A GA mechanism for optimizing the design of attribute double sampling plan

An attribute double sampling plan (ADSP) can be performed when the acceptance parameters are known. These include first sample size, second sample size, first acceptance number, first rejectable number, and second acceptance number. The acceptance parameters must match the predefined probability 1-α of accepting a lot if the lot proportion defective is at the acceptable quality level (AQL) and β of accepting a lot if the lot proportion defective is at the rejectable quality level (RQL). In addition, the parameters must be all nonnegative integers and thus the system can not be solved as a closed-form solution. As a result, the trial-and-error method is usually used to seek the solutions. This paper presents a genetic algorithms-based mechanism for facilitating the ADSP design process. Objectives of minimizing both the deviations of fitting AQL-α and RQL-β and the total sample sizes are traded off in the optimization process. Case studies show that the new mechanism can effectively locate the acceptance parameters and therefore facilitate the task of ADSP design. In addition, a computer program is developed for facilitating the task of performing the design of an ADSP.