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A fuzzy stochastic technique for project selection

Author

Listed:
  • Eric Wong
  • George Norman
  • Roger Flanagan

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.

Suggested Citation

  • Eric Wong & George Norman & Roger Flanagan, 2000. "A fuzzy stochastic technique for project selection," Construction Management and Economics, Taylor & Francis Journals, vol. 18(4), pages 407-414.
    • Handle: RePEc:taf:conmgt:v:18:y:2000:i:4:p:407-414
    DOI: 10.1080/01446190050024824
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    Citations

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    Cited by:

    1. S. Meysam Mousavi & Fariborz Jolai & Reza Tavakkoli-Moghaddam, 2013. "A Fuzzy Stochastic Multi-Attribute Group Decision-Making Approach for Selection Problems," Group Decision and Negotiation, Springer, vol. 22(2), pages 207-233, March.
    2. Nina Begičević & Blaženka Divjak & Tihomir Hunjak, 2010. "Decision-making on prioritization of projects in higher education institutions using the analytic network process approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 18(3), pages 341-364, September.
    3. Yohannes Hagos Subagadis & Niels Schütze & Jens Grundmann, 2016. "A Fuzzy-Stochastic Modeling Approach for Multiple Criteria Decision Analysis of Coupled Groundwater-Agricultural Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(6), pages 2075-2095, April.
    4. Bibiana Alarcon & Antonio Aguado & Resmundo Manga & Alejandro Josa, 2010. "A Value Function for Assessing Sustainability: Application to Industrial Buildings," Sustainability, MDPI, vol. 3(1), pages 1-16, December.
    5. Boguslaw Nowak & Maciej Nowak & Tadeusz Trzaskalik, 2011. "Multicriteria decision aiding in project planning using dynamic programming and simulation," RePAd Working Paper Series UQO-DSA-wp2202011, Département des sciences administratives, UQO.

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