周雍強952221E002268MY3(第1å¹&acut

周雍強952221E002268MY3(第1å¹&acut

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Unformatted text preview: 行政院國家科學委員會補助專題研究計畫 □成果報告 期中進度報告 (計畫名稱) 總計畫: 生產網絡:不確地性、風險與協同控制 子計畫二: 半導體供應鏈的協同產能與生產控制 計畫類別:□ 個別型計畫 整合型計畫 計畫編號:NSC 95-2221-E-002-268-MY3 執行期間: 95 年 8 月 1 日至 96 年 7 月 計畫主持人: 周雍強 共同主持人: 計畫參與人員: 黃馨儀、張書銘 成果報告類型(依經費核定清單規定繳交): 31 日 精簡報告 □完整報告 本成果報告包括以下應繳交之附件: □赴國外出差或研習心得報告一份 □赴大陸地區出差或研習心得報告一份 □出席國際學術會議心得報告及發表之論文各一份 □國際合作研究計畫國外研究報告書一份 處理方式:除產學合作研究計畫、提升產業技術及人才培育研究計畫、 列管計畫及下列情形者外,得立即公開查詢 □涉及專利或其他智慧財產權,□一年□二年後可公開查詢 執行單位: 國立台灣大學工業工程所 中 華 民 國 96 年 5月 31 日 1 行政院國家科學委員會專題研究計畫期中報告 子計畫(二)半導體供應鏈的協同產能與生產控制(1/3) 計畫編號: NSC 95-2221-E-002-268-MY3 執行期限: 95 年 8 月 1 日至 96 年 7 月 31 日 ychou@ntu.edu.tw 主持人: 周雍強 國立台灣大學工業工程所 一、中英文摘要 高科技製造的供應鏈在製程與產品研發以 及產品需求各方面有極高的不確定性,這些不 確定性所引發的動態事件對供應鏈的工作負載 和產能需求往往造成嚴重的變動,因此如何制 訂對策是企業關切的問題。本計畫的目的是針 對動態事件發展協同生產控制的機制與方法。 本計畫以工廠為供應鏈的基本生產單位,建構 跨單位的動態控制機制與方法。第一年的研究 已推導兩節點的動態系統模式。本報告摘錄模 式要點並以數值運算說明其應用方式。 二、計畫緣由與目的 生產網絡是近年來興起的複雜系統,具有 幾個顯著的特性:不確定性很高、網絡運作動 態變化、非集權式生產組織。總計畫對生產網 絡控制的研究構想如圖(1)所示,可分為規 劃層與基盤結構層的研究 在規劃層的橫向結 。 構內,主要的流程是材料規劃、產能規劃、需 求規劃、生產協調。 coordination materials 規劃層 product supply and demand Node N ode 1 Node 2 關鍵詞:供應鏈控制、動態系統、生產函數 Abstract High-tech manufacturing supply chains are full of uncertainties in process and product engineering and in product demand. Dynamic events that originate from these sources of uncertainty have the potential to interrupt the supply. Designing policies to mitigate the effect of disrupting events is a keen concern of all companies. The objective of this project is to develop collaborative control mechanism and methods for production control in the supply chains. Treating factories as the basic production units, this project will construct dynamic control mechanism and methods for a chain of production units. Keywords: supply chain control, dynamic systems, production function capacity capacity (a) 橫向結構 基盤結構層 Mechanism, policy design, performance coordination 規劃層 materials capacity product supply (b) 縱向結構 圖 1: 生產網絡控制之主要物件與結構 供應鏈有多重績效指標,如週期時間、風險、 成本、服務水準等,這些績效受各種流程共同影 響,因此流程不能分離處理,而必須有所統整。 在總計畫探討生產網絡不確定性 風險與協同控 、 制的架構之中、以及「駕馭」生產網絡的大目標 之下 本子計畫的任務是產能與生產決策的協同 , 控制 本計畫以供應鏈動態事件與其控制方法為 。 對象,而不是經常性的作業層次的問題。 2 生網絡的運作應考量到不確定性因素 所可能導致的狀況,不確定事件可分為三類 (Gaonkar and Viswanadham, 2004): (1) 生產系統參數的偏移(deviation) (2) 干擾(disruption) (3) 不可抗力的災難(disasters) 第一類的不確定性可以透過生產網絡的設 計與調整,來防範可能發生的風險。本計畫 所關切的動態事件屬於對供應鏈產生干擾 的第二類。 只限於 M/M/1、M/M/c、M/Ek/1、M/G/1 等 較為簡單的系統。分析更為複雜系統的行為 必須藉助不亦使用的模擬。後設模式(meta models)乃是介於兩者之間的第三類模式。本 計畫以生產函數的後設模式為節點行為,從 產出函數的觀點,建構供應鏈的動態控制模 式(圖三)。 研究目的 本計畫的目的是研究並發展供應鏈的協同 生產控制機制與方法。供應鏈的複雜度與動態 性都遠超過工廠系統,本計畫將有助於提升對 供應鏈系統的掌控能力。 圖三:供應鏈動態控制模式 本計畫第一年已經完成供應鏈動態控 制模式的理論。成果將在 International Journal of Production Research 刊出(Huang Chou and Chang, accepted for publication)。模 式要點、數值運算以及應用方式摘錄如下。 三、研究方法 本計畫對如圖(二)所示的控制機制進 行基本研究。供應鏈將被視為一個系統,組成 元素為工廠。有兩個必要的研究主題:探討各 節點工廠的生產行為(production function)、建 構兩節點生產決策的互動機制。 Control 四、結論與成果 The sources of uncertainty exist in supply nodes, production processes, and demand and distribution nodes. In this study, we first define a classification for risk and uncertainty (Figure 4). customer business mfg mfg supply chain performance of the supply eng 圖二: 供應鏈控制的目的 生產單位對所輸入的需求資訊,作出物 料、產能、排程各項生產決策,經生產運行 後生成產品,而整個過程的績效依據設定的 指標來衡量。所謂生產行為係指需求資訊、 產能與生產績效之間的關係,換言之,生產 單位內的細部決策並非主要關懷,主要關懷 在於生產單位對輸入資訊作出反應後所呈 現於外的績效。 探討生產行為模式的論文可分為三類: 解析模式、模擬模式、後設模式。