近年來，大眾對於服飾的需求逐漸增加，製造商必須快速地生產不同種類的服飾吸引顧客。訂單分碼規劃(COP)在成衣業中是被重視的問題之一，如果能夠將各種尺寸之需求量妥善安排在不同模板中，將會降低布料浪費以減少成本。訂單分碼規劃(COP)著重於在模板的長度、寬度和高度皆受到切割設備及切割台的限制下，找尋各種尺寸在模板中之合適組合。
本文提出了一種基於基因遺傳演算法的啟發式，其目的為在滿足需求量的情況下，找出最佳的模板組合並盡可能地減少耗費布料之總長度。首先採用我們提出的啟發式所獲得之可行解作為基因遺傳演算法之初始解，接著使用基因遺傳演算法優化初始解以找尋最佳模板組合。有別於以往大部分的文獻考慮設置、材料及設備等成本，我們只考量布料耗費的總長度。此外，我們將應用本文所提出的方法所產生之結果與其他文獻進行比較，研究結果顯示，本文所提出的方法相較於其他文獻中提出的方法具有更好的表現。

In recent years, the demand for garments has gradually increased and the manufacturers must produce garments rapidly in order to attract customers. Cut order planning (COP) is one of the most important issue in the apparel industry. If there is an appropriate stencils arrangement, it may cut down the cost and fabric wastage. The problem focuses on determining the size combination of a pattern which is subject to the length of cutting table, width, demand orders, and the height of cutting equipment.
This paper presents a Lite Heuristic with Genetic Algorithm (GA) to figure out the COP problem where the objective is to find the optimal combination of stencils to meet to fulfill the demand requirement and minimize the total length. We first apply Lite Heuristic to produce a feasible initial solution of GA in the first stage, and then use GA to yield the optimal feasible solution. Instead of revolving around the setup cost, material cost and so on, we merely consider the fabric length. Additionally, we compare the results with other literature. The results show that our proposed method has better performance than the methodology proposed in other literature.

Abeysooriya, R. P. (2012). Genetic Optimization of Cut Order Planning in Apparel Manufacturing, Doctoral dissertation, University of Sri Jayewardenepura, Nugegoda.
Abeysooriya, R. P., & Fernando, T. G. I. (2012). Canonical genetic algorithm to optimize cut order plan solutions in apparel manufacturing. Journal of Emerging Trends in Computing and Information Sciences, 3(2), 150-154.
Abeysooriya, R. P., & Fernando, T. G. I. (2012). Hybrid approach to optimize cut order plan solutions in apparel manufacturing. International Journal of Information and Communication Technology Research, 2(4), 348-353.
Burns, S. A. (Eds.) (2002). Recent advances in optimal structural design. ASCE Publications.
Chu, P. C., & Beasley, J. E. (1998). A genetic algorithm for the multidimensional knapsack problem. Journal of heuristics, 4(1), 63-86.
Coit, D. W., & Smith, A. E. (1996). Reliability optimization of series-parallel systems using a genetic algorithm. IEEE Transactions on reliability, 45(2), 254-260.
Dumishllari, E., & Guxho, G. (2015). Impact of Marker on Cut Plan in Garment Production. International Journal of Innovative Research in Science, Engineering and Technology, 4(8), 7377-7381.
Degraeve, Z., & Vandebroek, M. (1998). A mixed integer programming model for solving a layout problem in the fashion industry. Management Science, 44(3), 301-310.
Degraeve, Z., Gochet, W., & Jans, R. (2002). Alternative formulations for a layout problem in the fashion industry. European Journal of Operational Research, 143(1), 80-93.
De Silva, P. H. H. P. N., Lanel, G. H. J., & Perera, M. T. M. (2017). Integer Quadratic Programming (IQP) Model for Cut Order Plan. IOSR Journal of Mathematics (IOSR-JM), 13(2), 76-80.
