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.
California Department of Transportation (Caltrans), Project Risk Management Handbook, fourth ed. Caltrans, Office of Project Management Process Improvement, Sacramento, CA, 2007.
2.
CarrV. and TahJ.H.M., A fuzzy approach to construction project risk assessment and analysis: Construction risk management system, Advances in Engineering Software32 (2001), 847–857. DOI: 10.1016/S0965-9978(01)00036-9
3.
EbrahimnejadS., MousaviS.M. and MojtahediS.M.H., A fuzzy BOT project risk evaluation model in Iranian power plant industry, Singapore, In: Proc, 5th IEEE Int, Conf on Ind Eng Eng Management, 2008, pp. 1038–1042.
4.
EbrahimnejadS., MousaviS.M. and MojtahediS.M.H., A fuzzy decision making model for risk ranking with application to the onshore gas refinery, International Journal of Business Continuity and Risk Management1 (2009), 38–66. DOI: 10.1504/IJBCRM.2009.028950
5.
FangW., Bayesian theory and robust control strategy in risk management oflarge-scale engineering project, In: Proc, Int Conf Manage Ser Sci, IEEE, 2009, pp. 1–7. DOI: 10.1109/ICMSS.2009.5301136
6.
FaucherP. and FitzgibbonsK., Public demand and the management of technological risk in large-scale projects, Science and Public Policy20(3) (1993), 173–185. DOI: 10.1093/spp/20.3.173
7.
FloricelS. and MillerR., Strategizing for anticipated risks and turbulence in large-scale engineering projects, International Journal of Project Management19(8) (2001), 445–455. DOI: 10.1016/S0263-7863(01)00047-3
8.
FreundJ. and WalpoleR., Mathematical Statistics, Prentice Hall, Third Edition, 1980.
9.
HastakM. and BaimE.J., Risk factors affecting management and maintenance cost of urban infrastructure, Journal of Infrastructure Systems7(2) (2001), 67–76. DOI: 10.1061/(ASCE)1076-0342
10.
LeeE., ParkY. and ShinJ.G., Large engineering project risk management using a Bayesian belief network, Expert Systems with Applications36 (2009), 5880–5887. DOI: 10.1016/j.eswa.2008.07.057
11.
LongN.D., OgunlanaS., QuangT. and LamK.C., Large construction projects in developing countries: A case study from Vietnam, International Journal of Project Management22(7) (2004), 553–561. DOI: 10.1016/j.ijproman.2004.03.004
12.
MakuiA., MojtahediS.M.H. and MousaviS.M., Project risk identification and analysis based on group decision making methodology in a fuzzy environment, International Journal of Management Science and Engineering Management5(2) (2010), 108–118. DOI: 10.1080/17509653.2010.10671098
13.
MaleklyH., MousaviS.M. and HashemiH., A fuzzy integrated methodology for evaluating conceptual bridge design, Expert Systems with Applications37 (2010), 4910–4920. DOI: 10.1016/j.eswa.2009.12.024
14.
MillerR. and LessardD., Understanding and managing risks in large engineering projects, International Journal of Project Management19(8) (2001), 437–443. DOI: 10.2139/ssrn.289260
15.
MojtahediS.M.H., MousaviS.M. and MakuiA., Risk identification and analysis concurrently: Group decision making approach, In: Proc, 4th IEEE Int, Conf Manag Innov Technol (ICMIT), Thailand, 2008, pp. 299–304.
16.
MojtahediS.M.H., MousaviS.M. and MakuiA., Project risk identification and assessment simultaneously using multi-attribute group decision making technique, Safety Science48(4) (2010), 499–507. DOI: 10.1016/j.ssci.2009.12.016
17.
MooreR.E., Methods and Applications of Interval Analysis, Society for Industrial and Applied Mathematics, Philadelphia, PA, 1979.
18.
MorrisP.W.G. and PintoJ.K., The Wiley Guide to Managing Projects, John Wiley & Sons, Chichester, UK, 2004.
19.
MotawaI.A., AnumbaC.J. and El-HamalawiA., A fuzzy system for evaluating the risk of change in construction projects, Advances in Engineering Software37 (2006), 583–591. DOI: 10.1016/j.advengsoft.2006.01.006
20.
MousaviS.M.H., MaleklyH., HashemiH. and MojtahediS.M.H., A two-phase fuzzy decision making methodology for bridge scheme selection, In: Proc 5th IEEE Int, Conf Ind Eng Eng Manag, Singapore, 2008.
21.
MousaviR., Tavakkoli-MoghaddamH., HashemiS.M.H. and
Mojtahedi, A novel approach based on non-parametric resampling with interval analysis for large engineering project risks, Safety Science49 (2011), 340–1348. DOI: 10.1016/j.ssci.2011.05.004
22.
Project Management Institute, A Guide to the Project Management Body of Knowledge (PMBOK Guide), fourth ed, Proj Manage Inst, Newton Square, PA, 2008.
23.
RamachandranK.M. and TsokosC.P., Mathematical statistics with applications, Academic Press, Amsterdam, Boston, 2009.
24.
SayadiM.K., HeydariM. and ShahanaghiK., Extension of VIKOR method for decision making problem with interval numbers, Applied Mathematical Modelling33(5) (2009), 2257–2262. DOI: 10.1016/j.apm.2008.06.002
25.
TahJ.H.M. and CarrV., Towards a framework for project risk knowledge management in the construction supply chain, Advances in Engineering Software32 (2001), 835–846. DOI: 10.1016/S0965-9978(01)00035-7
26.
Tavakkoli-MoghaddamR., AzaronA., MousaviS.M., MojtahediS.M.H. and HashemiH., Risk assessment for highway projects using jackknife technique, Expert Systems with Applications38 (2011), 5514–5524. DOI: 10.1016/j.eswa.2010.10.085
27.
TseC.K., WongW.M. and ChuC.H., Risk consideration in the handling of a large scale engineering project, In: Proc, 4th Int Struct Eng Const Conf (ISEC4), 2008, pp. 1501–1506.
28.
ZayedT., AmerM. and PanJ., Assessing risk and uncertainty inherent in Chinese highway projects using AHP, International Journal of Project Management26(4) (2008), 408–419. DOI: 10.1016/j.ijproman.2007.05.012
29.
ZhangW., International Project Risk Management, Master Degree Thesis. Professional Management Science and Engineering Unit of Tianjin University, 2004.
30.
ZhouH.-B. and ZhangH., Dynamic risk management system for large project construction in china, In: Proc, Geo Florida Conf Adv Anal Model Des, 2010, pp. 1992–2001. DOI: 10.1061/41095(365)202
31.
ZoubirA.M. and BoashashB., The bootstrap and its application in signal processing, IEEE Signal Processing Magazine15(1) (1998), 56–76. DOI: 10.1109/79.647043
