A Novel Mixed Attribute-Variable Sampling Plan Based on a Multiple Dependent State Repetitive Framework
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Abstract
This paper presents a novel mixed acceptance sampling plan, termed the attribute-variable sampling plan based on a multiple dependent state repetitive framework (AVS-MDSR), designed to enhance efficiency and accuracy in modern quality inspection. AVS-MDSR integrates attribute, variable, and multiple dependent state repetitive sampling, using the process capability index as the primary decision statistic for normally distributed quality characteristics. A key innovation is the inclusion of a repetitive sampling stage, allowing re-inspection when initial results are inconclusive, thereby reducing the risks of accepting nonconforming lots and rejecting conforming ones. To ensure optimal performance, a genetic algorithm determines the plan’s design parameters, minimizing the average sample number while controlling producer’s and consumer’s risks. Numerical studies, considering both symmetric and asymmetric nonconforming proportions, demonstrate the superiority of AVS-MDSR over existing mixed acceptance sampling plans, consistently reducing the required sample size. Its practical applicability is further validated with real industrial data. The findings confirm that AVS-MDSR provides an accurate and flexible solution for stringent quality control environments.
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