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研究生:施富棋
研究生(外文):Fu-chi Shih
論文名稱:迴流考量之流程型工廠排程問題研究
論文名稱(外文):The Flow-shop Scheduling Problem with Reentrant Consideration
指導教授:駱景堯駱景堯引用關係
指導教授(外文):Chin-yao Low
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:工業工程與管理研究所碩士班
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:98
中文關鍵詞:暫存區限制粒子群最佳化演算法迴流型流程式生產排程混合型基因演算法
外文關鍵詞:particle swarm optimizationhybrid genetic algorithmbuffers limitedreentrant flow-shop scheduling problem
相關次數:
  • 被引用被引用:3
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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:1
流程式生產排程為排程作業中的主要方法之一。近幾年隨著彈性製造的發展,導致傳統上的生產型態被擴展為具有迴流型(Re-entrant)特性的生產製造系統,尤其是在半導體產業(Semi-conductor)以及印刷電路板產業(Printed Circuit Board)之中,無論是迴流型零工式生產排程(Reentrant Job-shop Scheduling)或是迴流型流程式生產排程(Reentrant Flow-shop Scheduling),對原先的運用均有別於傳統的排程技術。
本研究針對迴流型(Reentrant)流程式工廠的生產排程問題,考量暫存區限制條件(Buffer Limited),並以總完工時間最小化為目標,發展出粒子群最佳化演算法(Particle Swarm Optimization)及混合型基因演算法(Hybrid Genetic Algorithms)兩種啟發式演算法。
在影響演算法執行之參數上,採用田口實驗求出在不同規模下之最佳參數組合,再利用求出的參數組合來增進在本研究中所提出的啟發式演算法之求解品質及效率,並比較此兩演算法之成效。研究發現,在求解的品質上,混合型基因演算法優於粒子群最佳化演算法;在求解效率上,粒子群最佳化演算法則優於混合型基因演算法。
Flow-shop scheduling is one of the most important and popularly discussed subjects in production scheduling. In the recent years, the development of flexible manufacturing system caused the traditional production styles to expand their original characters into reentrant processing, particularly in semi-conductor industry and printed circuit board industry. Moreover, the applications in reentrant job-shop scheduling or reentrant flow-shop scheduling are distinctive from the traditional ones. In this study, some characteristics such as the reentrant processing and buffer limit within the shop stations are taken into account for the proposed a flow shop scheduling problem with minimization of the makespan. Two heuristics based on particle swarm optimization (PSO) and hybrid genetic algorithm (HGA) are developed for solving the proposed scheduling problem. The experimental results demonstrate that the hybrid genetic algorithm achieves higher solution quality than particle swarm optimization heuristic; nevertheless, particle swarm optimization provides more solution efficiency than hybrid genetic algorithm.
中文摘要 i
Abstract ii
目錄 iii
表目錄 vi
圖目錄 viii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究範圍與限制 2
1.4 研究方法與架構 4
第二章 文獻探討 7
2.1 排程問題之分類 7
2.2 排程的評估準則 8
2.3 排程的求解方法 8
2.4 迴流生產系統相關文獻探討 10
2.5 生產排程具暫存區限制之相關文獻探討 13
2.6 粒子群最佳化演算法之相關文獻探討 18
2.7 基因演算法之相關文獻之探討 21
第三章 數學模式建構 26
3.1 問題描述 26
3.2 數學模式之建構 27
3.2.1 符號定義 27
3.2.2 數學模式 28
3.2.3 範例說明 30
第四章 啟發式演算法之建構 33
4.1 粒子群最佳化演算法之建立 33
4.1.1 粒子群最佳化演算法之建構流程 34
4.1.2 計算適合度值 37
4.1.3 候選名單之建立 37
4.2 基因演算法之建立 40
4.2.1 基因演算法之建構流程 40
4.2.2 染色體及母體數 43
4.2.3 適合度值(Fitness Value) 43
4.2.4 育種池 43
4.2.5 交配(Crossover) 44
4.2.6 突變(mutation) 45
4.2.7 終止條件 46
4.3 起始解法 46
4.4 範例說明 47
第五章 結果分析與討論 51
5.1 實驗數據與參數設定 51
5.2 田口實驗 53
5.2.1 粒子群最佳化演算法 54
5.2.2 PSO參數之確認性實驗 58
5.2.3 小結 63
5.2.4 混合型基因演算法 64
5.2.5 HGA參數之確認性實驗 69
5.2.6 小結 74
5.3 PSO與HGA之分析比較 74
第六章 結論與建議 83
6.1 結論 83
6.2 未來研究之建議 84
參考文獻 85
[1] 林建民,2003,混合基因演算法應用於具迴流特性流程工廠之研究,國立台灣科技大學,碩士論文。
[2] 盧研伯,2003,混合式模擬退火法應用於具迴流特性流程工廠之研究,國立台灣科技大學,碩士論文。
[3] 張謙閔,2007,兩階段等效平行機台於流程式生產系統批量流之研究,大葉大學,碩士論文。
[4] 吳建廣,2003,混合塔布搜尋法應用於具迴流特性流程工廠之研究,國立台灣科技大學,碩士論文。
[5] Allahverdi, A., and F.S. Al-Anzi, 2006, “A PSO and a Tabu search heuristics for the assembly scheduling problem of the two-stage distributed database application,” Computers & Operations Research, vol. 33, pp.1056-1080.
