營建工程採用各式各樣不同的營建材料，因此如何訂定完善的統計抽樣驗收計畫，對於營建工程品質之確保影響甚鉅。而統計抽樣驗收中，尤以計數型抽樣驗收方法應用最為方便且廣泛，然而如何考量產品允收機率的條件下制定出完善的計數型抽樣驗收計畫，將有助於驗收計畫施行之成效，以確保營建工程品質。
本文採用群智慧演算法之蟑螂演算法，最佳化計數型抽樣驗收計畫之允收參數，包括抽樣樣本數、允收不良品個數與拒收不良品個數，於原先設定之生產者冒險率與消費者冒險率下，同時達成抽樣樣本數最少及冒險率偏差最小之目標，分別求得單次、雙次與三次抽樣驗收計畫供管理者決策之用，以因應不同需求，制定最適宜且經濟之抽樣驗收計畫。

Construction Industry uses various kinds of materials. To set suitable statistical sampling acceptance plans greatly impacts construction qualities. In statistical sampling acceptance plans, methods of attribute acceptance sampling plans are widely applicable. However, to identify suitable attribute acceptance sampling plans under settings of acceptance probabilities is the key to ensure construction qualities.
This research used a swarm intelligent algorithm, namely Roach Infestation Optimization (RIO), to calculate parameters of the attribute acceptance sampling plans including the numbers of samples, acceptances, and rejections under predefined risks of producers and customers. The resultant plans simultaneously aimed at two objectives, i.e. minimizing the total number of samples and the discrepancy among predefined risks and calculated risks. This research provided optimal results of single, double, and triple sampling acceptance plans. Various resultant plans were provided for project managers to face different needs and gave higher profits.

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