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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 楊烽正 | |
| dc.contributor.author | Chia-Yang Lee | en |
| dc.contributor.author | 李佳陽 | zh_TW |
| dc.date.accessioned | 2021-06-17T08:44:17Z | - |
| dc.date.available | 2021-08-07 | |
| dc.date.copyright | 2019-08-07 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-08-07 | |
| dc.identifier.citation | Baruwa, O. T., & Piera, M. A. (2016). A coloured Petri net-based hybrid heuristic search approach to simultaneous scheduling of machines and automated guided vehicles. International Journal of Production Research, 54(16), 4773-4792.
Bilge, Ü., & Ulusoy, G. (1995). A time window approach to simultaneous scheduling of machines and material handling system in an FMS. Operations Research, 43(6), 1058-1070. Brandimarte, P. (1993). Routing and scheduling in a flexible job shop by tabu search. Annals of Operations research, 41(3), 157-183. Chaudhry, I. A., Mahmood, S., & Shami, M. (2011). Simultaneous scheduling of machines and automated guided vehicles in flexible manufacturing systems using genetic algorithms. Journal of Central South University of Technology, 18(5), 1473. Gen, M., & Cheng, R. (1997). Genetic algorithms and engineering optimization (Vol. 7). New York: John Wiley & Sons. Gen, M., & Lin, L. (2014). Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey. Journal of Intelligent Manufacturing, 25(5), 849-866. Gen, M., Zhang, W., Lin, L., & Yun, Y. (2017). Recent advances in hybrid evolutionary algorithms for multiobjective manufacturing scheduling. Computers & Industrial Engineering, 112, 616-633. Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning, 3(2), 95-99. Han, Z., Wang, D., Liu, F., & Zhao, Z. (2017). Multi-AGV path planning with double-path constraints by using an improved genetic algorithm. PloS one, 12(7), e0181747. Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press. Kacem, I., Hammadi, S., & Borne, P. (2002). Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 32(1), 1-13. Larranaga, P., Kuijpers, C. M., Murga, R. H., & Yurramendi, Y. (1996). Learning Bayesian network structures by searching for the best ordering with genetic algorithms. IEEE transactions on systems, man, and cybernetics-part A: systems and humans, 26(4), 487-493. Liang, Y., Lin, L., Gen, M., & Chien, C. F. (2012). A hybrid evolutionary algorithm for FMS optimization with AGV dispatching. In Proceedings of the 42nd international conference on computers and industrial engineering, 296.1-296.14. Lin, L., & Gen, M. (2008). A random key-based genetic algorithm for AGV dispatching in FMS. International Journal of Manufacturing Technology and Management, 16(1-2), 58-75. Mousavi, M., Yap, H. J., Musa, S. N., Tahriri, F., & Dawal, S. Z. M. (2017). Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization. PloS one, 12(3), e0169817. Nishi, T., Hiranaka, Y., & Grossmann, I. E. (2011). A bilevel decomposition algorithm for simultaneous production scheduling and conflict-free routing for automated guided vehicles. Computers & Operations Research, 38(5), 876-888. Rossi, A. (2014). Flexible job shop scheduling with sequence-dependent setup and transportation times by ant colony with reinforced pheromone relationships. International Journal of Production Economics, 153, 253-267. Saidi-Mehrabad, M., Dehnavi-Arani, S., Evazabadian, F., & Mahmoodian, V. (2015). An Ant Colony Algorithm (ACA) for solving the new integrated model of job shop scheduling and conflict-free routing of AGVs. Computers & Industrial Engineering, 86, 2-13. Syswerda, G. (1989). Uniform crossover in genetic algorithms. Paper presented at the Third International Conference on Genetic Algorithms. Tabatabaei, A., F. Torabi & T. Paitoon (2018). Simultaneous scheduling od machines and automated guided vehicles utilizing heuristic search algorithm. 2018 IEEE 8th Annal Computing and Communication Workshop and Conference (CCWC): 54-59. Wang, L., Zhou, G., Xu, Y., Wang, S., & Liu, M. (2012). An effective artificial bee colony algorithm for the flexible job-shop scheduling problem. The International Journal of Advanced Manufacturing Technology, 60(1-4), 303-315. Zhang, G., Gao, L., & Shi, Y. (2011). An effective genetic algorithm for the flexible job-shop scheduling problem. Expert Systems with Applications, 38(4), 3563-3573. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74587 | - |
| dc.description.abstract | 本研究首先定義彈性零工生產與搬運排程問題，再提出具遺傳演算優化的求解法，有效降低排程系統中產品的最大完工時間。已知各產品的加工作業順序、不同候選機台和加工時間、及搬運設備搬運時間，模擬實務工廠生產。目標是規劃一套最佳的加工作業順序及選定加工機台和搬運設備，最小化產品的最大完工時間。然而計算產品完工時間必須由加工作業、機台加工及搬運設備的細節排程中求得，因此提出一精確的排程演算程序模擬生產系統中的排程。本研究除了提出貪婪式的經驗求解法外，也使用遺傳演算法求解問題，並在遺傳演算法初始解中嘗試加入適當比例的貪婪解，以提升求解效率。為了驗證演算機制的效能，以(Liang et al., 2012)文獻做測試範例並另自創大型複雜標竿問題進行驗證。結果顯示本研究提出的遺傳優化演算法能顯著地減少產品的最大完工時間，也較貪婪式經驗求解法求得更佳的解。此外，因實務上有不同機台布置及不同加工特性產品，本研究也測試在不同情境下的搬運設備分析，提供使用者一項做決策的工具。 | zh_TW |
| dc.description.abstract | This paper defines Flexible Job and Material Delivery Scheduling Problem and uses genetic algorithm to construct a solving method, effectively reduce the maximal completion time of products in the system. Before simulating a schedule, the production process sequence of each product, the different candidate machines and processing time, and the handling time of handling equipment are known. The goal is to minimize product maximal completion time by planning an optimal set of processing operations, selected machines and handling equipment. However, the calculation of product maximal completion time can be evaluated only when detailed schedules of processing operations, machines and handling equipment are available. This work derives a concise simulation algorithm to generate a schedule in the production system. Our research proposes not only greedy heuristic method but also genetic algorithm to solve this problem. This study also attempts to add an appropriate proportion of greedy solution in the initial solution of the genetic algorithm to improve the efficiency of the solution. In order to verify the effectiveness of each methods, the academic literature (Liang et al., 2012) and another large-scale complex standard problem was verified. After applying this problem to test several benchmarks, the results shows that our research can significantly reduce the maximal completion time of products and obtain a great solution. The results also has a better performance than greedy heuristic method. In addition, there are different machine layout and different processing characteristics of product in practice. Our research tests the analysis of handling equipment in different situations and provides users with a decision-making tool. