(3.238.7.202) 您好!臺灣時間:2021/03/01 21:21
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:林宜萱
研究生(外文):Lin, I-Hsuan
論文名稱:考慮自動倉儲擺放位置之迴流式混合流程型生產排程問題
論文名稱(外文):Considering Stocker Locations in the Reentrant Hybrid Flow Shop Scheduling Problem
指導教授:林春成林春成引用關係
指導教授(外文):Lin, Chun-Cheng
口試委員:姚銘忠吳建瑋張國浩
口試委員(外文):Yao, Ming-JongWu, Chien-WeiChang, Kuo-Hao
口試日期:2019-06-13
學位類別:碩士
校院名稱:國立交通大學
系所名稱:工業工程與管理系所
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:2019
畢業學年度:107
語文別:中文
論文頁數:40
中文關鍵詞:迴流式混合流程型生產排程自動物料搬運系統自動倉儲混合猴群演算法
外文關鍵詞:ReentrantHybrid flow shopAutomated material handling systemStockerHybrid monkey search algorithm
相關次數:
  • 被引用被引用:0
  • 點閱點閱:106
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
摘 要 I
ABSTRACT II
誌謝 IV
目錄 V
表目錄 VII
圖目錄 VIII
第一章 緒論 1
第二章 問題描述 5
2.1 情境描述 5
2.2 問題描述 5
2.3 相關文獻回顧 8
2.3.1 迴流式混合流程型生產排程 8
2.3.2 自動物料搬運系統 10
第三章 研究方法 12
3.1 解的表示法 13
3.2 解碼與適應度值計算 15
3.2.1 第一部分 15
3.2.2 第二部分 16
3.3 產生起始解 19
3.4 爬行 19
3.5 PSO速度更新猴子位置 20
3.6 蟻群優化局部搜索 21
3.7 突變 22
3.8 觀望後跳躍 22
3.9 翻筋斗 23
3.10 終止條件 23
第四章 實驗結果 24
4.1 參數與實驗環境 24
4.2 演算法參數設定 25
4.3 實驗結果分析之演算法比較 26
4.4 實驗結果分析之自動倉儲擺放位置 30
第五章 結論 36
參考文獻 37
Gartland, K. (1999). Automated material handling system (AMHS) framework user requirements document: Version 1.0. International SEMATECH.
Pillai, D. et al. (1999). Integration of 300 mm fab layouts and material handling auto-mation. In 1999 IEEE International Symposium on Semiconductor Manufac-turing Conference Proceedings (Cat No. 99CH36314) IEEE Press, pp. 23-26.
Ruiz, R. et al. (2010). The hybrid flow shop scheduling problem. European journal of operational research, 205(1), 1-18.
Gupta, J. N. (1988). Two-stage, hybrid flowshop scheduling problem. Journal of the Operational Research Society, 39(4), 359-364.
Gourgand, M. et al. (2000). Meta-heuristics for the deterministic hybrid flow shop problem. Journal Europeen Des Systemes Automatises, 34(9), 1107-1136.
Ying, K. C. et al. (2006). Multiprocessor task scheduling in multistage hybrid flow-shops: an ant colony system approach. International Journal of Produc-tion Research, 44(16), 3161-3177.
Zandieh, M. et al. (2006). An immune algorithm approach to hybrid flow shops scheduling with sequence-dependent setup times. Applied Mathematics and Computation, 180(1), 111-127.
Marichelvam, M. K. et al. (2017). Hybrid monkey search algorithm for flow shop scheduling problem under makespan and total flow time. Applied Soft Compu-ting, 55, 82-92.
Shelokar, P. S. et al. (2007). Particle swarm and ant colony algorithms hybridized for improved continuous optimization. Applied mathematics and computa-tion, 188(1), 129-142.
Kim, J. et al. (2016). Semiconductor FAB layout design analysis with 300-mm FAB data:“Is minimum distance-based layout design best for semiconductor FAB design?”. Computers & Industrial Engineering, 99, 330-346.
Lee, S. et al. (2019). Iterative two-stage hybrid algorithm for the vehicle lifter location problem in semiconductor manufacturing. Journal of Manufacturing Systems, 51, 106-119.
Wang, C. N., Hsu, H. P., Dang, D. C., & Nguyen, Q. T. (2019). The remaining time concept for dispatching on roller belt conveyor in 450-mm wafer fabrications. The International Journal of Advanced Manufacturing Technology, 1-15.
Meller, R. D. (1997). The multi-bay manufacturing facility layout prob-lem. International Journal of Production Research, 35(5), 1229-1237.
Castillo, I. et al. (2004). Integrating design and production planning considerations in multi-bay manufacturing facility layout. European Journal of Operational Re-search, 157(3), 671-687.
Wang, H. et al. (2017). A NSGA-II based memetic algorithm for multiobjective par-allel flowshop scheduling problem. Computers & Industrial Engineering, 113, 185-194.
