工業界在進料或產品量產出貨時，由於受到時間與成本的限制，必須藉由抽樣檢驗隨機選取樣本進行檢驗測試，然後根據檢測結果作出該批量是否達到允收或拒收之判斷，特別是破壞性檢測常須付出一些額外的成本包含決策錯誤造成的損失及樣本檢測所花費的人工及物料成本。
適合破壞性檢測的Wald’s逐次抽樣檢驗計畫，其抽樣數為一隨機變數且抽樣計畫停止法則的選定常依經驗法則而定，因此實務工作者常無法得知停止抽樣之個數是否最經濟。為了有效降低逐次抽樣檢驗計畫平均抽樣數，本研究先設定抽樣數的上限，接著在此抽樣數上限之下決定允收或拒收之最佳停止法則。我們利用經濟成本之觀點，建構總成本函數，作為衡量逐次抽樣檢驗計畫的總損失成本。
研究結果顯示在合理的型Ι誤差及型Ⅱ誤差下，本研究所提出的抽樣計畫停止法則可有效降低平均抽樣數使抽樣總成本達到最低。

During the in-coming and/or outgoing inspection of the industrial products, the decision of accepting or rejecting a lot is made according to the inspection/testing results for the key characteristics of sample units. Additional costs including labor and material costs as well as the loss of mis-judgement usually occur when applying Wald's sequential sampling plan to the destructive testing.

Normally, previous stopping rules for Wald's sequential sampling plan are empirically determined based on rules of thumb. Practical and unable to decide whether the sample number for terminating inspection/testing is economical or not .

In order to effectively reduce the average sample number of sequential sampling plan, the upper limit of the sample number is specified first, then the optimal stopping rules is determined based on this specified sample number . Finally, a total cost function is established to assess the total loss of the proposed sequential sampling plan .

The results show that the optimal stopping rule for our proposed sequential sampling plan can effectively reduce the average sample number and thus achieve a minimum total loss under the reasonable type Ι and Ⅱ errors.

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