Multi-Attribute Decision Making in Contractor Selection Under Hybrid Uncertainty
Arbaiy Nureize*,** and Junzo Watada*
*Graduate School of Information, Production and System, Waseda University, 2-7 Hibikino, Wakamatsu, Kitakyushu, Fukuoka 808-0135, Japan
**Faculty of Science Computer and Information Technology, University Tun Hussein Onn Malaysia, 86400 Batu Pahat, Johor, Malaysia
The successful of a construction industry project depends on contractor evaluation and selection. Further, human judgment and unknown evaluation risk make evaluation and selection increasingly complex. Such situations show that a contractor selection is influenced by multiple attributes that often have the hybrid uncertainty of fuzziness and probability. The objective of this study is therefore to propose a fuzzy random variable based multi-attribute decision scheme that enables us to solve such problems within the bounds of hybrid uncertainty by using a fuzzy random regression model. The proposed model is explained in examples and its usefulness is clarified. This decision model is facilitated in its use by evaluating alternatives and enables us to indicate the optimum choice in the presence of hybrid uncertainty.
-  Construction Industry Development Board, “Construction Industry Master Plan Malaysia 2006-2015,” 2007.
-  E. Palaneeswaran and M. Kumaraswamy, “Recent advances and proposed improvements in contractor prequalification methodologies,” Building and Environment, Vol.36, No.1, pp. 73-87, 2001.
-  Z. Hatush and M. Skitmore, “Contractor selection using multicriteria utility theory: an additive model,” Building and Environment, Vol.33, No.2-3, pp. 105-115, 1998.
-  G. D. Holt, P. O. Olomolaiye, and F. C. Harris, “Factors influencing U.K. construction clients choice of contractor,” Building and Environment, Vol.29, No. 2, pp. 241-248, 1994.
-  G. D. Holt, “Which contract selection methodology,” Int. J. of Project Management, Vol.16, No.3, pp. 153-164, 1998.
-  Y. Li, S. Chen, and X. Nie, “Fuzzy Pattern Recognition Approach to Construction Contractor Selection Export,” Fuzzy Optimization and Decision Making, Vol.4, No.2, pp. 103-118, 2005.
-  M. M. Kumaraswamy and J. M. Matthews, “Improved subcontractor selection employing partnering principles,” ASCE J.
-  J. E. Diekmann, “Cost-Plus Contractor Selection: A Case Study,” J. of the Technical Councils of ASCE, Vol.107, No.1, pp. 13-25, 1981.
-  G. D. Holt, P. O. Olomolaiye, and F. C. Harris, “Applying Multi-Attribute Analysis to Contractor Selection Decision,” European J. of Purchasing and Supply Management, Vol.1, No.3, pp. 139-148, 1995.
-  D. Singh and R. L. K. Tiong, “A Fuzzy Decision Framework for Contractor Selection,” J. of Construction Engineering and Management, Vol.131, No.1, pp. 62-70, 2005.
-  V. U. Nguyen “Tender evaluation by fuzzy sets,” J. of Construction Engineering and Management, Vol.111, No.3, pp. 231-43, 1985.
-  J. S. Russell and M. J. Skibniewski, “Decision criteria in contractor prequalification,” J. of Management in Engineering, Vol.4, No.2, pp. 148-164, 1988.
-  E. K. Zavadskas, Z. Turskis, and J. Tamoaitiene, “Contractor selection of construction in a competitive environment,” J. of Business Economics and Management, Vol.9, No.3, pp. 181-187, 2008.
-  M. M. Kumaraswamy and D. H. T. Walker, “Multiple performance criteria for evaluating construction contractors,” Procurement Systems – A Guide to Best Practice in Construction, London: E & F N Spon, pp. 228-251, 1999.
-  K. C. Lam, S. T. Ng, T. S. Hu, M. Skitmore, and S. O. Cheung, “Decision support system for contractor prequalification – artificial neural network model,” Engineering, Construction and Architectural Management, Vol.7, No.3, pp. 251-266, 2000.
-  H. Li, L. Y. Shen, and P. E. D. Love, “ANN-based mark-up estimation system with self-explanatory capacities,” J. of Construction Engineering and Management, Vol.125, No.3, pp. 185-189, 1999.
-  C. H. Wong, “A contractor performance prediction model for the UK construction contractor: the study of logistic regression approach,” J. of Construction Engineering and Management, ASCE, Vol.130, No.5, pp. 691-698, 2004.
-  Y.-T. Tan, L. Shen, A. G. Khalid, and S.-C. Song, “An examination of the factors affecting contractors’ competition strategy: a Hong Kong study,” Int. J. of Project Organization and Management, Vol.1, No.1, pp. 4-23, 2008.
