Abstract As a widely used inventory management technique, multiple criteria ABC analysis is an effective way to classify inventory items into prioritized classes. Various methods have been proposed to solve the problem of multiple criteria ABC analysis. However, the information provided by experts or experienced managers is typically taken without any doubt in existing research. Little attention has been paid on the accuracy of the furnished sample classifications. To close the gap, this paper proposes a model to accommodate the possibility of misclassifications in the given information. The maximum likelihood method is used to estimate the parameters in the model. To avoid local optimum, grid search is implemented when the initial estimation is set. Odds are used to identify potential misclassifications in the given sample data. The proposed method is validated with both simulated and real-life data sets. The results show that the proposed method has a better performance in terms of classification accuracy and can learn the classification rules of experts from the training set and apply them to classify new items.

中文翻译：

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具有错误分类容忍度的多准则 ABC 分析的概率方法

摘要 作为一种广泛使用的库存管理技术，多准则ABC分析是将库存物品按优先级分类的有效方法。已经提出了各种方法来解决多准则ABC分析的问题。然而，专家或有经验的管理人员提供的信息通常在现有研究中毫无疑问地采用。很少有人关注提供的样本分类的准确性。为了缩小差距，本文提出了一个模型来适应给定信息中错误分类的可能性。最大似然法用于估计模型中的参数。为了避免局部最优，在设置初始估计时实施网格搜索。赔率用于识别给定样本数据中的潜在错误分类。所提出的方法通过模拟和现实生活数据集进行了验证。结果表明，所提出的方法在分类准确率方面具有更好的性能，可以从训练集中学习专家的分类规则，并将其应用于新项目的分类。