Filipič, B., Fister, I., & Mernik, M. (2006). Evolutionary search for optimal combinations of markers in clothing manufacturing. Proceedings of the 8th annual conference on Genetic and evolutionary computation, ACM, 1661-1666.
Filipič, B., Fister, I., & Mernik, M. (2008). Optimization of markers in clothing industry. Engineering Applications of Artificial Intelligence, 21(4), 669-678.
Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning, 3(2), 95-99.
Haque, M. N. (2016). Impact of Different Sorts of Marker Efficiency in Fabric Consumption. International Journal of Textile Science, 5(5), 96-109.
Hui Patrick, C. L., Ng Frency, S. F., & Chan Keith, C. C. (2000). A study of the roll planning of fabric spreading using genetic algorithms. International Journal of Clothing Science and Technology, 12(1), 50-62.
Jebari, K., & Madiafi, M. (2013). Selection methods for genetic algorithms. International Journal of Emerging Sciences, 3(4), 333-345.
Jacobs-Blecha, C., Ammons, J. C., Schutte, A., & Smith, T. (1997). Cut order planning for apparel manufacturing. IIE transactions, 30(1), 79-90.
Martens, J. (2004). Two genetic algorithms to solve a layout problem in the fashion industry. European Journal of Operational Research, 154(1), 304-322.
M'Hallah, R., & Bouziri, A. (2016). Heuristics for the combined cut order planning two‐dimensional layout problem in the apparel industry. International Transactions in Operational Research, 23(1-2), 321-353.
Nascimento, D. B., de Figueiredo, J. N., Mayerle, S. F., Nascimento, P. R., & Casali, R. M. (2010). A state-space solution search method for apparel industry spreading and cutting. International Journal of Production Economics, 128(1), 379-392.
Puasakul, K., & Chaovalitwongse, P. (2013). The development of heuristic for solving multi objective mark planning problem in garment industry. 2013 IEEE International Conference on Industrial Engineering and Engineering Management, 743-747.
Puasakul, K., & Chaovalitwongse, P. (2016). The Review of Mark Planning Problem, Engineering Journal, 20(3).
Reeves, C. R. (Eds.) (1995). Modern Heuristic Techniques for Combinatorial Problems: Advanced Topics in Computer Science. McGRAW-HILL BOOK COMPANY.
Rose, D. M., & Shier, D. R. (2007). Cut scheduling in the apparel industry. Computers & operations research, 34(11), 3209-3228.
Spears, W. M., & Anand, V. (1991). A study of crossover operators in genetic programming. International Symposium on Methodologies for Intelligent Systems, 409-418.
Soni, N., & Kumar, T. (2014). Study of various mutation operators in genetic algorithms. International Journal of Computer Science and Information Technologies, 5(3), 4519-4521.
Umbarkar, A. J., & Sheth, P. D. (2015). Crossover operators in genetic algorithms: a review. ICTACT journal on soft computing, 6(1), 1083-1092.
Wong, W. K., & Leung, S. Y. S. (2008). Genetic optimization of fabric utilization in apparel manufacturing. International Journal of Production Economics, 114(1), 376-387.
Wong, W. K., Chan, C. K., Kwong, C. K., Mok, P. Y., & Ip, W. H. (2005). Optimization of manual fabric-cutting process in apparel manufacture using genetic algorithms. The International Journal of Advanced Manufacturing Technology, 27(1-2), 152-158.
Yun, Y. (2006). Hybrid genetic algorithm with adaptive local search scheme. Computers & Industrial Engineering, 51(1), 128-141.
Yan-mei, L., Shao-cong, Y., & Shu-ting, Z. (2011). Research on cut order planning for apparel mass customization. International Conference on Artificial Intelligence and Computational Intelligence, 267-271.
Zhezhova, S., Demboski, G., & Panov, V. (2013). Optimization of technological process of cutting by use of software applications for cut order planning. Tekstil i obleklo, (3), 77-79.