ABSTRACT
Generalized optimality-criteria (GOC) approaches are based on the mathematical satisfaction of a set of the necessary conditions, not necessarily all of the conditions, for an optimal solution. The Kuhn–Tucker conditions for an optimal solution are used in a customized procedure that will ensure that the prescribed set of optimality conditions is met [1–11]. Unlike mathematical programming that is typically based on defining a set of directions that will move each iteration to a ‘better’ solution using prescribed methods of choosing how far to travel in those directions, GOC uses the gradients of the objective and constraints, combined with Lagrange multipliers and a recursion relationship to iteratively reach the optimal solution through the satisfaction of the Kuhn–Tucker conditions for optimality.
