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A new acceptance sampling plan using Bayesian approach in the presence of inspection errors

机译：在存在检查错误的情况下使用贝叶斯方法的新验收抽样计划

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In acceptance sampling plans, the decisions on either accepting, rejecting the lot or inspecting all items in the lot is still a challenging problem. We developed a new acceptance sampling plan to decide about the received lot based on cost objective function in the presence of inspection errors. It is assumed that inspection is not perfect and type I and type II errors occur in the inspection process. The problem of acceptance sampling plan in the presence of inspection errors is modeled using decision tree approach, where the sample size and decision of accepting, rejecting or inspecting are decision nodes. Bayesian inference is used to update the probability distribution function of nonconforming proportion. Then the cost at terminal nodes is analysed and optimal decisions are determined using a backward recursive approach. A case study is solved for illustrating the application of the model and sensitivity analysis is carried out on the parameters of the proposed methodology and the behavior of model by changing the parameters is investigated. We also compared the proposed model with single sampling model. At the end, the model is generalized in order to consider different potential decisions that can be made in practice.
机译：在验收抽样计划中，关于接受，拒绝批次或检查批次中所有项目的决定仍然是一个具有挑战性的问题。我们制定了一个新的验收抽样计划，在存在检查错误的情况下，基于成本目标函数决定接收批次。假定检查不完美，并且在检查过程中发生I型和II型错误。采用决策树方法对存在检查错误的验收抽样计划问题进行建模，其中样本量和接受，拒绝或检查的决策是决策节点。贝叶斯推理用于更新不合格比例的概率分布函数。然后分析终端节点的成本，并使用后向递归方法确定最佳决策。通过案例研究来说明该模型的应用，并对所提出方法的参数进行敏感性分析，并研究了通过更改参数来改变模型的行为。我们还将提议的模型与单一抽样模型进行了比较。最后，对模型进行了概括，以考虑可以在实践中做出的不同潜在决策。

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