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Optimizing a Taguchi's Loss Function Based Economical Single Sampling Plan with Unknown Incoming Quality

机译：基于未知质量的田口损失函数经济单次抽样方案的优化

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This paper takes over the common dilemma facing a consumer receiving a lot from a supplier with unavailable information about the supplier's process level, or the information being available but untrustworthy or uncertain. This paper aims to model and optimize an economical single sampling plan that is independent of the supplier's process level, where the loss caused by accepting low quality lots is treated as a Taguchi's loss function; the model also considers inspection cost, and replacement cost. The Taguchi's loss function in this paper is a function of the expected percent defect in the accepted lots, which later through standardizing the Operating Characteristic (OC) curve becomes a function of the sample size n, and the defectives rejection limit c achieving independence from the supplier's process level. The standardization is attained through mathematical estimation and use of the beta function properties; the reliability associated with using the expectation is assessed later through the variance. The optimization technique used to find the value of n and c that minimizes the total cost associated with this sampling plan is direct search since both variables are discrete and bounded by the lot size.
机译：本文解决了消费者从供应商那里收到大量货品而面临的共同难题，这些供应商缺乏有关供应商过程级别的信息，或者该信息可用但不可信或不确定。本文旨在建模和优化与供应商的流程水平无关的经济的单次抽样计划，其中将接受劣质批次造成的损失视为田口的损失函数；该模型还考虑了检查成本和更换成本。本文中的Taguchi损失函数是所接受批次中预期缺陷百分比的函数，随后通过标准化操作特性（OC）曲线成为样本量n的函数，缺陷排除极限c可以独立于供应商的流程水平。通过数学估计和使用beta函数属性来实现标准化；稍后通过差异评估与使用期望相关的可靠性。由于两个变量都是离散的并且受批量限制，因此用于查找使该采样计划的总成本最小的n和c值的优化技术是直接搜索。

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