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  1. NTU Theses and Dissertations Repository
  2. 工學院
  3. 工業工程學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/28012
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dc.contributor.advisor楊烽正
dc.contributor.authorHsiu-Hui Huangen
dc.contributor.author黃秀惠zh_TW
dc.date.accessioned2021-06-12T18:33:23Z-
dc.date.available2008-08-03
dc.date.copyright2007-08-03
dc.date.issued2007
dc.date.submitted2007-07-31
dc.identifier.citationBalasubramanian, H., L. Moench, J. Fowler and M. Pfund (2004). 'Genetic algorithm based scheduling of parallel batch machines with incompatible job families to minimize total weighted tardiness.' International Journal of Production Research 42(8): 1621-1638.
Beatrice, O., J. R. Brian and H. Franklin (2006). 'Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows.' Applied Intelligence V24(1): 17-30.
Beck, J. C., P. Patrick and S. Evgeny (2002). On the Reformulation of Vehicle Routing Problems and Scheduling Problems.
Chen, H., J. Ihlow and C. Lehmann (1999). A genetic algorithm for flexible job-shop scheduling. Robotics and Automation, 1999. IEEE International Conference on Proceedings. 1999.
Cheng, R., M. Gen and Y. Tsujimura (1996). 'A tutorial survey of job-shop scheduling problems using genetic algorithms--I. representation.' Computers & Industrial Engineering 30(4): 983-997.
Cheng, R., M. Gen and Y. Tsujimura (1999). 'A tutorial survey of job-shop scheduling problems using genetic algorithms, part II: hybrid genetic search strategies.' Computers & Industrial Engineering 36(2): 343-364.
Deb, K. (1999). 'Evolutionary Algorithms for Multi-Criterion Optimization in Engineering Design.' Evolutionary Algorithms in Engineering and Computer Science: 135-161.

Goldberg, D. E. (1953). Genetic algorithms in search optimization and machine learning, Addison-Wesley.
Jie, G., G. Mitsuo and S. Linyan (2006). 'Scheduling jobs and maintenances in flexible job shop with a hybrid genetic algorithm.' Journal of Intelligent Manufacturing V17(4): 493-507.
Lozinski, C. and C. R. Glassey (1988). 'Bottleneck starvation indicators for shop floor control [semiconductor manufacturing process].' Semiconductor Manufacturing, IEEE Transactions on 1(4): 147-153.
Michael Pinedo, X. C. (1999). Operations scheduling with applications in manufacturing and services, McGraw-Hill.
Mitsuo Gen, R. C. (1997). Flow-Shop Sequencing Problems Genetic algorithms and engineering design, Wiley: 173-189.
Palisade (1998). Evolver Developer's Kit, Palisade Corporation.
Palisade (2001). Evolver, Palisade Corporation.
Sule, D. R. (1997). Industrial scheduling, PWS.
Tan, K. C., Y. H. Chew and L. H. Lee (2006). 'A Hybrid Multiobjective Evolutionary Algorithm for Solving Vehicle Routing Problem with Time Windows.' Computational Optimization and Applications V34(1): 115-151.
