A serial manufacturing system generally consists of multiple and different dedicated processing stages that are aligned sequentially to produce a specific end product. In such a system, the intermediate and end product quality generally varies due to setting of in-process variables at a specific stage and also due to interdependency between the stages. In addition, the output quality at each individual stage may be judged by multiple correlated end product characteristics (so-called ‘multiple responses’). Thus, achieving the optimal product quality, considering the setting conditions at multiple stages with multiple correlated responses at individual stage is a critical and difficult task for practitioners. The solution to such a problem necessitates building data driven empirical response function(s) at individual stage. These response function(s) may be nonlinear and multimodal in nature. Although extensive research works are reported for single-stage multiple response optimization (MRO) problems, there exist little evidence on work addressing multistage MRO problem with more than two sequential stages. This paper attempts to develop an efficient and simplified solution approach for a typical serial multistage MRO problem. The proposed approach integrates a modified desirability function and an ant colony-based metaheuristic search strategy to determine the best process setting conditions in serial multistage system. Usefulness of the approach is verified by using a real life case on serial multistage rolled aluminum sheet manufacturing process. 
A multistage and multiple response optimization approach for serial manufacturing system