a full ranking method using integrated dea models and its application to modify ga for finding pareto optimal solution of mop problem

this paper uses integrated data envelopment analysis (dea) models to rank all extreme and non-extreme efficient decision making units (dmus) and then applies integrated dea ranking method as a criterion to modify genetic algorithm (ga) for finding pareto optimal solutions of a multi objective programming (mop) problem. the researchers have used ranking method as a shortcut way to modify ga to decrease the iterations of ga. the modified algorithm reduces the computational efforts to find pareto optimal solutions of mop problem and can be used to find pareto optimal solutions of mop with convex and non-convex efficient frontiers. an example is given to illustrate the modified algorithm.