COMPUTATIONAL METHODS THROUGH GENETIC ALGORITHMS FOR OBTAINING STACKELBERG SOLUTIONS TO TWO-LEVEL MIXED ZERO-ONE PROGRAMMING PROBLEMS

In this paper, we develop computational methods for obtaining Stackelberg solutions to two-level mixed zero-one programming problems in which the decision maker at the upper level controls zero-one variables and the decision maker at the lower level controls real variables. To illustrate two-level mixed zero-one programming problems, we formulate a facility location and transportation problem as a two-level mixed zero-one programming problem. We develop computational methods through genetic algorithms for obtaining Stackelberg solutions. To demonstrate the feasibility and efficiency of the proposed methods, computational experiments are carried out and comparisons between the methods based on the branch-and-bound techniques and the proposed methods are provided.