In today’s Industry 4.0–oriented manufacturing environment, practitioners require decision-support tools that can rapidly identify robust operating conditions capable of responding to demand variability. However, existing studies on FMS largely focus on theoretical analyses and lack application-oriented frameworks that systematically integrate multi-response simulation optimization with alternative MCDM methods under real operational constraints. Addressing this gap, this study proposes an application-oriented methodology for determining effective operating conditions in real FMS environments. The methodology is demonstrated through a real-world case study and integrates simulation optimization based on the Taguchi method with MCDM techniques, including the TOPSIS, GRA, and VIKOR. These MCDM methods are employed to combine multiple performance measures – cycle time, throughput rate, waiting time in queue, and investment cost – into a single composite performance measure, while explicitly considering both quantitative and qualitative decision variables. The computational results from the case study demonstrate the effectiveness of the proposed framework, with the redesigned FMS configurations achieving a minimum production rate of 798 units per month, corresponding to approximately a 2.5-fold improvement over the current system performance. Rather than introducing a theoretical model, the proposed methodology emphasizes practical applicability and DSS for managers in selecting operating conditions aligned with their operational priorities.
Dengiz et al. (Wed,) studied this question.