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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/76338

    Title: 含有不良品之經濟訂購量模式的最適訂購量、運送次數及不完美檢驗程序的檢定力
    Other Titles: The Optimal Order Quantity, Delivery Schedule, and the Power of Test for an Imperfect Inspection Process in an Eoq Model with Defective Products
    Authors: 歐陽良裕
    Contributors: 淡江大學經營決策學系
    Keywords: 存貨;不良品;型Ι錯誤;型Ⅱ錯誤;檢定力
    Date: 2011-08
    Issue Date: 2012-05-07 14:51:20 (UTC+8)
    Abstract: 現今企業之間的競爭以及消費者維護自身權益意識的抬頭,使得產品品質檢驗能 力的提升更被業界所重視。若企業未能將不良品檢驗出來,而將之販售給消費者,不 僅會因退貨而產生退貨成本,更進而減少了銷售所帶來的利潤,甚至導致商譽受損, 降低企業未來的競爭力。本研究考慮經濟訂購量模式,在進貨批量中含有不良品 的情形下,探討零售商的訂購存貨問題。研究中假設產品品質檢驗的結果會發 生型Ⅰ和型Ⅱ錯誤並將檢驗出不良品的能力視為決策變數之一,為提高檢定力 (power of test),零售商透過投資資金來改善產品檢驗的品質,減少不良品 流入市場的機率。本研究以零售商單位時間存貨相關總成本最小化為目標,利 用數學分析方法找出其最適的訂購數量、運送次數和檢定力。接著,設計演算 法,以利求得最適解。最後,舉數值範例,說明求解過程並對主要參數進行敏 感性分析。所得結果可提供給擬定訂購策略的管理者重要之參考依據,進而提 高企業的利潤與競爭力。
    Due to competition between businesses and the increased awareness of consumer rights protection, businesses have placed increasing attention on the enhancement of product quality inspection capability. If businesses are unable to determine defective products and, consequently, sell them to consumers, it will result in higher return costs and also decrease sales revenue, damage business reputation and lower the businesses’ competitiveness. This study applies the Economic Order Quantity Model and explores retailers’ order and inventory problems in an environment where defective products are part of a bulk purchase. This study assumes that type Ⅰ and Ⅱ errors occur in the product quality inspection process and the ability to filter out defective products is regarded as a decision variable. In order to enhance inspection capability, retailers invest money in improving the product inspection quality and in lowering the probability of defective products sold into the market. In this study, the retailer’s inventory-related total cost minimization per time unit is set as the objective, and mathematical analyses is utilized to seek the optimum order quantity, shipping times, and the power of the test. The algorithm is designed to resolve the optimum solution. Finally, numerical values are used to demonstrate the resolution process and major parameters are applied for conducting sensitivity analysis. The results derived can provide useful references for managers responsible for the order strategy planning, as well as to enhance business profits and competitiveness.
    Appears in Collections:[管理科學學系暨研究所] 研究報告

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