Process of Solving Multi-Response Optimization Problems Using a Novel Data Envelopment Analysis Variant-Taguchi Method

Multi-response optimization (MRO) is important to modern manufacturing that requires simultaneous consideration of multiple performance metrics to achieve high efficiency and quality. Therefore, this study presents an innovative hybrid method that combines the Data Envelopment Analysis Variant (DEAV) model with Taguchi to address the challenges associated with MRO. The DEAV model improves traditional DEA by incorporating multiple attribute decision-making (MADM) principles to offer a comprehensive evaluation framework. Taguchi uses efficiency scores from the DEAV model to enable optimal parameter determination through Taguchi optimization. Moreover, the hybrid method was validated using two examples, including optimization of a fish scale scraping machine and the CNC turning process of ST37 steel. The results showed that the DEAV-Taguchi method produced more efficient and reliable outcomes than existing methods, significantly improving key performance metrics such as surface roughness and material removal rate. The main contribution of this study was the development of a robust optimization framework that balanced multiple conflicting objectives in manufacturing processes. Future studies are expected to apply the method to different materials and processes, expand experimental designs, and assess the economic and environmental impacts of optimized parameters. The DEAV-Taguchi method also offers a significant advancement in MRO, providing a versatile and effective solution for modern manufacturing optimization.