Economic design of control charts using the Taguchi loss function
Computers and Industrial Engineering - Collection of papers on Computer-Integrated Manufacturing
Expert Systems with Applications: An International Journal
Development of fuzzy process control charts and fuzzy unnatural pattern analyses
Computational Statistics & Data Analysis
Economic design of variable sampling intervals T2 control charts using genetic algorithms
Expert Systems with Applications: An International Journal
Generating robust and flexible job shop schedules using genetic algorithms
IEEE Transactions on Evolutionary Computation
Economic design of EWMA control charts based on loss function
Mathematical and Computer Modelling: An International Journal
Economic design of integrated model of control chart and maintenance management
Mathematical and Computer Modelling: An International Journal
An integrated model based on statistical process control and maintenance
Computers and Industrial Engineering
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This paper studies integrated systems approach to Statistical Process Control (SPC) and Maintenance Management (MM). Previously, only four policies which are in control alert signal, out of control alert signal, in control no signal, and out of control no signal, were used in the consideration (Zhou & Zhu, 2008). The objectives of this research are to develop an integrated model between Statistical Process Control and Planned Maintenance of the EWMA control chart. To do this, warning limit is considered to increase the policy from four to six such as warning limit alert signal and warning limit no signal. A mathematical model is given to analyze the cost of the integrated model before the genetic algorithm approach is used to find the optimal values of six variables (n,h,w,k,@h,r) that minimize the hourly cost. A comparison between four-policy and six-policy models shows that the six policy model contains the hourly cost higher than that of the four policy model, it is because the addition of the warning limit in the model leads into increased ability of defective product detection. This consequently results to the increase of repairing and maintenance of machines; therefore the hourly cost is higher. Finally, multiple regressions are employed to demonstrate the effect of cost parameters.