貝氏方法解析考量檢驗誤差之戴明成本模式

A Bayesian Analysis on the Deming’s Model with Inspection Errors

蘇小惠(Hsiao-Huei Su)

指導教授 ： 徐旭昇 博士

元智大學/工程學院/工業工程與管理學系/碩士(1998年)

摘要

本文針對不可修復產品之組件發展一貝氏決策模式，以提供廠商作一適當之決策模式。此模式抽樣方式為單次抽樣，並考量購買成本 、檢驗成本 及不良品損失成本 。我們考慮在組件品質驗前分配為Beta分配及考慮檢驗誤差的假
設下，以貝氏分法來推導此模式之數學模式並以C語言撰寫此模式之程式，此程式之執行能力在數分鐘為限內，適用批量可至2000。
實驗結果發現，此抽樣模式之參數 的值較大時，傾向於0檢驗，且檢驗誤差越大時，抽樣數越小。 的值較小時，傾向於0檢驗，且檢驗誤差越大時，抽樣數越小。而隨著檢驗誤差越大其期望成本越高且檢驗通過標準 也就越嚴格。

關鍵字

貝氏決策理論 ； 單次抽樣 ； 檢驗誤差

並列摘要

The thesis develops a Bayesian decision model for making an optimum selection among suppliers who provide raw materials of non-repairable products. The model takes into account sampling information, inspection errors, inspection cost, material purchase cost, and product failure cost, with minimizing the total cost as the model objective. Two types of inspection errors are considered in our model. One is the error due to taking a good component for a bad one, and the other is the error due to taking a bad component for a good one.
The research first applies the theory of decision trees to construct the total objective functions of the model. Then all formulas relevant to the model are derived and a computer program written by C++ programming language is developed according to the formulas and is included in the appendix of the thesis. The computer program can execute the computation in several minutes on the model with a lot size of 2000 units. The research also performs experimental analysis on the model to show how the component quality, the inspection errors, product failure cost, and purchase cost have impact on the optimum total cost and the optimum sample size.

並列關鍵字

Bayesian Decision Theory ； Inspection Sampling Plans ； Decision Trees

參考文獻

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