Authors: Amirhossein Amiri; Hamed Mogouie; Mohammad H. Doroudyan
Addresses: Industrial Engineering Department, Faculty of Engineering, Shahed University, Tehran, P.O. Box 18151/159, Iran ' Industrial Engineering Department, Faculty of Engineering, Shahed University, Tehran, P.O. Box 18151/159, Iran ' Industrial Engineering Department, Faculty of Engineering, Shahed University, Tehran, P.O. Box 18151/159, Iran
Abstract: 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.
Keywords: MEWMA control charts; economic-statistical design; Lorenzen and Vance cost function; multi-objective approach; genetic algorithms; GAs; multivariate exponentially weighted moving average; SPC; statistical process control; process monitoring.
International Journal of Productivity and Quality Management, 2013 Vol.11 No.2, pp.131 - 149
Available online: 10 Feb 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article