A multi-objectives optimisation model for the joint design of statistical process control and engineering process control

Statistical process control and engineering process control are two methodologies used for process control and improvement. These technologies have existed independently of one another. Consequently, this research aims to simultaneously design statistical process control and engineering process control utilising multi-objectives optimisation. In this research, statistical and economic criteria are used to construct statistical process control and engineering process control jointly. To solve the developed model, an effective heuristic method is proposed. A numerical example is used to illustrate the significance of combining the two techniques. The results showed that the proposed solution could obtain the Pareto efficient solutions. This will help decision-makers to select the best solution based on their preferences. In addition, the findings indicated that the expected income values range between $172.0839 and $177.2175, and the Taguchi cost values vary between $4.469333 and $7.907547. On the other hand, the power values range between 0.91373 and 1. Moreover, the results revealed that as the Taguchi cost increases the expected income will increase and the power will decrease. Furthermore, sensitivity analysis is performed to determine the effect of variables in the model. The sensitivity analysis showed that the power of the chart decreases as the value of sigma is raised.