Shahvari, O. et al. (2018). A comparison of two stage-based hybrid algorithms for a batch scheduling problem in hybrid flow shop with learning ef-fect. International Journal of Production Economics, 195, 227-248.
Fu, Y. et al. (2019). Scheduling Dual-Objective Stochastic Hybrid Flow Shop With Deteriorating Jobs via Bi-Population Evolutionary Algorithm. IEEE Transac-tions on Systems, Man, and Cybernetics: Systems.
Zhang, W. et al. (2019). Hybrid Multiobjective Evolutionary Algorithm based on Dif-ferential Evolution for Flow Shop Scheduling Problems. Computers & Indus-trial Engineering, 130, 661-670.
Li, Z., et al. (2019). Bi-objective hybrid flow shop scheduling with common due date. Operational Research, 1-26.
Pranzo, M. (2004). Batch scheduling in a two-machine flow shop with limited buffer and sequence independent setup times and removal times. European Journal of Operational Research, 153(3), 581-592.
Wang, L. et al. (2006). An effective hybrid genetic algorithm for flow shop schedul-ing with limited buffers. Computers & Operations Research, 33(10), 2960-2971.
Liu, B. et al. (2008). An effective hybrid PSO-based algorithm for flow shop sched-uling with limited buffers. Computers & Operations Research, 35(9), 2791-2806.
Li, J. Q. et al. (2015). Solving the large-scale hybrid flow shop scheduling problem with limited buffers by a hybrid artificial bee colony algorithm. Information Sciences, 316, 487-502.
Dugardin, F. et al. (2010). New multi-objective method to solve reentrant hybrid flow shop scheduling problem. European Journal of Operational Research, 203(1), 22-31.
Ying, K. C. et al. (2014). Bi-objective reentrant hybrid flowshop scheduling: an iter-ated Pareto greedy algorithm. International Journal of Production Re-search, 52(19), 5735-5747.
Shen, J. N. et al. (2016). A modified teaching–learning-based optimisation algorithm for bi-objective re-entrant hybrid flowshop scheduling. International Journal of Production Research, 54(12), 3622-3639.
Cho, H. M. et al. (2017). A two-level method of production planning and scheduling for bi-objective reentrant hybrid flow shops. Computers & Industrial Engi-neering, 106, 174-181.
Lee, B. et al. (2008). A due-date based production control policy using WIP balance for implementation in semiconductor fabrications. International Journal of Production Research, 46(20), 5515-5529.
Cho, H. M. et al. (2012). Preemptive goal programming based heuristic methods for reentrant flow shop planning with bi-objective. Journal of the Society of Korea Industrial and Systems Engineering, 35.
Deb, K. et al. (2000). A fast elitist non-dominated sorting genetic algorithm for mul-ti-objective optimization: NSGA-II. In International conference on parallel problem solving from nature. pp. 849-858. Springer, Berlin, Heidelberg.
Cho, H. M. et al. (2011). Bi-objective scheduling for reentrant hybrid flow shop using Pareto genetic algorithm. Computers & Industrial Engineering, 61(3), 529-541.
Mousavi, S. M. et al. (2018). An efficient bi-objective algorithm to solve re-entrant hybrid flow shop scheduling with learning effect and setup times. Operational Research, 18(1), 123-158.
Zhang, X. Y. et al. (2018). A re-entrant hybrid flow shop scheduling problem with machine eligibility constraints. International Journal of Production Re-search, 56(16), 5293-5305.
Agrawal, N. et al. (2006). Multi-objective optimization of the operation of an industri-al low-density polyethylene tubular reactor using genetic algorithm and its jumping gene adaptations. Industrial & engineering chemistry research, 45(9), 3182-3199.
Bahri, N. et al. (2001). A comparison of unified vs. segregated automated material handling systems for 300 mm fabs. In 2001 IEEE International Symposium on Semiconductor Manufacturing. ISSM 2001. Conference Proceedings (Cat. No. 01CH37203). IEEE Press, pp. 3-6.
Zhao, R. Q. et al. (2008). Monkey algorithm for global numerical optimiza-tion. Journal of Uncertain Systems, 2(3), 165-176.
Nawaz, M. et al. (1983). A heuristic algorithm for the m-machine, n-job flow-shop se-quencing problem. Omega, 11(1), 91-95.
Tseng, L. Y. et al. (2010). A hybrid genetic algorithm for no-wait flowshop scheduling problem. International journal of production economics, 128(1), 144-152.
Ding, J. Y. et al. (2015). An improved iterated greedy algorithm with a Tabu-based reconstruction strategy for the no-wait flowshop scheduling problem. Applied Soft Computing, 30, 604-613.
Bean, J. C. (1994). Genetic algorithms and random keys for sequencing and optimiza-tion. ORSA journal on computing, 6(2), 154-160.
Tasgetiren, M. F. et al. (2007). A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem. European journal of operational research, 177(3), 1930-1947.
Rajkumar, R. et al. (2009). An improved genetic algorithm for the flowshop schedul-ing problem. International Journal of Production Research, 47(1), 233-249.
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