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Article type: Research Article
Authors: Sarvestani, Mohammad Reza Edalata | Shahraki, Mohammad Rezab | Anisseh, Mohammadc; *
Affiliations: [a] Department of Industrial Engineering, Sistan and Baluchestan University, Zahedan, Iran | [b] Department of Industrial Engineering, Sistan and Baluchestan University, Zahedan, Iran | [c] Department of Industrial Management, Imam Khomeini International University, Iran
Correspondence: [*] Corresponding author. Mohammad Anisseh, Assistant Professor, Department of Industrial Management, Imam Khomeini International University, Iran. Tel.: +98 9121467582; E-mail: [email protected].
Abstract: Since the successful performance of the projects results from their complicated nature and internal non-reliability, risk evaluation for gaining success in management and project performance, is a required factor. In reality, due to the complicated nature of the projects and lack of specialized manpower, attaining comprehensive information in connection with project risks shall be difficult, while there is a little information about them. The Jackknife resampling method is therefore used meticulously to solve such problems and estimate the society parameter. In this paper, non-parameter Jackknife resampling method has been used for interval estimation of risks, considering the concept of the confidence interval and opinions of the specialists. Then evaluation and classification of major project risks have been made, using interval analysis method. A case study has also been presented to prove the efficiency of the proposed method and finally the comparison of the results of this method with those of Bootstrap sampling methods were presented. The results of this research have indicated that the proposed method not only plays an important role in lowering the expenses and is timely economical, but also gives more meticulous results in proportion to the Bootstrap sampling method.
Keywords: Project risks ranking, jackknife resampling method, interval analysis, construction project
DOI: 10.3233/IFS-151877
Journal: Journal of Intelligent & Fuzzy Systems, vol. 30, no. 5, pp. 2593-2600, 2016
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