Optimizing the Design of Attribute Double Sampling Plans Attribute double sampling plan (ADSP) plays an important role in the construction quality inspection. The design of ADSP begins at setting proper acceptable quality level (AQL), rejectable quality level (RQL), producer’s risk (αrisk), and consumer’s risk (βrisk), then, a proper combination of acceptance parameters including (1) required sample size in the first sampling, (2) sample size in the second sampling, (3) first accepted number, (4) first rejected number, and (5) second accepted number can be selected to match AQL-αand RQL-β. However, the parameters must be all nonnegative integers and thus the system does not have 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 optimization mechanism for facilitating the ADSP design process. Minimizing the deviations of fitting AQL-α and RQL-β and the total sample sizes are considered in the optimization objective. Case studies show that the new mechanism can effectively locate the acceptance parameters and therefore facilitate the task of ADSP design.

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