A hybrid method combining analytical and simulation models for performance evaluation of reconfigurable manufacturing systems

Today's dynamic manufacturing context, characterized by frequent product variations and consistently rising production volumes, forces companies to continuously adapt their systems with frequent reconfigurations. To support effective decision-making in this regard, it is necessary to have performance evaluation methods that can be modified conveniently to represent configuration alternatives while accounting for the intertwined dynamics of different production areas. The objective of this paper is to propose a modular architecture for performance evaluation of manufacturing systems, able to integrate models of different parts of the same system that are built independently from each other with different approaches, as analytical or simulation. The proposed method is based on the decomposition approach and evaluates the performance of manufacturing systems at the steady-state. The method has been validated through comparison with discrete event simulation considering different system layouts and parameters. Results demonstrate the accuracy of the method and the robustness of the underlying evaluation algorithm. The applicability of the method in industry has been proven in a case study involving the reconfiguration analysis of a manufacturing system producing electrical distribution equipment in scenarios with strongly increasing demand of products.