進階搜尋


 
系統識別號 U0026-2807201519543000
論文名稱(中文) 彈性流程式生產環境之多目標自動排程
論文名稱(英文) Multi-Object Automatic Scheduling Methodology in Flexible Manufacturing Systems
校院名稱 成功大學
系所名稱(中) 工業與資訊管理學系碩士在職專班
系所名稱(英) Department of Industrial and Information Management (on the job class)
學年度 103
學期 2
出版年 104
研究生(中文) 洪嘉宜
研究生(英文) Chia-Yi Hung
學號 R37021040
學位類別 碩士
語文別 中文
論文頁數 45頁
口試委員 指導教授-林清河
口試委員-李昇暾
口試委員-耿伯文
口試委員-林義旭
口試委員-丁介人
中文關鍵字 生產排程  流程式生產  電腦整合製造  先進規劃與排程 
英文關鍵字 Scheduling  Flexible shop manufacturing system(FMS)  Computer integrated manufacturing(CIM)  Adaptive planning and Scheduling(APS) 
學科別分類
中文摘要 在全球化的競爭下,企業為增加各自的競爭優勢,生產模式從傳統的推式生產轉為拉式生產,以減少存貨、過期品的成本風險並提高資產報酬率,生產客戶真正需求的產品。因此企業為了應付市場的轉變,產品製造的過程必須更加富有彈性同時還要精進工廠內部流程,降低生產成本才能在日益變遷的市場裡生存。排程計畫在工廠裡扮演著重要的角色,如何達到滿足客戶需求及資源分配、工廠機台產能發揮至最大降低營運成本是生產排程的目標。本研究利用實際TFT-LCD工廠的排程數據來進行探討。
從實際工廠的現況分析,該工廠為半自動化工廠,有著工廠生產的資料系統,但是工廠的排程仍然由工廠人員進行規劃執行。排程人員在執行排程調度時,需以工廠設定的目標進行排程考量規則,通常工廠設定的目標大多並不會僅單一目標,在多樣化產品限制及目標的考慮下,排程人員需耗費許多心力及時間才能完成,當排程人員不同時,容易因經驗不同導致工廠生產的目標飄移及生產成本增加。
為了減少人員經驗誤差造成工廠目標飄移及生產成本增加提供穩定的排程品質,本研究提出透過簡易的資料查詢來掌握產品的狀況及機台的狀況,將排程與工廠資料即時的進行比較及結合,透過排程及自動化排程相關的文獻探討,提出自動化排程架構並利用遺傳演算法解決提供生產線最佳化的排程以減少工廠因排程反覆異動問題,提高生產排程的品質。
透過實際工廠的排程數據帶入提出的方法執行排程並與現行比較,發現所提出的方法可以提供人員詳細的排程資料,並且效能與現行排程一致,耗費的時間較短,可透過此方法減少人員的工作負荷及工廠的目標飄移。
英文摘要 Summary
Scheduling is an important problem in production planning systems. Determining a method by which to meet customer demand and resource allocation, in addition to factory machine capacity in order maximally reduce the operating cost of the production schedule is an issue. Scheduling needs to be set according to targeted factory scheduling considerations, but in the real world, the targets set at the factory are usually multiple in nature. This study discusses the fact the production schedule planning of TFT-LCD manufacturing company A still relies on staff, and taking in to consideration production diversification constraints and objectives and the fact the this company has its own production database, we proposed an automated scheduling model using the Multi objective Genetic Algorithms(MOGA) in order to provide schedule quality and stability.

Key words: Scheduling , Flexible shop manufacturing system(FMS), Computer integrated manufacturing(CIM), Adaptive planning and Scheduling(APS)

Introduction
Because of global competition, in order to reduce cost to increase return on assets, companies production patterns began to turn pull production and diversification. With changes in production mode, enterprises are facing increasingly shorter production cycle times, increasingly more production conditions, and increased complexity of scheduling problems.
The current production schedule planning of company A is based on the manufacturing plant personnel scheduling, and the scheduling quality performance varies because the individuals doing it are different. Addressing scheduling problems has become a popular research topic. In a recent study, Zhang et al. (2012) proposed the Flexible Manufacturing System (FMS) as a highly automated production systems, the due to the fact that the need for factories to reduce delivery time is considered a very important goal. Ruiz et al. (2008) mentioned that many past studies have been conducted to reduce the differences between theory and reality. In reality, the impacts of the schedule constraints include: available release dates for machines, unrelated parallel machine issues, available machine qualifications, necessity to skip the processing phase, and machine setup time sequence dependency, among other restrictions. Scheduling problems are based on the structural properties of different factories, different consideration limitation to the problem, staff planning and scheduling time constraints, and the inability to guarantee the quality of the schedule, not to mention realistic targets for multiple objects.
Semi-automated environments has real-time automated industrial production data, so automated scheduling has begun to gain increasing importance in the enterprises. We propose the use of real-time factory data through multi-genetic algorithms to provide stable schedule performance and quality.
Materials and Methods
This study considers an automated flexible scheduling production system that orders processing operations in accordance with the path order for each individual product. Each workstation has fixed assumptions for processing time.

