Multiple Objective Linear Programming with Fuzzy Coefficients

In this paper, we treat multiple objective programming problems with fuzzy coefficients. We introduce the approaches based on possibility and necessity measures. Our aim in this paper is to describe the treatments of the problem rather than the solution method for the problem. We describe the modality constrained programming approach, the modality goal programming approach and modal efficiency approach. In the first approach, we discuss treatments of fuzziness in the programming problems. The extensions of a fuzzy relation to the relation between fuzzy numbers are developed in order to treat generalized constraints. In the second approach, we show that two kinds of differences between a fuzzy objective function value and a fuzzy target are conceivable under the fuzziness. We describe the distinction of their applications in programming problems. In the third approach, we describe how the efficiency can be extended to multiple objective programming problems with fuzzy coefficients. Necessary and sufficient conditions for a feasible solution to satisfy the extended efficiency are discussed. Finally some concluding remarks are given.

[1]  Hideo Tanaka,et al.  On Fuzzy-Mathematical Programming , 1973 .

[2]  Didier Dubois,et al.  Possibility Theory - An Approach to Computerized Processing of Uncertainty , 1988 .

[3]  M. Inuiguchi,et al.  Goal programming problems with interval coefficients and target intervals , 1991 .

[4]  Hidetomo Ichihashi,et al.  Relationships between modality constrained programming problems and various fuzzy mathematical programming problems , 1992 .

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

[6]  M. K. Luhandjula Multiple objective programming problems with possibilistic coefficients , 1987 .

[7]  Philippe Fortemps,et al.  Multi-Objective Fuzzy Linear Programming: The MOFAC Method , 2000 .

[8]  R. S. Laundy,et al.  Multiple Criteria Optimisation: Theory, Computation and Application , 1989 .

[9]  J. Ramík,et al.  Inequality relation between fuzzy numbers and its use in fuzzy optimization , 1985 .

[10]  Constantin Virgil Negoita,et al.  The current interest in fuzzy optimization , 1981 .

[11]  R. Słowiński,et al.  Stochastic Versus Fuzzy Approaches to Multiobjective Mathematical Programming under Uncertainty , 1990, Theory and Decision Library.

[12]  Masahiro Inuiguchi,et al.  Solution Concepts for Fuzzy Multiple Objective Programming Problems , 1990 .

[13]  Hiroaki Kuwano,et al.  On the fuzzy multi-objective linear programming problem: Goal programming approach , 1996, Fuzzy Sets Syst..

[14]  I. M. Stancu-Minasian,et al.  Stochastic Programming: with Multiple Objective Functions , 1985 .

[15]  M. Zeleny Linear Multiobjective Programming , 1974 .

[16]  Hidetomo Ichihashi,et al.  Some properties of extended fuzzy preference relations using modalities , 1992, Inf. Sci..

[17]  Hidetomo Ichihashi,et al.  Modality constrained programming problems: A unified approach to fuzzy mathematical programming problems in the setting of possibility theory , 1993, Inf. Sci..

[18]  J. Fodor,et al.  Preferences and Decisions under Incomplete Knowledge , 2000 .

[19]  Marc Roubens,et al.  Ranking and defuzzification methods based on area compensation , 1996, Fuzzy Sets Syst..

[20]  Didier Dubois,et al.  Possibility theory in constraint satisfaction problems: Handling priority, preference and uncertainty , 1996, Applied Intelligence.

[21]  M. Vila,et al.  A general model for fuzzy linear programming , 1989 .

[22]  R. Słowiński Fuzzy sets in decision analysis, operations research and statistics , 1999 .

[23]  Masahiro Inuiguchi,et al.  Modality Constrained Programming Problems Introduced Gödel Implication and Various Fuzzy Mathematical Programming Problems , 1991 .

[24]  Masatoshi Sakawa,et al.  Fuzzy Sets and Interactive Multiobjective Optimization , 1993 .

[25]  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..

[26]  D. Dubois,et al.  The mean value of a fuzzy number , 1987 .

[27]  Amelia Bilbao-Terol,et al.  Solution of a possibilistic multiobjective linear programming problem , 1999, Eur. J. Oper. Res..

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

[29]  H. Prade,et al.  Inverse Operations for Fuzzy Numbers , 1983 .

[30]  Heinrich Rommelfanger,et al.  Fuzzy linear programming with single or multiple objective funtions , 1999 .

[31]  Ching-Lai Hwang,et al.  Fuzzy Mathematical Programming , 1992 .

[32]  D. Dubois,et al.  FUZZY NUMBERS: AN OVERVIEW , 1993 .

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

[34]  E. Sanchez Solution of fuzzy equations with extended operations , 1984 .

[35]  Madan M. Gupta,et al.  Fuzzy mathematical models in engineering and management science , 1988 .

[36]  James P. Ignizio,et al.  Linear Programming in Single- and Multiple-Objective Systems , 1984 .

[37]  Didier Dubois,et al.  A review of fuzzy set aggregation connectives , 1985, Inf. Sci..

[38]  G. Bitran Linear Multiple Objective Problems with Interval Coefficients , 1980 .

[39]  Mashaallah Mashinchi,et al.  Linear programming with fuzzy variables , 2000, Fuzzy Sets Syst..

[40]  M. K. Luhandjula Fuzzy optimization: an appraisal , 1989 .

[41]  Didier Dubois,et al.  Computing improved optimal solutions to max-min flexible constraint satisfaction problems , 1999, Eur. J. Oper. Res..

[42]  Hans-Jürgen Zimmermann,et al.  Applications of fuzzy set theory to mathematical programming , 1985, Inf. Sci..