Manufacturing flow line systems: a review of models and analytical results
Queueing Systems: Theory and Applications - Special issue on queueing models of manufacturing systems
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Bayesian Algorithms for One-Dimensional GlobalOptimization
Journal of Global Optimization
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
A Taxonomy of Global Optimization Methods Based on Response Surfaces
Journal of Global Optimization
Properties and Analysis of Queueing Network Models with Finite Capacities
Performance Evaluation of Computer and Communication Systems, Joint Tutorial Papers of Performance '93 and Sigmetrics '93
A Trust Region Framework for Managing the Use of Approximation Models in Optimization
A Trust Region Framework for Managing the Use of Approximation Models in Optimization
Design and Modeling for Computer Experiments (Computer Science & Data Analysis)
Design and Modeling for Computer Experiments (Computer Science & Data Analysis)
Journal of Intelligent Manufacturing
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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.