Computational methods through genetic algorithms for obtaining Stackelberg solutions to two-level integer programming problems

2003 ◽  
Vol 86 (6) ◽  
pp. 59-66 ◽  
Author(s):  
Ichiro Nishizaki ◽  
Masatoshi Sakawa ◽  
Tetsuya Kan
Keyword(s):  
Genetic Algorithms ◽  
Integer Programming ◽  
Computational Methods ◽  
Stackelberg Solutions ◽  
Integer Programming Problems

scholarly journals An Interactive Fuzzy Satisficing Method for Multiobjective Nonlinear Integer Programming Problems with Block-Angular Structures through Genetic Algorithms with Decomposition Procedures

2009 ◽  
Vol 2009 ◽  
pp. 1-17 ◽  
Author(s):  
Masatoshi Sakawa ◽  
Kosuke Kato
Keyword(s):  
Genetic Algorithms ◽  
Integer Programming ◽  
Decision Maker ◽  
Large Scale ◽  
Discrete Systems ◽  
Nonlinear Integer Programming ◽  
Numerical Examples ◽  
Fuzzy Goals ◽  
Degree Of Satisfaction ◽  
Integer Programming Problems

We focus on multiobjective nonlinear integer programming problems with block-angular structures which are often seen as a mathematical model of large-scale discrete systems optimization. By considering the vague nature of the decision maker's judgments, fuzzy goals of the decision maker are introduced, and the problem is interpreted as maximizing an overall degree of satisfaction with the multiple fuzzy goals. For deriving a satisficing solution for the decision maker, we develop an interactive fuzzy satisficing method. Realizing the block-angular structures that can be exploited in solving problems, we also propose genetic algorithms with decomposition procedures. Illustrative numerical examples are provided to demonstrate the feasibility and efficiency of the proposed method.

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