解析模式 的建構以排隊理論為主,但是文獻上的成果 Figure 4: Uncertainty factors, exposure and risk A dynamic system model A dynamic system model of Figure 3 can be represented by the following equations. & W1 (t ) = r1 (t ) − Ω1 (t ) & W (t ) = Ω (t ) − Ω (t ) 2 1 2 r1 (t ) = d (t ) + E (t ) & I (t ) = Ω (t ) − d (t ) + φ (t ) 2 Ωk (t ) = akα k 2 − ekα k k=1,2 The input to the manufacturing chain is a planned demand process d(t) and a shock input. Release rate decision r1(t) is made based on d(t). Each node (k=1 or 2) has a capacity Ck, a state variable of work-in-process Wk, and an output rate Ωk(t). A general form of the objective function is expressed 3 as V* = max u ∫ [∑ − gk (α k (t )) − hk (Wk (t ))] − m0 Sdt k an optimal path exists, it can be concluded that the shock input will not degrade the quality of supply services. where g k and hk are continuous and differentiable cost functions and m0 is a parameter. Analytical solutions have been derived: e ( m − m2 )(t − T ) − c1 α1* (t ) = 1 1 2a1 ( m1 − m2 )(t − T ) + 2b1 α 2* (t ) = e2m2 (t − T ) − c2 2a2m2 (t − T ) + 2b2 s − ET E−E s − ET E−E (a) Control variables ⎧ ⎪ E t < (m1T + k ) /( m1 − m0 ) = ⎪ E *(t ) = ⎨ ⎪ E t > (m T + k ) /(m − m ) = 1 1 0 ⎪ ⎩ where k = ( s − ET )(m1 − m0 ) − m1T , (E − E) (b) State variables Figure 5. Contingent capacity and state paths E and E denotes the lower and upper limits of the domain of E(t). A numerical example is included below to illustrate how this model can be applied. Numerical examples Two manufacturing shops have a capacity to meet nominal demand and d =0.0. The production functions, in which the throughput rate has been normalized by capacity, are The upper limit of flexible capacity is α k =.04 for k=1,2. %% Assume initial inventory [ w1 , w2 ] = [0.13,0.08] . Let s = 0.12, the planning horizon T=3, and the domain of E(t) is set as [ E , E ] = [0.035. 0.045]. The objective function is formulated as Max Ω1 = −50.562 ⋅ α 12 + 4.045 ⋅ α 1 2 Ω 2 = −51.711 ⋅ α 2 + 4.137 ⋅ α 2 ∫ ∑ [Ω T 0 k =1 2 k (t ) − α k (t )] − 4W1 (t ) − W2 (t ) − 6 Sdt In this project, a dynamic system model of supply chains is described which integrates the production functions of production units with a shock demand input process. It addresses a type of supply chain problems in which dynamic events can not be satisfactorily modelled by probability distribution. It can be applied to proactively manage dynamic, disrupting events by assess the impacts of the events and, if necessary, by activating contingency plans for their mitigation. Given an unordinary event of demand shock, this model can be used to determine if the shock can be absorbed by the manufacturing chains, without degrading its fulfilment services. If the effect of a class of dynamic events can be mitigated, they will no longer be dynamic events. Instead, their remnant uncertainty will be at the level of deviations. If that happened, then we could say that supply chain control has been improved. The optimal control paths and resultant state path are: 文獻: [1] Gaonkar, R., and N. Viswanadham, “A conceptual and Analytical Framework for the Management of Risk in Supply Chains,” Proc. 2004 IEEE International Conference on Robotics & Automation, 2004, 2699-2704. [2] Huang, Hsing-Yi, Yon-Chun Chou, and Sherman Chang, “A Dynamic System Model for Proactive Control of Dynamic Events in Full-Load States of Manufacturing Chains, International Journal of Production Research, to appear. 4 1 α1 * (t ) = 0.04 − 1011.240 − 303.372t 1.000 α 2* (t ) = 0.04 − 413.688 − 103.422t ⎧0.045 t < 1.5 E *(t ) = ⎨ ⎩0.035 t > 1.5 S (t ) = 0.12 − 0.045 min(t ,1.5) − 0.035 max(t − 1.5, 0) The optimal control paths and the resultant state variables are plotted in the following figure. Since ...
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This note was uploaded on 11/27/2009 for the course IM MA420 taught by Professor Mar,lee during the Spring '09 term at National Taiwan University.

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