長久以來，營造產業被視為台灣的主要發展工業。近年來，營造市場景氣不佳，多數的營造公司因成本控制沒有做好妥善的規劃，造成利潤不斷下滑。因此較為積極的公司，早將成本控制視為重要的一環。而隨著科技日新月異，在工程領域裡，期望藉由最佳化演算法技術的提升，使得成本管控更佳完善。
本研究規劃的目標為降低鋼筋裁切之總成本，亦即增加已使用原料鋼筋的剩料退回價值、與未使用的原料鋼筋退回價值，並減少裁切次數所花費的成本。
以往模式在求解過程中，常會遇到違背限制式的裁切計畫，而浪費計算資源。本研究首先提出改良式基因演算法以減少限制式之數目。為妥善處理不可行解，改良式基因演算法將鋼筋裁切之染色體的編碼方式進行改變，並提出二套鋼筋裁切問題的求解模式，第一套模式則以前測式迴圈持續搜尋可行解;第二套模式中以懲罰值策略處理不可行解。兩套模式皆採用競爭式選擇理論。為驗證求解品質與效率，提出之演算法針對實際案例進行測試。結果證明提出之求解模式確實能夠正確且更有效率的求解出品質良好的裁切計畫。且在有限的時間內，求得最佳的解，節省人力資源，提升工程品質，滿足施工作業需求。

For many years, the construction industry has been regarded as one of the main developed industries in Taiwan. Since the construction market has been under recession in recent years, many construction companies face a decrease in profit due to not well-planned cost control. Therefore, contractors have seen the cost control as an important task. As the technology changes day by day, it is desired that the cost control may be improved using advanced optimization techniques.

The purpose of this research is to minimize the total cost of cutting steel bar, in other words, to increase the return value of used and unused raw steel bar, and reduce the cost by cutting frequency reduction.

For existing of the past algorithms , a cutting plan that violates the constraints was often encountered, and this resulted in waste of calculation resources. An improved Genetic Algorithm was firstly presented in this research so as to minimize the number of constraints. In order to handle infeasible solutions properly, the coded system of chromosome of steel bar cutting will be changed, and two models of solutions of cutting steel bar will also be brought up in this research. One model is to continuously search until the feasible solutions are found, and the other one is to apply the penalty strategy to handle the non-feasible solutions. Both models adopted the theory of tournament selection. As to verify the quality and efficiency of the solutions, the proposed models were tested in practical cases, and the results indicated that they are able to produce good cutting plans correctly and efficiently. Moreover, the models are also able to meet the requirements of construction operations because they generate the best solutions within the time constraints, and then retrench the human resource and upgrade the construction quality.

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