods to estimate the project time and cost at completion.
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  • Proposing a method to predict the time and cost at completion of the project under uncertainty.
  • Assuming the time and cost of the project activities are randomly determined by a statistical distribution.
  • Using Earned Value Management (EVM) indicators to project monitoring and control.
  • Using Monte Carlo simulation to generate the “universe” of possible projects.
  • Applying Artificial neural network (ANN) To predict cost and duration of the project.

Type of Study: Research | Subject: Project Control
Received: 2017/10/22 | Accepted: 2018/04/15 | Published: 2018/05/20

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