A fuzzy stochastic technique for project selection

Eric Wong, George Norman () and Roger Flanagan

Construction Management and Economics, 2000, vol. 18, issue 4, 407-414

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.

Keywords: Project Risk Multi-ATTRIBUTE Utility Theory Fuzzy Analysis Stochastic Dominance (search for similar items in EconPapers)
Date: 2000
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/01446190050024824 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:conmgt:v:18:y:2000:i:4:p:407-414

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RCME20

DOI: 10.1080/01446190050024824

Access Statistics for this article

Construction Management and Economics is currently edited by Will Hughes

More articles in Construction Management and Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().