跳到主要內容

臺灣博碩士論文加值系統

(35.172.136.29) 您好!臺灣時間:2021/07/26 21:46
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:黃冠霖
研究生(外文):Guan-Lin Huang
論文名稱:運用系統模擬與基因演算法於解決非等效平行機台之人力分配排程
論文名稱(外文):The Applications of Simulation-GA Method to the Unrelated Parallel Machine Worker Assignment Scheduling Problem
指導教授:黃信豪黃信豪引用關係
學位類別:碩士
校院名稱:國立虎尾科技大學
系所名稱:工業工程與管理研究所
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:2009
畢業學年度:97
語文別:中文
論文頁數:77
中文關鍵詞:排程系統模擬基因演算法非等效平行機台總完工時間
外文關鍵詞:SchedulingSimulationGenetic AlgorithmUnrelated Parallel MachineMakespan
相關次數:
  • 被引用被引用:0
  • 點閱點閱:590
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:2
排程(Scheduling)是生產管理中十分重要的領域,它的意義為在某一段時間內,分配有限的資源及決定一群工作進行的順序,以達成一定的目標或效率。因此排程往往被視為一個有限資源最佳化的分配工具。
處理非等效平行機台之排程問題明顯地比相同機台及等效機台之排程困難許多,更何況增加考慮工作安排中人力分配的情形,則問題變更加複雜化。為了在短時間內有效找出非等效平行機台最佳的工作安排與人力指派的方式,本研究應用系統模擬與基因演算法來尋找可使總完工時間最小的人力指派及排程。本研究中,首先利用系統模擬軟體來建構非等效平行機台排程模式,接著使用軟體之GA模組來尋找最佳排程及人力配置的問題。模擬之結果會與窮舉法之結果作比較,期望找出符合實務排程需求之最佳建議。
模擬結果顯示,窮舉法雖然能保證求得最佳解,卻非常消耗時間;至於使用系統模擬與基因演算法是可行的並且可在很短時間內找到相似最佳解。因此,本研究建議可針對不同的需求選擇適當的解決方法。
Production scheduling is a very important area of management, and its significance for a certain period of time, the allocation of limited resources and decided to order a group of work in order to reach certain goals or efficiency. So scheduling is often seen as a limited allocation of resources to the best tools.
The present study, the first to use simulation software to construct non-equivalent parallel machine scheduling model, and then use software to find the best GA module scheduling and staffing issues. Simulation results of the exhaustive method and the results compared to expectations in line with the practice schedule to identify the needs of the best available.
Simulation results show that although the exhaustive method can guarantee the optimal solution obtained, it is very time-consuming; As for the use of system simulation and genetic algorithm is feasible and can be found in a very short period of time similar to optimal solution. Therefore, this study suggests that different needs can be to choose the appropriate solution.
中文摘要 ---------------------------------------------------------------------------- i
英文摘要 ---------------------------------------------------------------------------- ii
誌謝 ---------------------------------------------------------------------------- iii
目錄 ---------------------------------------------------------------------------- v
表目錄 ---------------------------------------------------------------------------- vi
圖目錄 ---------------------------------------------------------------------------- vii
符號說明 ---------------------------------------------------------------------------- viii
第一章 緒論---------------------------------------------------------------------- 1
1.1 研究背景------------------------------------------------------------ --- 1
1.2 研究目的---------------------------------------------------------------- 3
1.3 研究範圍---------------------------------------------------------------- 4
1.4 研究方法---------------------------------------------------------------- 5
1.5 研究流程---------------------------------------------------------------- 5
1.6 論文架構---------------------------------------------------------------- 7
第二章 文獻探討---------------------------------------------------------------- 8
2.1 系統模擬---------------------------------------------------------------- 8
2.1.1 前言---------------------------------------------------------------------- 8
2.1.2 系統模擬適用的範圍與時機---------------------------------------- 9
