• DocumentCode
    3404999
  • Title

    Integrated model for grey multi-attribute risk group decision-making

  • Author

    Dang, Luo ; Ling, Zhou

  • Author_Institution
    Coll. of Math. & Inf. Sci., North China Univ. of Conservancy & Hydroelectric Power, Zhengzhou, China
  • fYear
    2009
  • fDate
    10-12 Nov. 2009
  • Firstpage
    1013
  • Lastpage
    1018
  • Abstract
    The problem of grey multi-attribute risk group decision-making is studied, when the attributive values are interval grey numbers and the attribute weights are unknown. Using grey incidence method of the grey system theory, relative deviation degree based on the optimum alternative group and the worst alternative group, a new method is proposed. It establishes an integrated optimization model dealing the weights of index based on that the deviation of group members is minimum and the deviation between two groups is maximum. By using the model, the attribute weights can be gotten. Then, based on subjective preferences of decision-maker group, synthetically deviations of each alternative to the optimum alternative group and the worst alternative group are calculated to determine the ranking order of alternatives. Finally, an example is given to show the feasibility and availability of this method.
  • Keywords
    decision making; decision theory; grey systems; group theory; optimisation; grey incidence method; grey multi-attribute risk group decision making; grey system theory; integrated optimization model; optimum alternative group; worst alternative group; Assembly; Decision making; Educational institutions; Humans; Intelligent systems; Mathematics; Optimization methods; Random variables; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Grey Systems and Intelligent Services, 2009. GSIS 2009. IEEE International Conference on
  • Conference_Location
    Nanjing
  • Print_ISBN
    978-1-4244-4914-9
  • Electronic_ISBN
    978-1-4244-4916-3
  • Type

    conf

  • DOI
    10.1109/GSIS.2009.5408019
  • Filename
    5408019