A multi-objective optimization method combining generalized data envelopment analysis and genetic algorithms

Describes a method using generalized data envelopment analysis (GDEA) and genetic algorithms (GAs) for generating efficient frontiers in multi-objective optimization problems. The purpose of GDEA is to measure the relative efficiency of decision making units and reflects the various preferences of decision makers. In addition, a GA is used for directly finding Pareto optimal solutions of multi-objective optimization problems. We suggest combining GDEA and GA to search for Pareto optimal solutions. It is shown that the proposed method overcomes shortcomings of existing methods and yields desirable efficient frontiers even in problems with non-convex constraints as well as convex constraints, through several numerical examples.