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  • 學位論文

考量製造商最佳選別檢驗計畫與品質投資水準之研究

An optimal inspection and quality investment design for manufacturer under rectifying inspection plan

指導教授 : 黃允成

摘要


本文以製造商觀點探討商品在生產過程中因製程因素而發生變異,且品質特性呈偏態分布,文中以修正後的傳統品質損失函數衡量產品的品質並考量不良品可重工或報廢。本研究將建構總期望利潤之數學模型,探討在選別檢驗計畫及不同品質投資方案之水準下,利用電腦數值模擬分析,求解最佳品質投資方案及決定是否進行檢驗,若選擇進行檢驗,則尋找最適檢驗策略,以達製造商期望利潤最大化之目標。最後,期望研究之成果能供實務應用之參考。

並列摘要


In this paper, we based on the manufacturer's perspective to explore the variant products and skewed quality characteristic distribution due to unstable production process and assignable causes. The traditional quality loss function was revised to measure the quality cost of the product and the defective ones will be reworked or scrapped. This study will build a mathematical model for total expecting profit. By using the computerized numerical analysis method to find out the optimal quality investment program and decide whether the inspection plan will be executed. Finally, hoping the researching results can be referenced for practical applications and future studies.

參考文獻


Chen, C. H., (2011). The joint determination of optimum process mean and economic order quantity. Tamkang Journal of Science and Engineering, 14(4), 303-312.
Bartky, W., (1941). Multiple sampling with constant probability. The Annals of Mathematical Statistics, 14(4), 363-377.
Chan, W. M., Ibrahim, R. N., & Lochert, P. B., (2005). Evaluating the product quality level under multiple L-type quality characteristics. International Journal of Advanced Manufacturing Technology, 27, 90-95.
Chan, W. M., Ibrahim, R. N., & Lochert, P. B., (2005). Quality evaluation model using loss function for multiple S-type quality characteristics. International Journal of Advanced Manufacturing Technology, 26, 98-101.
Chang, Y. C., Liu, C. T., & Hung, W. L., (2008). Optimization of process parameters using weighted convex loss functions. European Journal of Operational Research, 196(2), 752-763.

被引用紀錄


謝雅惠(2016)。根據隨機過程理論建構複合式最適檢驗計畫之研究〔碩士論文,國立屏東科技大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0042-1805201714162524

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