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研究生:吳東儒
研究生(外文):Tung-JuWu
論文名稱:偏光板裁切計畫之編排研究
論文名稱(外文):The Study of Polarizer Cutting Plan Scheduling Problem
指導教授:黃悅民黃悅民引用關係
指導教授(外文):Yueh-Min Huang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:工程科學系專班
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:84
中文關鍵詞:偏光板裁切計畫節省物料降低成品庫存提昇產能粒子最佳化演算法
外文關鍵詞:Polarizer Cutting PlanMaterial SavingsInventory ReductionReduce inventoryParticle Swarm Optimization
相關次數:
  • 被引用被引用:5
  • 點閱點閱:355
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
偏光板裁切計畫(Polarizer Cutting Plan, PCP),是依據當月客戶訂單對各種不同規格偏光片的需求,選擇適當之刀模與裁切順序,編排出偏光板裁切計畫,其主要目的為偏光卷物料使用量的最小化,以節省物料之耗用成本。本研究主要探討如何將現行編排作業,從人工以經驗法則為基準的方式,轉為透過電腦系統自動化產生最佳化之編排裁切計畫,且在無增加任何固定及變動成本下,達到節省物料、降低成品庫存、提昇產能,進而增進企業競爭力。
粒子群最佳化(Particle Swarm Optimization, PSO)演算法歸類於群體智慧,是以群體為基礎的隨機搜尋之最佳化方法,已經被成功地應用在解最佳化問題上,且可以在很短的時間內得到非常不錯的結果。本研究為有效的節省物料之耗用,分別提出並實作以結合傳統人工之「經驗自動化(Combine Experience Automation, CEPA)」、結合傳統人工之「經驗窮舉法(Combine Exhaustive System, CEHS)」與結合「粒子群最佳化演算法(Combine Particle Swarm Optimization, CPSO)」等三種系統自動化編排方式所求出之裁切計畫,並比較其優劣,以產生最佳的裁切計畫。
本研究經實際模擬驗證比較之結果,顯示本研究提出的「CPSO」為一個結合PSO演算法的系統編排方式確實能夠編排出近似最佳的裁切計畫,產生一個物料耗用較少之裁切計畫,進而增進企業競爭力。

Polarizer Cutting Plan (PCP) is based on the month of customer orders, demand for a variety of different specifications of the polarizer, and selects the appropriate cutting mold and cutting sequence, arrangement of the polarizer cutting plan. There is no related work was seen before about this scheduling problem. This work is the first article study about the polarizer cutting scheduling problem.
There are three methods are adopted in the scheduling system. First, we propose an automatic scheduling algorithm by reducing material approach for minimizing the polarizer material usage to save the consumption cost of the materials. Then, using the appropriate greedy method to find the near the optimal solution. Finally, this study proposes a particle swarm optimization algorithm for minimizing the polarizer material usage. Furthermore, according to the rule of artificial way, we utilize the computer system automation to optimize the arrangement of cutting plan, Material Savings and the Inventory Reduction without increasing any fixed and variable costs.
Experimental results demonstrate the robustness of the proposed PSO algorithm in terms of solution quality.
摘要 I
ABSTRACT II
誌謝 III
目錄 IV
表目錄 VI
圖目錄 VIII
符號表 X
1 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究範圍與限制 3
1.4 研究流程 4
1.5 論文架構 4
2 問題定義 6
2.1 偏光板主要應用 6
2.2 偏光板基本構造 7
2.3 偏光板生產製程 8
2.4 偏光板裁切刀模 11
2.5 偏光板裁切計畫(PCP) 12
3 文獻探討 19
3.1 裁切問題 19
3.2 粒子群最佳化(PSO)演算法 21
3.2.1 PSO演算法公式及參數說明 23
3.2.2 PSO演算法基本步驟與流程 26
3.2.3 PSO演算法實例分析 27
4 研究方法 31
4.1 系統架構與求解模式說明 31
4.1.1 生產規劃系統(Master Production Schedule, MPS) 32
4.1.2 刀模系統(Knife Mold System, KMS) 33
4.1.3 偏光板裁切計畫(Polarizer Cutting Plan, PCP) 34
4.1.4 求解模式1(系統流程) 37
4.1.5 求解模式2(編排流程) 38
4.2 經驗自動化(COMBINE EXPERIENCE AUTOMATION, CEPA) 39
4.3 經驗窮舉法(COMBINE EXHAUSTIVE SYSTEM, CEHS) 43
4.4 粒子群最佳化(COMBINE PSO, CPSO) 46
4.4.1 編碼設計 46
4.4.2 刀模選取機制 48
4.4.3 解評估方式 48
4.4.4 PSO之參數設定與虛擬程式碼 49
5 實務驗證 50
5.1 模擬資料驗證(案例1) 50
5.2 實務資料驗證(案例2) 57
5.3 經驗法則之驗證 76
5.4 PSO速度V之驗證 77
5.5 統整與分析 78
6 結論與展望 80
6.1 結論 80
6.2 展望 81
參考文獻 82

英文文獻
[1]A., Salman, I., Ahmad, and S., Al-Madani, “Particle swarm optimization for task assignment problem, Microprocessors and Microsystems, 26, pp. 363-371, 2003.
[2]C., Reynolds, “Flocks, Herds, and Schools: A Distributed Behavioral Model, “ Computer Graphics, Vol. 21, pp. 25-34, 1987.
