Possibilistic programming decision making in modality perspective

The decision making faces a number of inherent uncertainties. The uncertainty of parameters of a model comes from vagueness and ambiguity included in the model structure and information. In this paper we present the decision model from the perspective of possibilistic programming to treat properly uncertainties in the decision making. The proposed concept plays a pivotal role in building fuzzy linear programming model, which is exposed with various types of uncertainties. The treatment of vagueness and ambiguity is given and a modality approach is used to solve the fuzzy linear program. An illustrative example explains the proposed model.

[1]  Li Yawei,et al.  Fuzzy Pattern Recognition Approach to Construction Contractor Selection , 2005 .

[2]  Hidetomo Ichihashi,et al.  Fuzzy Programming: A Survey of Recent Developments , 1990 .

[3]  Ichiro Nishizaki,et al.  Interactive multiobjective fuzzy random linear programming: Maximization of possibility and probability , 2008, Eur. J. Oper. Res..

[4]  H. Zimmermann Fuzzy programming and linear programming with several objective functions , 1978 .

[5]  Masahiro Inuiguchi,et al.  Possible and necessary efficiency in possibilistic multiobjective linear programming problems and possible efficiency test , 1996, Fuzzy Sets Syst..

[6]  L. Zadeh Probability measures of Fuzzy events , 1968 .

[7]  Abraham Charnes,et al.  Programming with linear fractional functionals , 1962 .

[8]  Ralph E. Steuer,et al.  Goal programming with linear fractional criteria , 1981 .

[9]  Jati K. Sengupta,et al.  Linear Programming under Uncertainty , 1981 .

[10]  Richard Bellman,et al.  Decision-making in fuzzy environment , 2012 .

[11]  Witold Pedrycz,et al.  Building Confidence-Interval-Based Fuzzy Random Regression Models , 2009, IEEE Transactions on Fuzzy Systems.

[12]  Matthias Ehrgott,et al.  Multiple criteria decision analysis: state of the art surveys , 2005 .

[13]  H. Zimmermann DESCRIPTION AND OPTIMIZATION OF FUZZY SYSTEMS , 1975 .

[14]  J. Watada,et al.  Building fuzzy random objective function for interval fuzzy goal programming , 2010, 2010 IEEE International Conference on Industrial Engineering and Engineering Management.

[15]  George J. Klir,et al.  Fuzzy sets, uncertainty and information , 1988 .

[16]  Masahiro Inuiguchi,et al.  Possibilistic linear programming: a brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem , 2000, Fuzzy Sets Syst..

[17]  Milan Zeleny,et al.  The pros and cons of goal programming , 1981, Comput. Oper. Res..

[18]  Nureize Arbaiy,et al.  Approximation of Goal Constraint Coefficients in Fuzzy Goal Programming , 2010, 2010 Second International Conference on Computer Engineering and Applications.

[19]  Hiroaki Ishii,et al.  Robust Expectation Optimization Model Using the Possibility Measure for the Fuzzy Random Programming Problem , 2009 .

[20]  L. Zadeh Fuzzy sets as a basis for a theory of possibility , 1999 .

[21]  Jati K. Sengupta,et al.  Stochastic goal programming with estimated parameters , 1979 .