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研究生:李基彰
研究生(外文):Chi-Chang Li
論文名稱:靈活製造環境下隨機供應鏈模型之分析
論文名稱(外文):Under Agile Manufacturing Environment, the Analysis of Stochastic Supply Chain Models
指導教授:陳武林陳武林引用關係
學位類別:碩士
校院名稱:靜宜大學
系所名稱:資訊管理學系研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2006/07/
畢業學年度:94
語文別:中文
論文頁數:85
中文關鍵詞:回溯最佳化線性規劃靈活製造整體生產規劃供應鏈混合整數線性規劃隨機最佳化
外文關鍵詞:Stochastic OptimizationRetrospective OptimizationMixed Integer Linear ProgrammingLinear ProgrammingAgile ManufacturingAggregate Production PlanningSupply Chain
相關次數:
  • 被引用被引用:2
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  • 下載下載:53
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由於供應鏈與產品製造環境日趨複雜且都有受到不確定性因素的影響,所以如何分析整體供應鏈的運作狀況是現今供應鏈管理所要面臨的挑戰。本研究針對靈活製造環境下隨機供應鏈模型提出分析方法。
首先,使用線性規劃(Linear Programming, LP)來建立整體供應鏈模型,以求得供應鏈各節點的最佳產量與物流量以使總成本為最低。再者,我們探討工廠裡的產品製造環境,傳統的整體生產規劃(Aggregate Production Planning, APP)可能使企業無法迎合客戶的需求以於失去其競爭優勢。使用混合整數規劃(Mixed Integer Programming, MIP)來建立靈活製造模式。在此模式中,使用0-1二元變數表達換線生產與缺貨後補。第三,本研究使用隨機最佳化(Stochastic Optimization)方法中的回溯最佳化(Retrospective Optimization, RO)來分析不確定性的情況。回溯最佳化方法主要分為兩個步驟:首先,透過模擬產生隨機參數;再者,以所產生的值為輸入常數,使用最佳化分析搜尋最佳解;重複執行上述二步驟直到符合收斂標準。最後,整合上述方法分析靈活製造環境下隨機供應鏈模式。
我們作一連串的實證設計包括供應鏈整體規劃模式、靈活製造模式、隨機的生產規劃與靈活製造環境下隨機供應鏈模式以驗證每個研究方法的可行性。從實證分析的結果我們得到以下幾點結論:第一、供應鏈整體規劃的競爭優勢在於能有效整合資源並以即時的方式持續地提供正確資訊給上下游的夥伴成員。第二、驗證了分析靈活製造模式的可行性。第三、藉由回溯最佳化方法可以求得各種隨機生產規劃的最佳解。第四、整合研究方法分析靈活製造環境下隨機供應鏈模式並求得最佳解。
本研究的貢獻除可提供供應鏈理論研究之參考外,於實務上亦能提供較多且符合實際情形之資訊以提供供應鏈設計與管理上的參考及應用。
Due to the gradually complicated and uncertain environment of Supply Chain (SC) and production manufacturing, how to analyze the operation of SC is a challenge for nowadays SC management to confront with. This research aims at analyzing the stochastic SC models under Agile Manufacturing (AM) environment.
First of all, the SC model is formulated by Linear Programming (LP), and then the best quantity of output among each node is figured out with the minimal total cost. Secondly, the agile manufacturing environment is considered, and the traditional Aggregate Production Planning (APP) may not satisfy customer’s needs so an enterprise may lose their competitive advantage. The AM model is established by Mixed Integer Linear Programming (MILP). This model utilizes 0-1 binary variables to handle changeover and backorder conditions. Thirdly, this research utilizes Retrospective Optimization (RO) of stochastic optimization to deal with uncertain factors. The main idea of RO is divided into two steps: first, the random parameters are produced through simulation; secondly, the produced values are served to be the input constants to search the best solution. Carry out two above-mentioned steps repeatedly till the convergent criterion is satisfied. Finally, we integrate above approaches to analyze the stochastic SC models under AM environment.
A succession of experimental examples, including overall SC planning models, AM models, stochastic production planning models and the stochastic SC models under AM environment, are designed in order to verify the feasibility of proposed approaches. The following conclusions are obtained: (1) the advantage of overall SC planning model is to integrate resources effectively and to provide correct and immediate information continuously to the related partners; (2) the feasibility of analyzing AM model is proved; (3) the optimal solutions of various stochastic production planning problems are obtained by RO; and (4) the optimal solutions can be obtained by the integrated approach to analyze the stochastic SC models under AM environment.
This research provides not only a reference for SC theorem, but also more accurate information for the further study and application on the field of SC.
目 錄
中文摘要 i
英文摘要 ii
謝辭 iv
目錄 v
表目錄 vii
圖目錄 viii
第一章 緒論 1
1.1研究動機與目的 2
1.2研究流程與論文架構 3

第二章 文獻探討 5
2.1靈活製造的相關研究 5
2.2供應鏈的相關研究 7
2.2.1供應鏈管理 8
2.2.2供應鏈模式 8
2.3隨機最佳化的相關研究 9

第三章 研究方法流程 11
3.1問題定義與符號說明 11
3.1.1問題定義 11
3.1.2符號說明 14
3.2研究方法 19
3.2.1供應鏈整體規劃模式 19
3.2.2靈活製造模式 24
3.2.3隨機最佳化問題 30
3.2.4靈活隨機供應鏈模式的建立與最佳化求解流程 33

第四章 實證研究與分析 35
4.1驗證供應鏈整體規劃的優勢 35
4.2驗證靈活製模式 43
4.3隨機的生產規劃模式 46
4.4靈活隨機供應鏈模式之實證分析 55
4.5實證結果小結 62

第五章 結論與後續研究建議 63
5.1 結論 63
5.2 後續研究建議 64

參考文獻 65
附錄 69
附錄A 隨機參數產生程式 69
附錄B 隨機的生產規劃模式 72
附錄C 成本最小化的最佳靈活生產規劃輸出檔 74
附錄D 靈活製造環境下的隨機供應鏈模式 81
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