本論文的研究範疇屬於一維剩餘物料裁切問題(RCSP)，並著重於裁切工廠在連續生產週期時廢料與相關裁切成本的最小化。其中，訂單需求具有時間週期性，不同年度但同季節的需求分佈會有相似的特性，因此本研究以去年的訂單需求來預測今年的訂單需求。本研究深入探討裁切過程中如何調整不標準原料的長度及數量，能有助於未來裁切時廢料的減少，並促使整個連續裁切過程產生的廢料及裁切成本為最小。在每次安排裁切時，儘量使不標準原料的長度與去年需求長度相同，未來在進行裁切時這些不標準原料便不會有廢料產生，進而減少裁切的總成本。實作方面，則是以基因演算法來求解本研究所設定的問題。

The main idea of this thesis is related to Residual Cutting Stock Problem (RCSP), and we focus on minimizing trim loss and cutting costs in factory subsequent process. Since the demands for orders are periodical, that is, the distributions of orders are similar in seasons among different years, our study explores how to adjust the length and amount of residual stock which is conducive to trim loss and minimize cost in the entire continuous cutting process by using last year’s orders to predict subsequent year’s orders. When arranging cutting plans, we assume that the length and orders of residual stocks are as much as possible the same and these residual stocks will never produce trim loss in subsequent cutting process, thus reducing the whole cutting costs. In problem solving, we use genetic algorithm to solve the problem proposed by this thesis.

中文
[1]徐家賢，2010，《具時間週期性的連續隨機需求之一維剩餘物料裁切問題之研究》，長榮大學資訊管理研究所碩士論文。
[2]林豐澤，2005，《演化式計算上篇：演化式演算法的三種理論模式》，智慧科技與應用統計學報（3卷1期）：1-28。
[3]林豐澤，2005，《演化式計算下篇：基因演算法以及三種應用實例》，智慧科技與應用統計學報（3卷1期）：29-56。
[4] 賴忠德，2010，《最佳化型鋼構件裁切之研究》，中央大學土木工程學研究所碩士論文。
[5]張瀚文，2005，《鋼筋裁切順序最佳化模式》，成功大學土木工程學研究所碩士論文。
[6]沈宇晟，2005，《鋼筋裁切問題之啟發式解法》，成功大學土木工程學研究所碩士論文。
[7]蘇明健，2009，《應用啟發式演算法求解多目標型鋼切割計劃之研究》，中山大學資訊管理研究所在職專班碩士論文。
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