In order to address real world scheduling problems and extend the research on this topic, a number of assumptions will be considered as follows:
1. The factory is not only the producing of a single type of product.
2. The batch lot in the machine can’t be interrupted until the process is completed.
3. Production quantities are known.
4. The production processes are not the same.
5. There is no rework.
6. Machine setup time is sequentially dependent.
7. The setup time for each machine is known.
8. The customer demand date is known.
9. The transmission time is not considered.
10. The machine produces only one product at a time.
11. Machine production efficiency varies.
12. Idle time is not included in the setup-time.
This study proposed a methodology for automated scheduling using the Microsoft Visual Basic for Application (VBA) program and Microsoft Access as the database in order to determine feasible solution for scheduling problems through the use of multi-object genetic algorithms.

Results and Discussion
In order to verify the effectiveness of the proposed algorithm, the experimental design made use of Company A as a case for verification and comparison. The system used Excel 2007 VBA to build the program and use Access 2007 as database.
The case assumes the factory has 25 jobs and that the factory process involves 7 operations; each process has to produce a machine group; the number of machine groups includes up to 4 machines and 4 objects respectively: setup-time, maximum output, machine idle time and minimum job delay.

From the results, it was determined that the schedule performance using the proposed algorithm was better, and that the proposed algorithm can provide personnel with decisions and more detailed schedule information, such as dispatch job route and job process start time.