-  V. Albino and A. C. Garavelli, “A neural network application to subcontractor rating in construction firms,” Int. J. of Project Management, Vol.16, No.1, pp. 9-14, 1998.
-  M.-Y. Cheng, M.-H. Tsai, and Z.-W. Xiao, “Construction management process reengineering: Organizational human resource planning for multiple projects,” Automation in Construction, Vol.15, No.6, pp. 785-799, 2006.
-  M. Schieg, “Post-mortem analysis on the analysis and evaluation of risks in construction project management,” J. of Business Economics and Management, Vol.8, No.2, pp. 145-153, 2007.
-  H. Tanaka, I. Hayashi, and J. Watada, “Possibilistic linear regression for fuzzy data,” European J. of Operational Research, Vol.40, No.3, pp. 389-396, 1989.
-  J. Watada and H. Tanaka, “The perspective of possibility theory in decision making,” Post Conference Book, Multiple Criteria Decision Making – Toward Interactive Intelligent Decision Support Systems, VII-th Int. Conf. (Eds.), by Y. Sawaragi, K. Inoue, and H. Nakayama, Springer-Verlag, pp. 328-337, 1986.
-  J.Watada, “Possibilistic time-series analysis and its analysis of consumption,” In: D. Dubois, M. M. Yager (Eds.), Fuzzy Information Engineering, John Wiley & Sons, pp. 187-200, 1996.
-  A. Nureize and J. Watada, “A fuzzy regression approach to hierarchical evaluation model for oil palm grading,” Fuzzy Optimization Decision Making, Vol.9, No.1, pp. 105-122, 2010.
-  S. Nahmias, “Fuzzy variables,” Fuzzy Sets and Systems, Vol.1, No.2, pp. 97-111, 1978.
-  B. Liu and Y.-K. Liu, “Expected value of fuzzy variable and fuzzy expected value models,” IEEE Trans. on Fuzzy Systems Vol.10, No.4, pp. 445-450, 2002.
-  J. Watada, “Applications in business, multi-attribute decision making,” In: T. Terano, K. Asai, M. Sugeno (Eds.), Applied Fuzzy System. AP Professional, pp. 244-252, 1994.
-  J.Watada, S.Wang, andW. Pedrycz, “Building confidence-intervalbased fuzzy random regression model,” IEEE Trans. on Fuzzy Systems, Vol.11, No.6, pp. 1273-1283, 2009.
-  J.Malczewski, “Propagation of errors in multicriteria location analysis: a case study. Multiple Criteria Decision Making,” Proc. of the Twelfth Int. Conf. Hagen (Germany): 1995. G. Fandel and T. Gal. Berlin: Springer-Verlag. pp. 154-165, 1997.
-  T. L. Saaty, “The analytic hierarchy process,” McGraw-Hill, New York, 1980.
-  R. L. Keeney and H. Raiffa, “Decisions with multi-objectives,” John Wiley & Sons, New York, 1976.
-  R. Yager, “On ordered weighted averaging aggregation operators in multi-criteria decision making,” IEEE Trans. on Systems, Man, and Cybernetics, Vol.18, No.1, pp. 183-190, 1988.
-  C.W. Chelsea and A. H. Hillary, “Resolvability for imprecise multiattribute alternative selection,” IEEE Trans. on Systems, Man, and Cybernetics-Part A: Systems and Humans, Vol.38, No.1, pp. 162-169, 2008.
-  O. Kulak and C. Kahraman, “Multi-attribute comparison of advanced manufacturing systems using fuzzy vs. crisp axiomatic design approach,” Int. J. of Production Economics, Vol.95, No.3, pp. 415-424, 2005.
-  J. Pan, Y. Teklu, and S. Rahman, “An interval-based MADM approach to the identification of candidate alternatives in strategic resource planning,” IEEE Trans. on Power Systems, Vol.15, No.4, pp. 1441-1446, 2000.
-  A. Tanja and J. B. Borka, “Application of multi-attribute decision making approach to learning management system evaluation,” J. of Computer, Vol.2, No.10, pp. 28-37, 2007.
-  A. L. Wilson, K. Ramamurthy, and C. N. Paul, “A multi-attribute measure for innovation adoption: the context of imaging technology,” IEEE Trans. on Engineering Management, Vol.46, No.3, pp. 311-321, 1999.
-  B. S. Ahn, “Multiattribute decision aid with extended ISMAUT,” IEEE Trans. on Systems, Man and Cybernetics, Part A, Vol.36, No.3, pp. 507-520, 2006.
-  K.-C. Hung, G. K. Yang, P. Chu, and W. T.-H. Jin, “An enhanced method and its application for fuzzy multi-criteria decision making based on vague sets,” Computer-Aided Design, Vol.40, No.4, pp. 447-454, 2008.
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