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研究生:吳兆凱
論文名稱:發展具Holonic概之兩階段排程法
論文名稱(外文):A study of constructing a two-stage scheduling/rescheduling algorithm in holonic manufacturing environment
指導教授:李祥林李祥林引用關係
學位類別:碩士
校院名稱:國立屏東科技大學
系所名稱:工業管理系
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:125
中文關鍵詞:排程重排程重排時程長度全方位製造系統遺傳基因演算法
外文關鍵詞:SchedulingReschedulingRescheduling planning horizonHolonic manufacturing systemsGenetic algorithms
相關次數:
  • 被引用被引用:19
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排程之目的在於規劃未來的生產作業活動,使其更有效率。然而在排程制定後,常會因為原料短缺、機器故障、緊急訂單等因素而需更改原有的工作內容及進度,使得排程需要不斷地修正。若企業在面臨重排程決策時,能在本身成本可容許的範圍內,減少重排程的變異幅度,縮小重排程的時程範圍,將可減小對於供應鏈及企業本身的衝擊。故本研究藉由全方位製造系統的概念,運用遺傳基因演算法,建構出一個具有兩階段的排程方法。第一階段是在生產週期之前,規劃具備整體最佳化之主生產排程;另外則是在生產過程中,當接到緊急訂單時,執行第二階段之重排程,以適時地反應訂單變化,並規劃新訂單及尚未完成之訂單的作業時程。研究顯示,小範圍的重排程,在成本與反應時間上,均較大範圍的重排程來得佳,此一現象與全方位製造系統中的自主性特徵類似。依此原則,可將小範圍的重排程排作業,委由生產現場處理,而讓生管人員專注於整體生產資源的最佳調度。
Scheduling has been one of the major tasks in operational management. It has direct impact on the efficiency and effectiveness of production operations. In order to obtain a better production schedule to improve the performance of daily production operations and to increase the competition of the business organization, numerous academic researchers and industrial participants have devoted tremendous amount of effort in developing scheduling algorithms. Due to the combinatorial characteristics, the scheduling problems are classified as NP-hard. Therefore, various heuristics, such as genetic algorithm, simulated annealing, and others, have been developed and tested to show the strength of obtaining closed-to-optimal solutions. Besides, it is quite common that new orders and/or rush orders may arrive after the master production schedule has been determined. The need of scheduling new arrivals creates the problem of ‘re-scheduling’. Scheduling and re-scheduling become typical hurdles for production planning personnel. It is very common, when rescheduling is needed, to reschedule all the pending orders. Such rescheduling method results in building a new mater production schedule every time when a new order is received. It is very typical that the production planning department holds a large number of pending orders. To reschedule all the pending orders increases the responding time to confirm a new order and to alter scheduled activities, including material planning, human resource planning, and etc. In the modern industrial environment, longer responding time means less profit. Therefore, how to shorten the responding time of rush orders, i.e., how to reschedule rush order in less time, has become one important issue in production planning domain.
This thesis develops a two-stage methodology to perform scheduling and rescheduling activities. The first stage determines the master production schedule. At the second stage, a partial rescheduling algorithm is developed to shorten the responding time to newly arrived rush orders. The partial rescheduling algorithm only reschedule the first several orders and the rush order and leaves the sequence of the rest orders unchanged. The problem is formulated as a nonlinear problem, which encompasses production costs, material and finished products storage costs, communication cost for changing delivery time, emergent procurement cost and penalty cost for late delivery. At both stages, genetic algorithm is used to solve scheduling and re-scheduling problems. The results show that the partial rescheduling methodology has advantages in terms of responding times and costs. It is also found that the numbers of orders to be rescheduled are between 5 and 7. Statistical tests show that capacity utilization, material storage cost, and communication cost have significant effect on rescheduling.