2.1.3 系統模擬的優缺點---------------------------------------------------- 10
2.1.4 系統模擬的程序------------------------------------------------------- 11
2.1.5 系統模擬在排程應用的相關文獻---------------------------------- 12
2.1.6 小結---------------------------------------------------------------------- 15
2.2 基因演算法------------------------------------------------------------- 15
2.2.1 前言---------------------------------------------------------------------- 15
2.2.2 基因演算法的演算流程---------------------------------------------- 17
2.2.3 基因演算法的優缺點------------------------------------------------- 26
2.2.4 基因決策參數---------------------------------------------------------- 27
2.2.5 基因演算法在排程應用的相關文獻------------------------------- 28
2.2.6 小結---------------------------------------------------------------------- 31
2.3 非等效平行機台排程問題------------------------------------------- 32
2.3.1 前言---------------------------------------------------------------------- 32
2.3.2 非等效平行機台排程問題的相關文獻---------------------------- 32
2.3.3 小結---------------------------------------------------------------------- 34
第三章 問題描述---------------------------------------------------------------- 35
3.1 人力分配排程問題說明---------------------------------------------- 35
3.2 系統問題定義與分析------------------------------------------------- 36
3.3 系統基本假設與限制------------------------------------------------- 38
第四章 研究方法---------------------------------------------------------------- 41
4.1 模擬模式建立與衡量指標------------------------------------------- 41
4.2 模擬程序---------------------------------------------------------------- 44
第五章 系統參數分析---------------------------------------------------------- 46
5.1 參數分析---------------------------------------------------------------- 46
5.2 田口實驗法------------------------------------------------------------- 46
5.2.1 基因實驗參數設定---------------------------------------------------- 47
5.2.2 實驗結果與分析------------------------------------------------------- 48
第六章 實驗結果---------------------------------------------------------------- 52
6.1 模擬之執行------------------------------------------------------------- 52
6.2 模擬結果---------------------------------------------------------------- 55
6.3 系統模擬與窮舉法之比較------------------------------------------- 58
6.3.1 數據比較---------------------------------------------------------------- 58
6.3.2 小結---------------------------------------------------------------------- 62
第七章 結論與未來展望------------------------------------------------------- 63
7.1 結論---------------------------------------------------------------------- 63
7.2 未來與展望------------------------------------------------------------- 64
參考文獻 ---------------------------------------------------------------------------- 66
附錄一 高權重數據------------------------------------------------------------- 72
附錄二 低權重數據------------------------------------------------------------- 75
1.王治元 (2004),智慧型基因演算法於多目標排程之發展與應用-以PCB鑽孔作業為例,元智大學工業工程與管理研究所,碩士論文。
2.阮永漢 (2002),系統模擬與基因演算法於完全相同機台排程之應用,元智大學工業工程與管理研究所,碩士論文。
3.邱創鈞 (2008),考量權重策略下之即時性存貨路徑問題,大葉大學工業工程與科技管理研究所,碩士論文。
4.何仁祥 (2003),以案例式推理為基礎的基因演算法解決生產排程問題,元智大學工業工程與管理研究所,碩士論文。
5.吳承宗 (2003),應用模擬方法於印刷電路板生產排程影響因素之研究,元智大學工業工程與管理研究所,碩士論文。
6.林則孟 (2001),系統模擬,初版,滄海書局,台中市。
7.姜林杰祐、張逸輝、陳家明、黃家祚 (2001),系統模擬,華泰文化事業公司出版,台北。
8.殷郁然 (2000),製造業預防管理機制之研究--虛擬工廠之建構方法,東海大學工業工程研究所,碩士論文。
9.郭佩純 (2001),多屬性待測品之半導體偵測區機台排程模擬分析,國立清華大學工業工程與管理研究所,碩士論文。
10.徐政功 (2005) ,以基因演算法計算多機流程型工廠在有限暫存之最小完工時間,中原大學工業工程研究所,碩士論文。
11.徐烈昭 (2001),應用塔布搜尋法於非等效平行機台之研究-以PCB鑽孔作業為例,元智大學工業工程與管理研究所,碩士論文。
12.徐智遠 (2006),DRAM模組廠SMT線排程問題之研究,國立台灣科技大學工業管理所,碩士論文。
13.張斐章、張麗秋、黃浩倫 (2004),類神經網路理論與實務,臺灣東華書局股份有限公司,台北。
14.陳宜欣 (1997),遺傳演算法在JSP排程問題上的研究,國立中央大學資訊管理所,碩士論文。
