Abstract This study develops an integrated vendor–buyer inventory model for a supply chain where quality issues and human error affect its coordination. Each lot shipped to the buyer contains defective items, with a rate which randomly changes from a lot to the other. The buyer inspects every shipped lot to segregate defective items. However, the inspection process at the buyer’s end goes wrong in the classification of defective and non-defective items. On the other hand, the buyer may run out of inventory, but in order to avoid lost sales, he/she offers a price discount on the backlogged items to his/her customers. Due to the vendor-buyer relationship, the buyer invests in reducing the setup cost of the vendor. Supply chain’s lead-time is considered variable, and two models are developed based on the probability distribution of the lead-time demand. In the first model, it is assumed that lead-time demand follows a normal distribution, while in the latter one, it does not follow any certain distribution. Two models are developed to determine the joint optimal decision variables that minimize the total cost of the supply chain. Two iterative algorithms are developed to obtain the optimal solution for both models. A set of numerical analysis and sensitivity analysis are conducted to gain insights.

中文翻译：

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人为错误对具有降低设置成本和延期交货价格折扣的集成随机供应链模型的影响

摘要 本研究为供应链开发了一个集成的供应商 - 买方库存模型，其中质量问题和人为错误会影响其协调。运送给买方的每一批次都包含有缺陷的物品，其比率从批次随机变化。买方检查每批装运的货物以隔离有缺陷的物品。但是，买方端的检验过程在缺陷和非缺陷项目的分类中出错。另一方面，买家可能会用完库存，但为了避免销售损失，他/她会向他/她的客户提供积压商品的价格折扣。由于供应商-买方关系，买方投资于降低供应商的设置成本。供应链的提前期被认为是可变的，并基于提前期需求的概率分布开发了两个模型。在第一个模型中，假设提前期需求服从正态分布，而在后一个模型中，它不服从任何特定分布。开发了两个模型来确定最小化供应链总成本的联合最优决策变量。开发了两种迭代算法以获得两种模型的最佳解决方案。进行一组数值分析和敏感性分析以获取见解。开发了两种迭代算法以获得两种模型的最佳解决方案。进行一组数值分析和敏感性分析以获取见解。开发了两种迭代算法以获得两种模型的最佳解决方案。进行一组数值分析和敏感性分析以获得见解。