A meta-heuristic satisficing tradeoff method for solving multiple criteria combinatorial optimization problem

In this paper a meta-heuristic satisficing tradeoff method for solving multiple criteria combinatorial optimization problem is proposed. Firstly, Pareto optimal solutions are generated by using a genetic algorithm with family elitist concept. Then, we try to find a preferred solution of the decision maker based on the satisficing tradeoff method. In this meta-heuristic satisficing tradeoff method we do not need to solve a complex min–max problem in each iteration, but we try to find a min–max solution in the Pareto optimal solutions, and starting from this solution we try to find a better solution locally by using simulated annealing method. As an example of multiple criteria optimization problem a numerical example of a flowshop scheduling problem is included to verify the effectiveness of the method proposed in this paper.