Computational methods through genetic algorithms for obtaining Stackelbergsolutions to two-level mixed zero-one programming problems

In this paper, we develop computational methods for obtaining Stackelberg s olutions to two-level mixed zero-one programming problems in which the deci sion maker at the upper level controls zero-one variables and the decision maker at the lower level controls real variables. To illustrate two-level m ixed zero-one programming problems, we formulate a facility location and tr ansportation problem as a two-level mixed zero-one programming problem. We develop computational methods through genetic algorithms for obtaining Stac kelberg solutions. To demonstrate the feasibility and efficiency of the pro posed methods, computational experiments are carried out and comparisons be tween the methods based on the branch-and-bound techniques and the proposed methods are provided.