Conclusions
This study considers a flow-shop production scheduling problem occurring in sequence dependent on setup time, taking in to consideration the different production processes and quality problems arising from delays in the production of products resulting from such issues as machine condition so that more realistic and feasible solution can be provided that will lead to more clear schedule information and reduced staff uncertainty.
論文目次 目錄
摘要 I
目錄 VII
表目錄 IX
圖目錄 IX
第一章 緒論 1
1.1研究背景 1
1.2研究動機與目的 2
1.2.1 研究動機 2
1.2.2 研究目的 3
1.3研究限制 3
1.4 研究大綱 4
第二章 文獻探討 5
2.1生產排程問題 5
2.1.1 排程問題類型 5
2.1.2 排程問題解決方法 8
2.2自動化排程系統 10
2.3遺傳演算法 12
2.4 小結 17
第三章 研究方法 18
3.1問題敘述與研究假設 18
3.1.1 問題敘述 18
3.1.2研究假設 20
3.2 模型架構 21
3.2.1 模式參數定義 22
3.2.2 彈性流程式生產排程 23
3.2.3多目標最佳化 24
3.2.4工廠狀態變數 25
3.3遺傳演算法之建構 26
3.3.1編碼 26
3.3.2適度函數 29
3.3.3交配 30
3.3.4突變 32
3.3.5停止條件 32
3.3.6遺傳演算法建構流程 33
第四章 驗證結果與分析 34
4.1系統實作 34
4.2系統數據驗證 35
第五章 結論與未來研究方向 40
5.1結論 40
5.2 未來研究方向 41
參考文獻 42
參考文獻 Arroyo,J. E. and A. de Souza Pereira (2011). "A GRASP heuristic for the multi-objective permutation flowshop scheduling problem." The International Journal of Advanced Manufacturing Technology 55(5-8):741-753.
Bean,J.C.(1994). "Genetic algorithms and Random key for sequencing and optimization. " INFORMS J.Comput. 6(2),154-160.
Chen,J. and F. Chen (2008). "Adaptive scheduling and tool flow control in flexible job shops." International Journal of Production Research 46(15): 4035-4059.
Dao,T.-M.,C. Makrem and S. C. Abou (2007). "A Hybrid Hopfield Neural Networks Based Simulation Approach for Optimisation of Manufacturing Group Scheduling." 工業工程學刊 24(4): 300-308.
Dayou,L.,Y. Pu and Y. Ji (2008). "Development of a multiobjective GA for advanced planning and scheduling problem." The International Journal of Advanced Manufacturing Technology 42(9-10): 974-992.
Ebrahimi,M.,S. M. T. Fatemi Ghomi and B. Karimi (2014). "Hybrid flow shop scheduling with sequence dependent family setup time and uncertain due dates." Applied Mathematical Modelling 38(9-10): 2490-2504.
ElMaraghy,H.,V. Patel and I. B. Abdallah (2000). "Scheduling of manufacturing systems under dual-resource constraints using genetic algorithms." Journal of Manufacturing Systems 19(3): 186-201.
Gen,M. and L. Lin (2013). "Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey." Journal of Intelligent Manufacturing 25(5): 849-866.
Gen,M.,L. Lin and H. Zhang (2009). "Evolutionary techniques for optimization problems in integrated manufacturing system: State-of-the-art-survey." Computers & Industrial Engineering 56(3): 779-808.
Georgy,M. E. (2008). "Evolutionary resource scheduler for linear projects." Automation in Construction 17(5): 573-583.
Gomes,H. C.,F. de Assis das Neves and M. J. F. Souza (2014). "Multi-objective metaheuristic algorithms for the resource-constrained project scheduling problem with precedence relations." Computers & Operations Research 44: 92-104.
Linn,R. and W. Zhang (1999). "Hybrid flow shop scheduling: A survey." Computers & Industrial Engineering 37(1–2): 57-61.
Maulik,U. Bandyopadhyay,S. and Mukhopadhyay,A. (2011). Multiobjective Genetic Algorithms for Clustering – Application in Data Mining and Bioinformatics ,Springer.
Pinedo,M. L. (2011). Scheduling: Theory,Algorithms,and Systems,Springer.
Rajkumar,M.,P. Asokan,N. Anilkumar and T. Page (2010). "A GRASP algorithm for flexible job-shop scheduling problem with limited resource constraints." International Journal of Production Research 49(8): 2409-2423.
Ruiz,R.,F. S. Şerifoğlu and T. Urlings (2008). "Modeling realistic hybrid flexible flowshop scheduling problems." Computers & Operations Research 35(4): 1151-1175.
Ruiz,R. and J. A. Vázquez-Rodríguez (2010). "The hybrid flow shop scheduling problem." European Journal of Operational Research 205(1): 1-18.
Shnits,B.,J. Rubinovitz * and D. Sinreich (2004). "Multicriteria dynamic scheduling methodology for controlling a flexible manufacturing system." International Journal of Production Research 42(17): 3457-3472.
Tang,Y.,R. Liu and Q. Sun (2014). "Schedule control model for linear projects based on linear scheduling method and constraint programming." Automation in Construction 37: 22-37.
Tavakkoli-Moghaddam,R.,F. Jolai,F. Vaziri,P. K. Ahmed and A. Azaron (2005). "A hybrid method for solving stochastic job shop scheduling problems." Applied Mathematics and Computation 170(1): 185-206.
Tuncel,G. (2011). "An integrated modeling approach for shop-floor scheduling and control problem of flexible manufacturing systems." The International Journal of Advanced Manufacturing Technology 59(9-12): 1127-1142.
Vahid Majazi Dalfard,V. R. (2012). "Multi-Projects scheduling with Resource constraints and priority rules by the use of Simulated Annealing Algorithm." Tehnicki vjesnik-Technica Gazette 19(3): 493-499.
Yenisey,M. M. and B. Yagmahan (2014). "Multi-objective permutation flow shop scheduling problem: Literature review,classification and current trends." Omega 45: 119-135.
Zhang,Q.,H. Manier and M. A. Manier (2012). "A genetic algorithm with tabu search procedure for flexible job shop scheduling with transportation constraints and bounded processing times." Computers & Operations Research 39(7): 1713-1723
丁介人 (1999). 不穩定製造系統之探討-改良式遺傳演算法之應用. 博士論文,國立成功大學.
林豐澤 (2005). "演化式計算下篇:基因演算法以及三種應用實例." 智慧科技與應用統計學報 3(1): 29-56.
莊雋雍 (2013). 以基因演算法分析含拘束條件的電腦整合製造系統之自動化排程. 博士論文,國立成功大學.
郭宜雍 (2005). 結合模擬與智慧搜尋法最佳化多機台流線式製程之排程研究. 博士論文,國立成功大學.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2017-08-12起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2017-08-12起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw