Compensatory fuzzy multiple level decision making

Abstract Fuzzy set theory has been shown to be an effective tool to overcome the computational difficulties encountered in solving large multiple level programming problems (Shih et al., 1996). In this paper, compensatory operators are introduced for adjusting the decision making process between the different levels and also between the decision makers of the same level. After a brief consideration of the bi-level and three level systems, the large decentralized organizations with both equal and unequal goals are investigated. Various numerical examples are given to compare the influences of compensation and to illustrate the approaches.

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

[2]  Gautam Mitra,et al.  Mathematical Models for Decision Support , 1987, NATO ASI Series.

[3]  G. Anandalingam,et al.  A Mathematical Programming Model of Decentralized Multi-Level Systems , 1988 .

[4]  Harald Dyckhoff,et al.  Basic concepts for a theory of evaluation: Hierarchical aggregation via autodistributive connectives in fuzzy set theory , 1985 .

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

[6]  Ue-Pyng Wen,et al.  Finding an efficient solution to linear bilevel programming problem: An effective approach , 1996, J. Glob. Optim..

[7]  Jonathan F. Bard,et al.  Coordination of a multidivisional organization through two levels of management , 1983 .

[8]  Omar Ben-Ayed,et al.  Bilevel linear programming , 1993, Comput. Oper. Res..

[9]  R. Yager.  On a general class of fuzzy connectives , 1980 .

[10]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decision-making , 1988 .

[11]  T. L. Saaty.  A Scaling Method for Priorities in Hierarchical Structures , 1977 .

[12]  Ue-Pyng Wen,et al.  Linear Bi-level Programming Problems — A Review , 1991 .

[13]  Ue-Pyng Wen,et al.  The hybrid algorithm for solving the three-level linear programming problem , 1986, Comput. Oper. Res..

[14]  H. Zimmermann,et al.  Decisions and evaluations by hierarchical aggregation of information , 1983 .

[15]  H. Zimmermann,et al.  Latent connectives in human decision making , 1980 .

[16]  W. Pedrycz.  Fuzzy relational equations with generalized connectives and their applications , 1983 .

[17]  D. White,et al.  A solution method for the linear static Stackelberg problem using penalty functions , 1990 .

[18]  Linus E. Schrage.  LINDO : an optimization modeling system , 1991 .

[19]  R. Burton,et al.  The multilevel approach to organizational issues of the firm--A critical review , 1977 .

[20]  Hiroyuki Nagasawa,et al.  A method for generating nondominated solutions to a multiobjective-headquarters, three-level decentralized system , 1994 .

[21]  Panos M. Pardalos,et al.  Editorial: Hierarchical and bilevel programming , 1996, J. Glob. Optim..

[22]  Brigitte Werners,et al.  Aggregation Models in Mathematical Programming , 1988 .

[23]  G. Anandalingam,et al.  Multi-level programming and conflict resolution , 1991 .

[24]  Jonathan F. Bard,et al.  An Efficient Point Algorithm for a Linear Two-Stage Optimization Problem , 1983, Oper. Res..

[25]  Young-Jou Lai,et al.  Hierarchical optimization: A satisfactory solution , 1996, Fuzzy Sets Syst..

[26]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decisionmaking , 1988, IEEE Trans. Syst. Man Cybern..

[27]  Sunduck Suh,et al.  Toward developing a national transportation planning model: A bilevel programming approach for Korea , 1988 .

[28]  Terry L. Friesz,et al.  Hierarchical optimization: An introduction , 1992, Ann. Oper. Res..

[29]  E. Stanley Lee,et al.  Fuzzy approach for multi-level programming problems , 1996, Comput. Oper. Res..