Multiattribute Bayesian Acceptance Sampling Plans Under Nondestructive Inspection
提出一种确定多属性贝叶斯验收抽样模型最优方案的方法，考虑属性间的交互作用，发现可筛选属性可独立求解，而可报废属性交互会减少样本量或降低接受概率，并开发了高效迭代算法。
A methodology for determining optimal sampling plans for Bayesian multiattribute acceptance sampling models is developed. Inspections are assumed to be nondestructive and attributes are classified as scrappable or screenable according to the corrective action required when a lot is rejected on a given attribute. The effects of interactions among attributes on the resulting optimal sampling plan are examined and show that: (1) sampling plans for screenable attributes can be obtained by solving a set of independent single attribute models, (2) interactions of scrappable attributes on screenable attributes and conversely result in smaller sample sizes for screenable attributes than in single attribute plans, and (3) interactions among scrappable attributes result in either smaller sample sizes, lower acceptance probabilities or both, relative to single attribute plans. An iterative subproblem algorithm is developed, which is effective in finding near optimal multiattribute sampling plans having a large number of attributes.