Pre-Project Neuro-Fuzzy Decision Support Model for Oil Industry Projects
During pre-project planning as an essential phase of a project, fundamental decisions that lead to project success or failure will make. This phase of a project is more important essentially in oil, gas and petrochemical mega projects that tremendous amount of resources should consume. Uncertainty i...
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doaj-6ecd5da284f4446bac77b299a07913dc2020-11-25T01:32:14ZfasUniversity of Tehranمدیریت صنعتی2008-58852423-53692015-12-017483786010.22059/imj.2015.5742857428Pre-Project Neuro-Fuzzy Decision Support Model for Oil Industry ProjectsMahmood Golabchi0Amir Faraji1Prof. Faculty of Architecture, University of Tehran, Tehran, IranPh.D. Student in Project Management and Construction, Faculty of Architecture, University of Tehran, Tehran, IranDuring pre-project planning as an essential phase of a project, fundamental decisions that lead to project success or failure will make. This phase of a project is more important essentially in oil, gas and petrochemical mega projects that tremendous amount of resources should consume. Uncertainty in the initial phases of the project is at the highest level and therefore major project decisions should be made based on the minimum level of information and assurance of future. In this paper, a performance forecasting model for oil industry projects proposed that based on Neuro-fuzzy inference systems and rooted in project progress functions which known as S curve models. In this study types of functions and models that can generate project S curves are investigated and nine most used functions identified. In the next step six performance variables in two main sections include project progress and resource growth recognized and 25 variables in two categories and four clusters using close questionnaire approach identified. Finally a model for project performance prediction based on Adaptive Neuro-Fuzzy Inference System developed.https://imj.ut.ac.ir/article_57428_876f8e282a2a0e7bc68795bd1e466f2b.pdfforecastingNeuro-Fuzzy ModelOil Industry ProjectsProject S CurveStrategic Decisions |
collection |
DOAJ |
language |
fas |
format |
Article |
sources |
DOAJ |
author |
Mahmood Golabchi Amir Faraji |
spellingShingle |
Mahmood Golabchi Amir Faraji Pre-Project Neuro-Fuzzy Decision Support Model for Oil Industry Projects مدیریت صنعتی forecasting Neuro-Fuzzy Model Oil Industry Projects Project S Curve Strategic Decisions |
author_facet |
Mahmood Golabchi Amir Faraji |
author_sort |
Mahmood Golabchi |
title |
Pre-Project Neuro-Fuzzy Decision Support Model for Oil Industry Projects |
title_short |
Pre-Project Neuro-Fuzzy Decision Support Model for Oil Industry Projects |
title_full |
Pre-Project Neuro-Fuzzy Decision Support Model for Oil Industry Projects |
title_fullStr |
Pre-Project Neuro-Fuzzy Decision Support Model for Oil Industry Projects |
title_full_unstemmed |
Pre-Project Neuro-Fuzzy Decision Support Model for Oil Industry Projects |
title_sort |
pre-project neuro-fuzzy decision support model for oil industry projects |
publisher |
University of Tehran |
series |
مدیریت صنعتی |
issn |
2008-5885 2423-5369 |
publishDate |
2015-12-01 |
description |
During pre-project planning as an essential phase of a project, fundamental decisions that lead to project success or failure will make. This phase of a project is more important essentially in oil, gas and petrochemical mega projects that tremendous amount of resources should consume. Uncertainty in the initial phases of the project is at the highest level and therefore major project decisions should be made based on the minimum level of information and assurance of future. In this paper, a performance forecasting model for oil industry projects proposed that based on Neuro-fuzzy inference systems and rooted in project progress functions which known as S curve models. In this study types of functions and models that can generate project S curves are investigated and nine most used functions identified. In the next step six performance variables in two main sections include project progress and resource growth recognized and 25 variables in two categories and four clusters using close questionnaire approach identified. Finally a model for project performance prediction based on Adaptive Neuro-Fuzzy Inference System developed. |
topic |
forecasting Neuro-Fuzzy Model Oil Industry Projects Project S Curve Strategic Decisions |
url |
https://imj.ut.ac.ir/article_57428_876f8e282a2a0e7bc68795bd1e466f2b.pdf |
work_keys_str_mv |
AT mahmoodgolabchi preprojectneurofuzzydecisionsupportmodelforoilindustryprojects AT amirfaraji preprojectneurofuzzydecisionsupportmodelforoilindustryprojects |
_version_ |
1725082404023959552 |