A Kriging-based algorithm to optimize production systems approximated by analytical models

Production system optimization still remains a difficult problem even if fast analytical methods are used to estimate their mean performance measures. This paper addresses optimization problems in which the system performance measures are obtained from analytical methods implemented in computer codes that are usually time expensive. A global search algorithm is proposed to solve the addressed optimization problem. A Kriging metamodel is built to approximate the system performance function on the basis of the deterministic output values provided by the analytical model. Then a standard optimization method is applied on the explicit metamodel expression. The main advantages of the proposed method are its generality and ease of use. Indeed, the algorithm can be applied to optimize any production system assessable by an analytical method. Also, the Kriging technique allows contemporarily building the approximation of the unknown function and assessing its quality. Numerical results are satisfactory and prove the applicability of the method to real problems.