Proposing an Optimum Model for Time Estimation of Construction Projects in Iranian Gas Refineries

Abstract

Time management can be effective in a project when the project schedule is based on comprehensive time scheduling. In the industries with complicated processes, many uncertainties and risks affect the timing of projects. Considering the very low reliability of the project planning in certainty-based approach, using more secure models for control and interact with uncertainty should be placed on the agenda. Iranian Gas Company has been using risk management to manage probable uncertainties in construction projects but in the field of possible uncertainties, actions are very scarce. This article aims to propose an optimum model based on the integrated risk management and fuzzy expert systems in order to provide comprehensive project time estimation and in this regard, reviews the results of the implementation of this model in construction projects of Iranian gas refineries. The results show that the proposed model increases the accuracy of time estimation about 8 to 24 percent.Time management can be effective in a project when the project schedule is based on comprehensive time scheduling. In the industries with complicated processes, many uncertainties and risks affect the timing of projects. Considering the very low reliability of the project planning in certainty-based approach, using more secure models for control and interact with uncertainty should be placed on the agenda. Iranian Gas Company has been using risk management to manage probable uncertainties in construction projects but in the field of possible uncertainties, actions are very scarce. This article aims to propose an optimum model based on the integrated risk management and fuzzy expert systems in order to provide comprehensive project time estimation and in this regard, reviews the results of the implementation of this model in construction projects of Iranian gas refineries. The results show that the proposed model increases the accuracy of time estimation about 8 to 24 percent

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