Yih-Yi, L., C. T. Chen and C. Wu (2005). Reaction chain of process queue time quality control.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/28012-
dc.description.abstract本研究探討具時窗限制的併批暨排程優化問題及其遺傳演算求解法。本研究完整定義問題的數學模式,採用最大化機台產能利用率為求解目標,限制條件為加工型別限制、加工先後順序限制、批次容量限制、及時窗限制。本研究依據問題模式開發遺傳演算求解法,採用「工件排程處理順位編碼法」及「加工作業排程處理順位編碼法」兩種編碼法。據此再提出「忽略時窗限制的解碼�排程演算程序」及「考慮時窗限制的解碼�排程演算程序」兩種解碼方法,前者視時窗限制為軟式限制,後者視時窗限制為硬式限制。兩種編碼和兩種解碼方法組成三種遺傳演算模式:「工件排程處理順位編碼忽略時窗限制求解模式」、「加工作業排程處理順位編碼忽略時窗限制求解模式」、及「工件排程處理順位編碼考慮時窗限制求解模式」。為驗證所提方法的適用性,本研究並以C#程式語言在.NET Framework的平台開發「遺傳演算為基的時窗限制併批暨排程系統」。本研究同時提出「時窗限制鬆緊」、「加工時間差異」、及「問題規模」三個屬性指標嘗試對所提問題進行分類,並制定標竿問題命名法則。為進行實例測試本研究依據所制定的分類法則創建多個不同類型的測試範例。範例測試結果顯示不同遺傳演算模式求解不同類型的問題有不同的效率,且所提的遺傳演算適存函式針對本研究問題能考量多層面的目標且求得有效率的排程解。zh_TW
dc.description.abstractThis research presents the definition and model of the Time Window Constrained Batching Scheduling Problem. The object of this model is to maximize machine utilization. The problem is subjected to four kinds of constraints. GA-based optimization methods are developed to solve this problem, which includes: two encoding methods and two decoding and scheduling procedures. These two decoding and scheduling procedures are “Time Window Constraints Ignored Decoding and Scheduling Procedure” and “Time Window Constraints Considered Decoding and Scheduling Procedure”. The former treats time window constraints as soft constraints and the later treats time window constraints as hard constraints. These encoding methods and decoding and scheduling procedures form three modes of GA-based optimization methods. Based on these proposed methods, a system named GA-based Time Window Constrained Batching Scheduling System (GA-TWCBSS) is developed. This paper defined three properties to classify the research problem and set up the naming rule. According to these properties, several classes of examples are created as test problems. Computation results show that different GA-based optimization methods solve different classes of examples in different performance. In addition, the proposed fitness function can cover multi objectives simultaneously by using the proposed GA-based optimization methods.en
dc.description.provenanceMade available in DSpace on 2021-06-12T18:33:23Z (GMT). No. of bitstreams: 1
ntu-96-R94546025-1.pdf: 970989 bytes, checksum: 077b040ac989c28828132081f6f27353 (MD5)
Previous issue date: 2007
en
dc.description.tableofcontents中文摘要 i
Abstract ii
目錄 iii
表目錄 v
圖目錄 vi
中英文名詞對照表 viii
符號列表 xii
第1章 緒論 1
1.1 研究背景 1
1.2 研究目的 2
1.3 研究方法與流程 2
1.4 章節概要 3
第2章 文獻探討 5
2.1 時窗限制 5
2.2 遺傳演算法 7
2.3 Evolver遺傳演算模式 10
2.4 排程問題的遺傳演算編碼方法 11
2.5 排程問題的目標函式 13
第3章 具時窗限制的併批暨排程優化問題 16
3.1 具時窗限制的併批暨排程優化問題描述與定義 16
3.2 具時窗限制的併批暨排程優化問題數學模式 21
3.3 具時窗限制的併批暨排程優化問題遺傳演算法 32
3.3.1 遺傳演算資料結構 33
3.3.2 解碼�排程演算程序 36
3.3.3 適存函式 53
第4章 標竿問題定義與範例測試 56
4.1 遺傳演算為基的時窗限制併批暨排程系統 56
4.2 問題類型定義 59
4.3 系統驗證與分析 62
4.3.1 各類型範例測試 62
4.3.2 適存函式功能驗證 66
4.3.3 編碼方式探討 71
第5章 結論與未來研究建議 75
5.1 結論 75
5.2 未來研究建議 76
參考文獻 77
附錄A 79
附錄B 94
dc.language.isozh-TW
dc.title具時窗限制的併批暨排程優化問題及其遺傳演算法zh_TW
dc.titleTime Window Constrained Batching Scheduling Problem And Its GA-Based Optimization Methoden
dc.typeThesis
dc.date.schoolyear95-2
dc.description.degree碩士
dc.contributor.oralexamcommittee陳正剛,張建仁,歐陽超
dc.subject.keyword具時窗限制的併批暨排程優化問題,時窗限制排程問題,目標函式,編碼,解碼,遺傳演算法,zh_TW
dc.subject.keywordTime Window Constrained Batching Scheduling Problem,Scheduling Problem with Time Window Constraints,Objective Functions,Encoding,Decoding,Genetic Algorithms,en
dc.relation.page98
dc.rights.note有償授權
dc.date.accepted2007-08-01
dc.contributor.author-college工學院zh_TW
dc.contributor.author-dept工業工程學研究所zh_TW
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