Advertisement

A Fuzzy Group Decision Support System for Projects Evaluation

  • Fahimeh Ramezani
  • Jie Lu
Part of the Communications in Computer and Information Science book series (CCIS, volume 297)

Abstract

In any organization there are some main goals and lots of projects for achieving these goals. For any organization, it is important to determine how much these projects affect on achieving the main goals. This paper proposes a new fuzzy multiple attribute-based decision support system (DSS) for evaluating projects in promoting the goals as such a selection may involve both quantitative and qualitative assessment attributes. There are many fuzzy ranking methods available to solve multi-attribute decision making (MADM) problems. Some are more suitable than other for particular decision problems. The proposed DSS has ability to choose the most appropriate fuzzy ranking method for solving given MADM problem. In addition it contains sensitivity analysis system which provides opportunity for analyzing the impacts of attributes’ weights and projects’ performance on achieving organizations’ goals. A DSS software prototype has been developed on the basis of the proposed DSS which can be applied for solving every FMADM problem which needs to rank some alternatives according to some attributes.

Keywords

Decision support systems FMADM Project Evaluation SAW TOPSIS 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Goletsis, Y., Psarras, J., Samouilidis, J.E.: Project Ranking in the Armenian Energy Sector Using a Multicriteria Method for Groups. Annals of Operations Research 120, 135–157 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Sanna, U., Atzeni, C., Spanu, N.: A fuzzy number ranking in project selection for cultural heritage sites. Journal of Cultural Heritage 9, 311–316 (2008)CrossRefGoogle Scholar
  3. 3.
    Imoto, S., Yabuuchi, Y., Watada, J.: Fuzzy regression model of R&D project evaluation. Applied Soft Computing 8, 1266–1273 (2008)CrossRefGoogle Scholar
  4. 4.
    Liang, Z., Yang, K., Sun, Y., Yuan, J., Zhang, H., Zhang, Z.: Decision support for choice optimal power generation projects: Fuzzy comprehensive evaluation model based on the electricity market. Energy Policy 34, 3359–3364 (2006)CrossRefGoogle Scholar
  5. 5.
    Chiang, T.A., Che, Z.H.: A fuzzy robust evaluation model for selecting and ranking NPD projects using Bayesian belief network and weight-restricted DEA. Expert Systems with Applications 37, 7408–7418 (2010)CrossRefGoogle Scholar
  6. 6.
    Buyukozkan, G., Ruan, D.: Evaluation of software development projects using a fuzzy multi-criteria decision approach. Mathematics and Computers in Simulation 77, 464–475 (2008)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Baykasoglu, A., Goc Ken, T., Kaplanoglu, V.: A Practical Approach to Prioritize Project Activities Through Fuzzy Ranking. Cybernetics and Systems. An International Journal 42, 165–179 (2011)Google Scholar
  8. 8.
    Saghaei, A., Didehkhani, H.: Developing an integrated model for the evaluation and selection of six sigma projects based on ANFIS and fuzzy goal programming. Expert Systems with Applications 38, 721–728 (2011)CrossRefGoogle Scholar
  9. 9.
    Mao, Y., Wu, W.: Fuzzy Real Option Evaluation of Real Estate Project Based on Risk Analysis. Systems Engineering Procedia 1, 228–235 (2011)CrossRefGoogle Scholar
  10. 10.
    Chen, S.J., Hwang, C.L.: Fuzzy multiple attribute decision making. Springer, Berlin (1992)Google Scholar
  11. 11.
    Ramezani, F., Memariania, A., Lu, J.: A Dynamic Fuzzy Multi-criteria Group Decision Support System for Manager Selection. In: Proceedings of Intelligent Systems and Knowledge Engineering (ISKE), pp. 265–274. Springer, Heidelberg (2011)Google Scholar
  12. 12.
    Ramezani, F., Lu, J.: A new approach for choosing the most appropriate fuzzy ranking algorithm for solving MADM problems. In: Proceedings of the PhD Seminar Autonomous Systems (PSAS), pp. 13–24. Springer, Spain (2011)Google Scholar
  13. 13.
    Memariania, A., Aminib, A., Alinezhadc, A.: Sensitivity Analysis of Simple Additive Weighting Method (SAW): The Results of Change in the Weight of One Attribute on the Final Ranking of Alternatives. Journal of Industrial Engineering 4, 13–18 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Fahimeh Ramezani
    • 1
    • 2
    • 3
  • Jie Lu
    • 1
    • 2
    • 3
  1. 1.Decision Systems & e-Service Intelligence LabUniversity of Technology, SydneyUltimoAustralia
  2. 2.Centre for Quantum Computation & Intelligent SystemsUniversity of Technology, SydneyUltimoAustralia
  3. 3.School of Software, Faculty of Engineering and Information TechnologyUniversity of Technology, SydneyUltimoAustralia

Personalised recommendations