A computational method using genetic algorithms for obtaining Stackelberg solutions to two‐level linear programming problems

Stackelberg solutions have been derived for two-level linear programming problems using genetic algorithms which in recent years have shown their efficacy in optimization problems having discrete variables. Two-level linear programming problems are converted into single-level programming problems by including the optimal conditions of lower-level problems in the conditions of higher-level problems. The obtained one-level programming problems become 0–1 mixed programming problems. A computational method for obtaining Stackelberg solutions by generating initial population and using corresponding genetic operators based on the characteristics of the problems by expressing the 0–1 variables as individuals of the genetic algorithms is proposed. The efficacy of the proposed method is shown in computational experiments by comparing it with the variable elimination method. © 2002 Scripta Technica, Electron Comm Jpn Pt 3, 85(6): 55–62, 2002

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