在當前競爭強烈的市場中，紡織業者需要在兼顧提供多樣化產品及快速生產的同時，盡可能的降低成本的支出；在多項支出成本之中，原物料的成本佔總生產花費的大宗，若可以有效地降低布料的浪費，將對於降低生產成本有顯著的幫助。為求布料使用效率最大化，訂單分碼規劃(COP)被視為紡織業製程之中關鍵的一環，此流程將依照客戶訂單以及馬克排版結果進行排版，若能夠將不同尺寸大小的模板進行妥善的安排，並且在最短的時間內獲得最佳解，將對紡織業有極大的益處。
由於訂單分碼規劃在求解上屬於NP-hard問題，當問題規模增加時，求解難度也會隨之急遽的增加，故幾乎難以透過窮舉法來尋找最佳解。本文提供一個新的一對一演算法與生物組織搜尋演算法(SOS)、基因遺傳演算法(GA)、混和演算法、切割搜尋演算法進行求解及比較。根據測試結果顯示，本研究所使用之一對一演算法能夠顯著的改善求解效率，並且針對極端訂單需求也能夠有良好的求解結果。

With the current competitive and aggressive market, the apparel industry needs to consider the provision of diversified products and rapid production, while reducing costs as much as possible. Among the multiple expenditure costs, the cost of raw materials accounts for a large proportion of the total production costs. If we can effectively reduce the wastage of cotton material used, will significantly help reduce the production costs. To maximize the efficiency of cloth used, cut order planning (COP) is regarded as a key part of the apparel industry process. This process will be based on customer orders and mark planning results. If different size templates can be properly processed and find the optimal solution in the shortest time will be of great benefit to the apparel industry.
Since cut order planning (COP) is an NP-hard problem, when the problem scale increases, the difficulty of finding a solution will increase sharply, so it is almost difficult to find the best solution through h exhaustive method. This paper provides a new approach called the One-by-One heuristic. And using this approach to compare with symbiotic organism search algorithm (SOS)、genetic algorithm (GA)、hybrid approach、Divide-and-Search heuristic. According to the test results, the One-by-One heuristic can significantly improve the solution efficiency cy, also can find the optimal solution for extreme demands

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