Multi-objective economic-statistical design of MEWMA control chart

The various advantages of MEWMA control chart such as the ability to detect small shifts in the process with multiple quality characteristics have motivated users to apply this chart for process monitoring. Considering the high costs of implementing MEWMA control chart, the economic-statistical design of this chart has been increasingly investigated. In most of the previous studies the cost function has been considered as the objective function while the statistical properties have been modelled as constraints in a mathematical programming. According to the dependency of the cost function on statistical properties in the constraints, the results of these methods are not efficient enough. In this paper, two multi-objective approaches, an aggregative and a non-aggregative approach are applied and optimised using a genetic algorithm. The proposed approaches are evaluated through a numerical example from the literature and the efficiency of the multi-objective approaches are verified in comparison with the previous methods.