Based on queuing theory,a nonlinear optimization model is proposed in this paper,which has the service load as its objective function and includes three inequality constraints of Work In Progress(WIP).A novel transformation of optimization variables is also devised and the constraints are properly combined so as to make this model into a convex one from which the Lagrangian function and the Karurh Kuhn Tucker(KKT) conditions are derived.The interior-point method for convex optimization is presented here as a computationally efficient tool.Finally,this model is evaluated on a real example,from which such conclusions are reached that the optimum result can ensure the full utilization of machines and the least amount of WIP in manufacturing systems;the interior-point method needs fewer iterations with significant computational savings and it is possible to make nonlinear and complicated optimization problems convexified so as to obtain the optimum. Based on queuing theory, a nonlinear optimization model is proposed in this paper, which has the service load as its objective function and includes three inequality constraints of Work In Progress (WIP). A novel transformation of optimization variables is also devised and the constraints are properly combined so as to make this model into a convex one from which the Lagrangian function and the Karurh Kuhn Tucker (KKT) conditions are derived. interior-point method for convex optimization is presented here as a computationally efficient tool. is evaluated on a real example, from which such conclusions are reached that the optimum result can ensure the full utilization of machines and the least amount of WIP in manufacturing systems; the interior-point method needs fewer iterations with significant computational savings and it is possible to make nonlinear and complicated optimization problems so as to obtain the optimum.