[6] Caraffa, V., S. Ianes, T.P. Bagchi, and C. Striskandarajah, 2001, “Minimizing makespan in a blocking flowshop using genetic algorithms,” International journal of production economics, vol. 70, pp.101-115
[7] Chen, J.S., C.H. Pan, and C.M. Lin, 2008, “A Hybrid Genetic Algorithm for the Re-entrant Flow-shop Scheduling Problem,” Expert System with Applications, vol. 34, pp.570-577.
[8] Choi, S.C. and Y.D., Kim, 2008, “Minimizing makespan on an m-machine re-entrant flowshop”, Computers & Operations Research, vol. 35, pp.1684-1696.
[9] Demirkol, E. and R., Uzsoy, 2000, “Decomposition Method for Reentrant Flow Shops with Sequence-dependent Setup Time”, Journal of Scheduling, vol. 3, issue 3, pp.155-177.
[10] Sule, D. R., 1996, “Industrial Scheduling,” PWS Publishing Company, Boston.
[11] Holland, J.H., 1975, “Adaptation in Natural and Artificial Systems,” the University of Michigan Press.
[12] Jolai, F., M. Rabbani, S. Amalnick, A. Dabaghi, M. Dehghan, and M.Y. Parast, “Genetic algorithm for bi-criteria single machine scheduling problem of minimizing maximum earliness and number of tardy jobs,” Applied Mathematics and Computation, vol. 194, pp.552-560.
[13] Kennedy, J. and R. Eberhart, 1995, “Particle swarm optimization,” IEEE International Conference on Neural Networks, vol. 4, pp.1942-1948.
[14] Kubiak, W., X.C., Lou, and Y.M., Wang, 1996, “Mean Flow Time Minimization in Reentrant Job Shops with a Hub,” Operation Research, vol. 44, No. 5, pp.764-776.
[15] Lian, Z., X. Gu, and B. Jiao, 2008, “A Novel Particle Swarm Optimization Algorithm for Permutation Flow-shop Scheduling to Minimize Makespan,” Chaos Solution & Fractals, vol. 35, pp.851-861.
[16] Lian, Z., B. Jiao, and X. Gu, 2006, “A Similar Particle Swarm Optimization Algorithm for Job-shop Scheduling to Minimize Makespan”, Applied Mathematics and Computation, vol. 183, pp.1008-1017.
[17] Liang, J.J. and P.N. Suganthan, 2005, “Dynamic Multi-swarm Particle Swarm Optimizer with Local Search,” IEEE Congress on Evolutionary Computation, Vol. 1, pp.522-528.
[18] Liao, C.J., C.T. Tseng, and P. Luarn, 2007, “A Discrete Version of Particle Swarm Optimization for Flowshop Scheduling Problem,” Computers & Operations Research, vol. 34, pp.3099-3111.
[19] Liu, B., L. Wang, and Y.H., Jin, 2007, “An Effective Hybrid PSO-based for Flow Shop Scheduling with Limited Buffers”, Computers & Operations Research, pp.1-16.
[20] Michel Baudin, 1990, Manufacturing System Analysis with Application to Production Scheduling, Prentice Hall Inc.
[21] Nawaz, M., 1983, “A Heuristic Algorithm for the M-Machine, N-Job Flow-shop Sequencing Problem,” Management Science, vol. 11, pp.91-95.
[22] Nowicki, E., 1999, “The Permutation Flow Shop with Buffers: A Tabu Search Approach,” European Journal of Operational Research, vol. 116, pp.205-219.
[23] Ronconi, A.P., and L.R.S. Henriques, 2009, “Some heuristic algorithms for total tardiness minimization in a flowshop with blocking,” OMEGA (The International Journal of Management Science), vol. 37, pp.272-281.
[24] Sawik, T., 2000, “Mixed Integer Programming for Scheduling Flexible Flow Lines with Limited Intermediate Buffers”, Mathematical and Computer Modelling, vol. 31, pp.39-52.
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