第一章 緒論
1.1 研究動機 …………………………………………………… 1
1.2 研究目的 …………………………………………………… 4
1.3 研究限制 …………………………………………………… 5
1.4 研究流程 …………………………………………………… 6
1.5 論文架構 …………………………………………………… 8
第二章 文獻探討
2.1 排程理論之探討 …………………………………………… 9
2.2 生產系統控制架構之演進 ………………………………… 16
2.3 分散式製造系統之概述 …………………………………… 19
2.4 階層式與分散式控制架構之比較 ………………………… 21
2.5 全方位製造系統之論述 …………………………………… 26
2.6 重排程決策問題與分析 …………………………………… 27
2.7 遺傳基因演算法之理論與運作 …………………………… 41
2.8 結語 ………………………………………………………… 53
第三章 主生產排程與重排程模式之建構
3.1 模式背景之說明 …………………………………………… 54
3.2 模式基本假設 ……………………………………………… 57
3.3 模式之設計與相關說明 …………………………………… 58
3.4 遺傳基因演算法 …………………………………………… 67
第四章 實證分析
4.1 範例描述 …………………………………………………… 70
4.2 實驗結果 …………………………………………………… 74
4.3 分析與討論 ………………………………………………… 101
第五章 結論與建議
5.1 結論 ………………………………………………………… 110
5.2 建議 ………………………………………………………… 112
參考文獻 ………………………………………………………… 113
圖 目 錄
圖1-1 研究流程圖 ………………………………………………… 6
圖2-1 四種製造系統之控制架構 ………………………………… 17
圖2-2 Holon整體與部份之示意圖 …………………………………25
圖2-3 三種基本holon的運行關係 ……………………………… 27
圖2-4 階層式的Scheduler holon與其它holon的對應關係 …… 28
圖2-5 執行重排程決策之問題 …………………………………… 35
圖2-6 遺傳基因演算法之運算流程 ……………………………… 47
圖3-1 模式運作流程圖 …………………………………………… 55
圖3-2 兩點交配運算法運作圖 …………………………………… 68
圖3-3 位移突變法運作圖 ………………………………………… 69
圖4-1 高產能利用率的產能需求圖 ……………………………… 71
圖4-2 低產能利用率的產能需求圖 ……………………………… 71
圖4-3 ---世代族群的散佈圖 ………………………………… 76
圖4-4 ---配對池的散佈圖 …………………………………… 76
圖4-5 +--世代族群的散佈圖 ………………………………… 78
圖4-6 +--配對池的散佈圖 …………………………………… 78
圖4-7 -+-世代族群的散佈圖 ………………………………… 80
圖4-8 -+-配對池的散佈圖 …………………………………… 80
圖4-9 ++-世代族群的散佈圖 ………………………………… 82
圖4-10 ++-配對池的散佈圖 ……………………………………82
圖4-11 --+世代族群的散佈圖 …………………………………84
圖4-12 --+配對池的散佈圖 ……………………………………84
圖4-13 +-+世代族群的散佈圖 …………………………………86
圖4-14 +-+配對池的散佈圖 ……………………………………86
圖4-15 -++世代族群的散佈圖 …………………………………88
圖4-16 -++配對池的散佈圖 ……………………………………88
圖4-17 +++世代族群的散佈圖 …………………………………90
圖4-18 +++配對池的散佈圖 ……………………………………90
圖4-19 ---+++重排張數對於總成本的影響 ………………93
圖4-20 +----+重排張數對於總成本的影響 ………………94
圖4-21 -+--+-重排張數對於總成本的影響 ………………95
圖4-22 ++-+--重排張數對於總成本的影響 ………………96
圖4-23 --++--重排張數對於總成本的影響 ………………97
圖4-24 +-+-+-重排張數對於總成本的影響 ………………98
圖4-25 -++--+重排張數對於總成本的影響 ………………99
圖4-26 +++++重排張數對於總成本的影響 …………………100
圖4-27 世代族群及配對池平均總成本趨勢線圖 …………………101
圖4-28 世代族群及配對池總成本差距圖 …………………………101
圖4-29 ---+++重排張數與總成本的離散圖 ………………102
圖4-30 -++-+-重排張數對於總成本的影響 ………………105
圖4-31 +---+-重排張數對於總成本的影響 ………………106
圖4-32 -+---+重排張數對於總成本的影響 ………………107
圖4-33 +-+--+重排張數對於總成本的影響 ………………108
表 目 錄
表2-1 與排程相關的績效指標 ……………………………………… 11
表2-2 常用的優先派工法則 ………………………………………… 14
表2-3 四種製造系統控制架構之比較 ……………………………… 18
表2-4 傳統階層式與分散式控制架構之比較 ……………………… 24
表2-5 有關製造性holons與驗證HMS技術與績效之文獻 ……………30
表2-6 全方位製造系統與其它分散式製造系統之比較 …………… 33
表2-7 重排程文獻之整理 …………………………………………… 38
表4-1 高產能利用率的訂單資料 …………………………………… 72
表4-2 低產能利用率的訂單資料 …………………………………… 73
表4-3 實驗變數設定表 ……………………………………………… 74
表4-4 ---的實驗結果 …………………………………………… 75
表4-5 +--的實驗結果 …………………………………………… 77
表4-6 -+-的實驗結果 …………………………………………… 79
表4-7 ++-的實驗結果 …………………………………………… 81
表4-8 --+的實驗結果 …………………………………………… 83
表4-9 +-+的實驗結果 …………………………………………… 85
表4-10 -++的實驗結果 ……………………………………………87
表4-11 +++的實驗結果 ……………………………………………89
表4-12 緊急訂單資料表 ………………………………………………91
表4-13 重排程之實驗因子表 …………………………………………92
表4-14 ---+++重排後的結果 …………………………………93
表4-15 +----+重排後的結果 …………………………………94
表4-16 -+--+-重排後的結果 …………………………………95
表4-17 ++-+--重排後的結果 …………………………………96
表4-18 --++--重排後的結果 …………………………………97
表4-19 +-+-+-重排後的結果 …………………………………98
表4-20 -++--+重排後的結果 …………………………………99
表4-21 ++++++重排後的結果 …………………………………100
表4-22 重排程統計分析表 ……………………………………………103
表4-23 重排程二次實驗因子表 ………………………………………104
表4-24 -++-+-重排後的結果 …………………………………105
表4-25 +---+-重排後的結果 …………………………………106
表4-26 -+---+重排後的結果 …………………………………107
表4-27 +-+--+重排後的結果 …………………………………108
表4-28 二次重排程統計分析結果表 …………………………………109
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