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Edwards, D J, Holt, G D and Harris, F C (2000) Estimating life cycle plant maintenance costs. Construction Management and Economics, 18(04), 427-35.

Lingard, H, Graham, P and Smithers, G (2000) Employee perceptions of the solid waste management system operating in a large Australian contracting organization: implications for company policy implementation. Construction Management and Economics, 18(04), 383-93.

Loosemore, M, Nguyen, B T and Denis, N (2000) An investigation into the merits of encouraging conflict in the construction industry. Construction Management and Economics, 18(04), 447-56.

Love, P E D and Li, H (2000) Quantifying the causes and costs of rework in construction. Construction Management and Economics, 18(04), 479-90.

Manavazhi, M R (2000) Hybrid modelling framework for synthesizing virtual structures. Construction Management and Economics, 18(04), 415-26.

Ranasinghe, M (2000) Impact of correlation and induced correlation on the estimation of project cost of buildings. Construction Management and Economics, 18(04), 395-406.

Shapira, A and Goldfinger, D (2000) Work-input model for assembly and disassembly of high shoring towers. Construction Management and Economics, 18(04), 467-77.

Tah, J H M and Carr, V (2000) A proposal for construction project risk assessment using fuzzy logic. Construction Management and Economics, 18(04), 491-500.

Tam, C M, Deng, Z M, Zeng, S X and Ho, C S (2000) Quest for continuous quality improvement for public housing construction in Hong Kong. Construction Management and Economics, 18(04), 437-46.

Wanous, M, Boussabaine, A H and Lewis, J (2000) To bid or not to bid: a parametric solution. Construction Management and Economics, 18(04), 457-66.

Wong, E T T, Norman, G and Flanagan, R (2000) A fuzzy stochastic technique for project selection. Construction Management and Economics, 18(04), 407-14.

  • Type: Journal Article
  • Keywords: fuzzy analysis; multi-attribute utility theory; project risk; stochastic dominance
  • ISBN/ISSN: 0144-6193
  • URL: https://doi.org/10.1080/01446190050024824
  • Abstract:

    The comparison of two or more risky projects is more of a challenge than the evaluation of one project in isolation. In the numerous decision models and methods suggested in the literature, often it is assumed that the criteria as well as the decision maker’s preference or utility function can be crisply defined. Multi-attribute decision aids that permit the consideration of both multi- variables and risks generally have been associated with complex mathematics and heavy consumption of resources. This paper shows how project selection problems can be dealt with when some project attributes are subject to random variations. The method incorporates fuzzy analysis into multi-attribute utility theory. The aggregate utility function for an individual project is derived as a fuzzy number (or interval) which, in turn, yields probabilistic information for stochastic dominance tests. A unique feature of the approach is that it dispenses with the task of selecting probability distributions for aggregate utility functions. A comparison of the proposed method with the expected utility approach was made and the findings showed agreement between the results.