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T08:44:17Z (GMT). No. of bitstreams: 1 ntu-108-R06546003-1.pdf: 7377873 bytes, checksum: 00b61a2680a5f59978d548afd9b3b951 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 誌謝 i
摘要 ii Abstract iii 目錄 iv 圖目錄 viii 表目錄 xi 1. 緒論 1 1.1. 研究背景與動機 1 1.2. 研究目的 2 1.3. 研究方法 3 2. 文獻探討 4 2.1. 彈性零工生產與搬運排程相關問題 4 2.1.1. 生產排程問題 4 2.1.2. 排程相關文獻 5 2.2. 遺傳演算法 7 2.3. 小結 10 3. 彈性零工生產與搬運排程問題及演算求解法 11 3.1. 彈性零工生產與搬運排程問題(Flexible Job and Material Delivery Scheduling Problem)定義 11 3.1.1. 問題描述及假設 11 3.2. 彈性零工生產與搬運排程問題之求解模式 22 3.2.1. 彈性零工生產與搬運排程問題的隨機解建構法 22 3.2.2. 彈性零工生產與搬運排程問題的貪婪解建構法 23 3.3. 彈性零工生產與搬運排程問題模式的遺傳演算求解法 29 3.3.1. 染色體的編碼 30 3.3.2. 染色體的適應值 30 3.3.3. 遺傳演算的母體初始化 31 3.3.4. 加工作業段的交配及突變運算 31 3.3.5. 選定AGV段與選定加工機台段的交配及突變運算 34 3.3.6. 染色體篩選法和停止條件 36 3.3.7. 初始母體加入貪婪解 36 3.3.8. 小結 37 4. 彈性零工生產與搬運排程問題的求解測試及應用 39 4.1. 標竿問題 39 4.1.1. 標竿問題格式 39 4.1.2. Y群標竿問題 40 4.1.3. 自行定義的L群標竿問題 43 4.2. 求解系統 45 4.3. 範例測試及效能分析 52 4.3.1. 彈性零工生產與搬運排程問題範例測試和效能分析 53 4.3.2. 不同情境範例測試與分析 62 4.3.3. 小結 78 5. 結論與未來研究建議 79 5.1. 結論 79 5.2. 未來研究建議 80 參考文獻 81 附錄一 83 Kacem 15x10完全彈性零工生產標竿問題 83 Brandimarte 15x8部分彈性零工生產標竿問題 84 附錄二 87 8機台直線型布置的搬運時間矩陣 87 10機台U型布置的搬運時間矩陣 87 10機台直線型布置的搬運時間矩陣 88 附錄三 89 L8-8-3標竿問題所有可能貪婪解的最大產品完工時間和數量 89 附錄四 90 Y9-5-4問題最佳產品最大完工時間362的 X=a,b,c解 90 Y9-5-5問題最佳產品最大完工時間362的 X=a,b,c解 91 附錄五 92 Y3-4-3標竿問題測試30次每次各方法的最佳產品最大完工時間 92 Y9-5-4標竿問題測試30次每次各方法的最佳產品最大完工時間 93 Y9-5-5標竿問題測試30次每次各方法的最佳產品最大完工時間 94 附錄六 95 L8-8-5標竿問題測試30次每次各方法的最佳產品最大完工時間 95 L15-10-6標竿問題測試30次每次各方法的最佳產品最大完工時間 96 L15-8-6標竿問題測試30次每次各方法的最佳產品最大完工時間 97 附錄七 98 L8-8-x標竿問題各AGV和各機台詳細稼動率 98 L8-8-x標竿問題各AGV和各機台詳細稼動率 98 L15-10-x標竿問題各AGV和各機台詳細稼動率 99 L15-10-x標竿問題各AGV和各機台詳細稼動率 100 L15-8-x標竿問題各AGV和各機台詳細稼動率 101 L15-8-x標竿問題各AGV和各機台詳細稼動率 102 附錄八 103 不同加工和搬運時間比例直線型分析圖示和數據 103 | |
| dc.language.iso | zh-TW | |
| dc.subject | 彈性零工生產與搬運排程問題 | zh_TW |
| dc.subject | 搬運設備模擬分析 | zh_TW |
| dc.subject | 遺傳演算法 | zh_TW |
| dc.subject | 排程演算程序 | zh_TW |
| dc.subject | Flexible Job and Material Delivery Scheduling Problem | en |
| dc.subject | Handling equipment simulation analysis | en |
| dc.subject | Genetic Algorithm | en |
| dc.subject | Scheduling Algorithm | en |
| dc.title | 彈性零工生產與搬運排程問題及啟發式求解法 | zh_TW |
| dc.title | Flexible Job and Material Delivery Scheduling Problem And Heuristic Solving Methods | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 羅士哲,藍俊宏,楊曙榮 | |
| dc.subject.keyword | 彈性零工生產與搬運排程問題,排程演算程序,遺傳演算法,搬運設備模擬分析, | zh_TW |
| dc.subject.keyword | Flexible Job and Material Delivery Scheduling Problem,Scheduling Algorithm,Genetic Algorithm,Handling equipment simulation analysis, | en |
| dc.relation.page | 109 | |
| dc.identifier.doi | 10.6342/NTU201902703 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-08-07 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
| 顯示於系所單位： | 工業工程學研究所 | |
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