15.陳智堯 (2006),整合基因演算法及田口方法於火力機組排程之研究,國立中山大學電機工程學系研究所,碩士論文。
16.劉志宏 (2000),不確定加工時間之平行機台排程,國立清華大學工業工程與管理研究所,碩士論文。
17.曾毓文 (2001),運用系統模擬與遺傳演算法從事非相關平行機器排程之研究,國立台北科技大學生產系統工程與管理研究所,碩士論文。
18.黃文品 (2008),開發混合式巨集啟發式方法求解具順序相依整備時間之非等效平行機台排程問題,國立政治大學資訊管理所,碩士論文。
19.黃元鴻 (2004),晶圓廠在製品流程時間之實證模式建構,台灣大學工業工程學研究所,碩士論文。
20.葉柏慶 (2007),運用系統模擬與基因演算法於解決相同機台之人力分配排程,國立虎尾科技大學工業工程與管理研究所,碩士論文。
21.趙文涼 (2000),基因演算法於單機交期絕對偏差及整備成本最小化排程問題之應用,元智大學工業工程研究所,碩士論文。
22.賴勇見 (2005),應用系統模擬於鞋模具生產與派工之探討,雲林科技大學工業工程與管理研究所碩士班,碩士論文。
23.鍾文豪 (2008),以基因演算法求解有限資源多專案排程問題,中原大學工業工程研究所,碩士論文。 
24.謝丞杰 (2007),應用系統模擬於流程型生產工廠派工法則之探討,大同大學資訊經營學所,碩士論文。
25.駱景堯、吳泰熙、張俊仁 (1999),「結合模糊論與遺傳演算法於多目標彈性製造系統」,工業工程學刊,第16卷,第三期。
26.蘇木春 (1997),機器學習類神經網路、模糊系統以及基因演算法則,全華出版社,台北。
27.蘇朝墩 (2006),品質工程,第四版,三民書局,台北。
28.簡聰海、鄒靜寧 (2000),系統模擬,高立圖書有限公司,台北。
29.Allahverdi, A. & Mittenthal, J. (1994). “Scheduling on M parallel machines subject to random breakdowns to minimize expected mean flow time”, Naval Research Logistics Quarterly, Vol. 41 pp.677-682.
30.Chan, F.T.S. & Chan, H.K. (2003). “Analysis of dynamic dispatching rules for a flexible manufacturing system”, Journal of Materials Processing Technology, Vol.138, pp.325–331.
31.Cheng, T.C.E. & Wang, G. (2000). “Single Machine Scheduling with Learning Effect Considerations”, Annals of Operation Research, Vol.98, 273-290.
32.Conway, R.W. (1963). “Some Tactical Problems in Digital Simulation” , Management Science, Vol.10, 1, pp. 47-61.
33.Croce, D.F., Tadei, R. & Volta G. (1995). “A genetic algorithm for the job shop problem”, Computers & Operations Research, Vol.22, p.15.
34.Davis, E. & Jaffe, J (1981), “Algorithms for scheduling tasks on unrelated processors”, Journal of the Association for Computing Machinery, Vol.25, No.4, pp.721-736.
35.Figielska, E. (1999). “Preemptive scheduling with changeovers: using column generation technique and genetic algorithm”, Computers and Industrial Engineering, Vol.37, pp.81-84.
36.Funda, S. S. & Ulusoy, G. (1999). “Parallel Machine Scheduling with Earliness and Tardiness Penalties”, Computers & Operations. Research, Vol.26(8), pp.773-787.
37.Ghirardi, M. & Potts, C.N. (2005). “Makespan minimization for scheduling unrelated parallel machines: A recovering beam search approach”, European Journal of Operational Research, Vol. 165, pp. 457-467.
38.Glass, C.A., Gupta, J.N.D. & Potts, C.N. (1994). “Lot Streaming in Three- stage Production Processes”, European Journal of Operational Research, Vol.75, pp.378-394.
39.Goldberg, D.E. & Richardson, J. (1985). “Genetic Algorithms with Sharing for Multimodal Function Optimization”, Proceedings of the Second International Conference on Genetic Algorithms, pp. 41-49.
40.Goldberg, D.E. (1989). Genetic algorithms in search, Optimization and Machine Learning, Addison-Wesley, Reading, MA.
41.Grefenstette, J.J. (1986). “Optimization of control parameters for genetic algorithms”, IEEE Transactions on systems, man & cybernetics, pp.122-128.
42.Hariri, A.M.A. & Potts, C.N. (1991). “Heuristics for scheduling unrelated parallel machines”, Computers & Operations. Research, Vol.18, pp.323-331.
43.Harrell, C.R., Robert, E.B., Thomas, J.G., & Jack, R.A.M. (1995). System improvement using simulation, 3rd edition, Orem, Utah: PROMODEL Corporation.
44.Hickman, A. & Matthias U. H. (2000). “Eliminate bottlenecks integrated analysis in Em-Plant”, Proceedings of the 2000 Winter Simulation Conference.
45.Horowitz, E. & Sahni, S. (1976). “Exact and approximate algorithms for scheduling nonodentical processors”, Journal of the Association for computing Machinery, Vol.23(2), pp.317-327.
