Computer-aided design of &Xmacr; and R charts using teaching-learning-based optimisation algorithm

Control charts are broadly applied in industries to establish and maintain statistical control of a process which leads to improve the quality and productivity. Design of control charts requires selection of three parameters namely sample size, sampling interval and width of control limits for the chart. The prime aim of the economic design of a control chart is to find the optimal values for control chart parameters such that the total cost of process control would be minimum. This article uses a new population based teaching‐learning‐based optimisation search algorithm for the joint economic design of &Xmacr; and R control charts that find global minimum for the loss cost function by optimising the design parameters. A MATLAB program has been developed for the computer‐aided design of control charts. The algorithm has been tested for as many as 160 sets of numerical data and the results were observed to be superior to that reported in two different papers with respect to cost savings.