[3]F., Van den Bergh, and AP., Engelbrecht,“Cooperative learning in neural network using particle swarm optimizers, South African Computer Journal, 26, pp. 84-90, 2000.
[4]G., Washer, H., Haubner, and H., Schumann, “An improved typology of cutting and packing problems, European Journal of Operational Research, Vol. 183, pp. 1109-1130, 2007.
[5]H., Dyckhoff, “A Typology of Cutting and Packing Problems, European Journal of Operational Research, Vol. 44, pp. 145-159, 1990.
[6]H., Yoshida, K., Kawata, Y., Fukuyama, and Y., Nakanishi, “A particle swarm optimization for reactive power and voltage control considering voltage security assessment,IEEE Transactions on Power Systems, 15 , pp.1232-1239, 2000.
[7]J., Kennedy, and W. M., Spears, “Matching Algorithms to Problems: An Experimental Test of the Particle Swarm and Some Genetic Algorithms on the Multimodal Problem Generator, In IEEE World Congress on Computational Intelligence, pp.74-77, 1998.
[8]K. P., Wang, L. Huang, C. G. Zhou, and W. Pang, “Particle swarm optimization for traveling salesman problem,Proceedings of International Conference on Machine Learning and Cybernetics, vol. 3, pp. 1583-1585, 2003.
[9]M., Savelsbergh, “A Branch-and-Price algorithm for the generalized assignment problem, Operations Research, Vol. 45, pp. 831–841, 1997.
[10]M. F., Tasgetiren, Y. C., Liang, M., Sevkli, and G., Gencyilmaz,“A Particle swarm optimization algorithm for makespan and total flowtime minimization in permutation flowshop sequencing problem, European Journal of Operational Research, 177, pp. 1930-1947, 2007.
[11]P. C., Gilmore, and R. E., Gomory, “A Linear Programming Approach to the Cutting Stock Problem, Operations Research, Vol. 9, pp. 849-859, 1961.
[12]P. C., Gilmore, and R. E., Gomory, “A Linear Programming Approach to the Cutting Stock Problem – part II, Operations Research, Vol. 11, pp. 863-888, 1963.
[13]P. C., Gilmore, and R. E., Gomory, “Multi-stage cutting stock problems of two and more dimensions, Operations Research, Vol. 13, pp. 94-120, 1965.
[14]P. C., Gilmore, and R. E., Gomory, “The Theory and Computation of Knapsack Functions, Operations Research, Vol. 14, pp. 1045-1074, 1966.
[15]R. C., Eberhart, and J., Kennedy, “Particle Swarm Optimization, Proceedings of IEEE Interna-tional Conference on Neural Networks, Vol. IV, pp. 1942-1948, 1995.
[16]R. C., Eberhart, and J., Kennedy, “A New Optimizer Using Particle Swarm Theory, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, IEEE Service Cen-ter, Piscataway, NJ, Nagoya, Japan, pp. 39-43, 1995.
[17]R. C., Eberhart, and Y., Shi, “Particle Swarm Optimization: Developments, Application and Resources, Proceedings of the 2001 Congress on Evolutionary Computation, vol. 1, pp. 81-86, 2001.
[18]Y., Shi, and R. C., Eberhart, “Parameter selection in particle swarm optimization, Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming New York. pp. 591-600, 1998.
[19]Y., Shi, and R. C., Eberhart, “Fuzzy adaptive particle swarm optimization, Proceedings of the IEEE Congress on Evolutionary Computation. (CEC 2001), Seoul, Korea. 2001.
[20]Z., Lian, B., Jiao, and X., Gu, “A similar particle swarm optimization algorithm for job-shop scheduling to minimize makespan, Applied Mathematics and Computation, 183(2) , pp. 1008-1017, 2006.
中文文獻
[21]王雅賢,「粒子群最佳化演算法改良之研究」,中原大學資訊管理學系碩士論文,2006。
[22]李雅惠,「縮短偏光板製造業新產品開發送樣時程之研究-以X 公司為例」,國立中央大學工業管理研究所碩士論文,2009。
[23]李麗、牛奔,「粒子群優化算法」,冶金工業出版社,北京,2009。
[24]馬慧民、吳勇、叶春民,「車輛路徑問題的並行粒子群算法研究」,上海理工大學學報,2007。
[25]黃朝義(1999/03),偏光膜介紹,財團法人光電科技工業協進會,取自: http://www.pida.org.tw/optolink/optolink_pdf/88032012.pdf
[26]劉育青,「利用數學規劃與啟發式演算法求解角度限制產品的裁切問題」,國立成功大學工業與資訊管理學系碩士論文,2010。
[27]鄭春生、周雅君,「以資料探勘為基建構偏光板品質異常診斷系統」,中華民國品質學會第43 屆年會暨第13 屆全國品質管理研討會。
[28]盧立昕,「BH型鋼裁切問題之啟發式解法」,國立成功大學土木工程學系碩士論文,2009。
[29]顏上堯、李旺蒼、施佑林,「路徑基礎類粒子群最佳化演算法於求解含凹形節線成本最小成本轉運問題之研究」,國立中央大學土木工程學系, 2007。
[30]羅仕堂、顏聰玲、吳東儒,「分散式搜尋策略於旅遊行程最佳化模式之研究」,2011第六屆智慧生活科技研討會論文集,pp. 2399-2342,2011。
[31]蘇明健,「應用啟發式演算法求解多目標型鋼切割計畫之研究」,國立中山大學資訊管理研究所碩士論文,2009。
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