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In this article we propose a new online control system aiming to lower the instants in which the production process migrates from being in- to out-of-control state, which generates an increase in the non-conformity rates. As shifts from in- to out-of-control are non-deterministic, a sample of size  n  is collected, for each  m  or  L  units produced, and each element from the sample is imprecisely classified as conform or non-conform (that is, there may be classification errors). If the amount of conform units from the sample is equal or greater than  a , the process would not be adjusted and another sample would be collected after  m  units produced. If the quantity of conform units is inferior to  a , the process would be adjusted and another sample would be collected after  L  units produced, given that  L  &gt;  m.  A genetic algorithm is proposed to approximately find the values of  a ,  n ,  m , and  L  that minimize all costs involved in the process being controlled. All procedures are illustrated through a detailed numerical example that attests the efficacy and efficiency of the proposed online control system.

An analysis of online quality control by attributes with an imperfect classification system and inspections with samples of size n