46.Hu, P. (1993). An Efficient Heuristic for the Worker Assignment Problem in the Identical and Nonidentical Parallel-Machine Model”, Ph.D. dissertation, Department of Industiral and Management Systems Engineering, The Pennsylvania State University, University Park, Pennsylvania.
47.Hu, P. (2005). “Minimizing total tardiness for the worker assignment scheduling problem in the identical parallel-machine model”, International Journal of Advanced Manufacturing Technology, Vol. 23, pp.383-385.
48.Karp, R.M. (1972). “Reducibility Among Combinatorial Problems”, In Complexity of Computer Computations, New York: Plenum, pp.85-103.
49.Kim, D.W., Na, D.G. & Chen, F.F. (2003). “Unrelated parallel machine scheduling with setup times and total weighted tardiness objective”, Robotics and Computer Integrated Manufacturing, Vol.19, pp173-181.
50.Kumar, N., Hemant, L. & Srinvasan, G. (1996). “A Genetic Algorithm for Job shop Scheduling –A Case Study”, Computer in Industry, Vol.31, pp.155-160.
51.Lawler, E.L. & Labetoulle, J. (1978). “On Preemptive Scheduling of Unrelated Parallel Processors by Linear Programming”, Journal of the ACM, Vol.25, pp.612-619.
52.Lin, L. & Cochran, J.K. (1992). “Assembly line systemdynamic behavior for high priority job processing”, International Journal of Production Research, Vol 30(7), pp1683-1697.
53.Murata, T., Ishibuchi, I. & Tanaka, H. (1996). ”Genetic Algorithm for Flowshop Scheduling Problem”, International Journal of Computers and Industrial Engineering, Vol.30, pp.1061-1071.
54.Padberg, F. (2002). “Using Process Simulation to Compare Scheduling Strategies for Software Projects”, Software Engineering Conference, pp.581-590.
55.Piersma, N. & Dijk, W.V. (1996). “A local search heuristic for unrelated parallel machine scheduling with efficient neighborhood search”, Mathematical and Computer Moddelling, Vol.25,No9,pp.11-19.
56.Pritsker, A.A.B. (1992). “Simulation:The Premier Technique of Industrial Engineering”, Industrial Engineering, Vol.24,7,pp.25-26.
57.Rajendran, C. (1994). “A no-wait flowshop scheduling heuristic to minimize makespan”, Orperational Research Society, Vol.45, pp.472-478.
58.Shannon, R.E. (1998). “Introduction to the art and science of simulation”, Proceedings of the 1998 Winter Simulation Conference, pp.7-14.
59.Schaffer, J.D., Caruana, R.A., Eshelman, L.J. & Das, R. (1989). “A study of control parameters affecting online performance of genetic algorithms for function optimization”, Proceedings of the Third International Conference on Genetic Algorithms, pp.51-60.
60.Srikanth, K.I. & Saxenab, B. (2004). “Improved genetic algorithm for the permutation flow shop scheduling problem”, Computers & Operations. Research, pp.593-606.
61.Tadahiko, M., Ishibuchi, H. & Tanaka, H. (1996). “Genetic Algorithms for Flow shop Scheduling Problems”, Computer Ind Engng, Vol.30(4), pp.1061-1071.
62.Ting, C.K., Li, S.T. & Lee, C.N. (2001). “TGA: A New Integrated Approach to Evolutionary Algorithms”, Congress on Evolutionary Computation (CEC2001), pp.917-924.
63.Varela, R., Vela, C.R., Puente, J. & Gomez, A. (2003). “A knowledge-based evolutionary strategy for scheduling problems with bottlenecks”, European Journal of Operational Research, pp.57-71.
64.Wang, C.S. & Uzsoy, R. (2002). “A genetic algorithm to minimize maximum lateness on a batch processing machine”, z, pp.1621-1640.
65.Wang, L., Zhang, L. & Zhang, D. (2006). “An effective hybrid genetic algorithm for flow shop scheduling with limited buffers”, Computer & Operations Research, pp.2960-2971.
66.Whitten, J.L. & Bentley, L.D. (1998). Systems Analysis and Design Methods, 4th ed, Boston: Irwin/McGraw-Hill.
67.Williams, E.J. (1998). “Analysis of conveyor systems within automotive final assembly”, Proceedings of the 1998 Winter Simulation Conference, pp.915-920.
68.Young, H.P, Jack, E.M. & David, M.M. (1998). “Simulation and analysis of the Mercedes-Benz All Activity Vehicle (AAV) production facility”, Proceedings of the 1998 Winter Simulation Conference, pp.921-926.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top