Selecting Appropriate Project Delivery System: Fuzzy Approach with Risk Analysis
Publication: Journal of Construction Engineering and Management
Volume 136, Issue 8
Abstract
The selection of an appropriate project delivery system that suits all project and owner needs is one of the key decisions to a successful project. Therefore, this decision should be made based on thorough analysis. In this paper, a fuzzy multiattribute decision-making (FMADM) model is developed. The model accounts for uncertainties and imprecision in the decision space as well as fuzziness in the nature of the decision attributes. The model utilizes fuzzy decision-making approach in order to evaluate the membership function corresponding to the utility of each project delivery alternative. Project delivery system alternatives are ranked using fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method based on their utility membership functions and by evaluating the distance of each project delivery alternative from fuzzy ideal solutions. In the TOPSIS method, alternatives are ranked based on their closeness coefficient (CC). In addition, the risk attitude of the decision maker is considered in the model by using derived utility membership functions corresponding to the risk attitude of the decision maker. The model is applied to a petrochemical project as a case study. In the case study, the model outcome that ranked Turnkey system as the best system conforms to the lessons learned by the decision maker from several past projects. Moreover, sensitivity analysis is done in the case study. The results show the significant value of the FMADM model for selecting appropriate project delivery system for projects.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The cooperation and assistance of PIDMCO Company and their executive director of the Third Ammonia and Urea branch are hereby acknowledged.
References
Alhazmi, T., and McCaffer, R. (2000). “Project procurement system selection model.” J. Constr. Eng. Manage., 126(3), 176–184.
Bonissone, P. P. (1982). “A fuzzy sets based linguistic approach: Theory and applications.” Approximate reasoning in decision analysis, M. M. Gupta and E. Sanchez, eds., North-Holland, Amsterdam, 329–339.
Cheung, S., Tsun-Ip, L., Yue-Wang, W., and Ka-Chi, L. (2001). “Improving objectivity in procurement selection.” J. Manage. Eng., 17(3), 132–139.
Construction Industry Institute. (2003). “Project delivery and contract strategy selection: A tool for owners.” Implementation resource 165-2, 2nd Ed., The University of Texas at Austin Press, Austin, Tex.
Fenton, N., and Wang, W. (2006). “Risk and confidence analysis for multi-criteria decision making.” Knowl. Base. Syst., 19, 430–437.
Gordon, C. (1994). “Choosing appropriate construction contracting method.” J. Constr. Eng. Manage., 120(1), 196–210.
Mahdi, I., and Alreshaid, K. (2005). “Decision Support system for selecting the proper delivery method using analytical hierarchy process (AHP).” Int. J. Proj. Manage., 23, 564–572.
Oyetunji, A. A., and Anderson, S. D. (2001). “Project delivery and contract strategy selection.” Research Rep. No. 165-12, Construction Industry Institute, The Univ. of Texas at Austin, Austin, Tex.
Oyetunji, A. A., and Anderson, S. D. (2006). “Relative effectiveness of project delivery and contract strategies.” J. Constr. Eng. Manage., 132(1), 3–13.
Saghafian, S., and Hejazi, S. R. (2005) “ Multi-criteria group decision making using a modified fuzzy TOPSIS procedure.” IEEE Proc., Int. Conf. on Computational Intelligence: For Modeling, Control and Automation (CIMCA 2005), IEEE, New York.
Yuan, Y. (1991). “Criteria for evaluating fuzzy ranking methods.” Fuzzy Sets Syst., 43, 139–157.
Zhang, G., and Zou, P. (2007). “Fuzzy Analytical hierarchy process risk assessment approach for joint venture construction projects in China.” J. Constr. Eng. Manage., 133(10), 771–779.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 136 • Issue 8 • August 2010
Pages: 923 - 930
Copyright
© 2010 ASCE.
History
Received: Aug 15, 2008
Accepted: Jan 20, 2010
Published online: Jan 22, 2010
Published in print: Aug 2010
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.