A Multi-Objectives Optimization Model for the Joint Design of Spc and Epc

Statistical process control is recognized as one of the most efficient techniques for increasing productivity of industrial processes. Two different approaches are used for process control, enhancement and improvement, which include statistical process control (SPC) and engineering process control (EPC). These methods were developed in isolation from each other and are widely utilized in various sectors. The literature reviews revealed that a joint design of SPC and EPC has not been addressed in a multi-objectives framework. Therefore, the purpose of the paper is to design SPC and EPC jointly using multi-objectives optimization. In this paper, statistical and economic criteria are utilized for the joint design of SPC and EPC. An efficient heuristic algorithm is developed to solve the proposed model and obtain the optimal solutions. To demonstrate the benefit of the integration of the two approaches, a numerical example is presented and discussed. The findings revealed that the integration of SPC and EPC leads to the earliest detection of assignable causes. The results also show that the Taguchi cost objective is proportional to the expected net income objective function. Moreover, sensitivity analysis is conducted to identify the impact of the model parameters. The sensitivity analysis indicated that as the value of sigma is increased the power of the chart reduces. In addition, it revealed that as the average time to find the assignable cause and penalty cost are increased, the expected net income objective function decreases