跳到主要內容

臺灣博碩士論文加值系統

(3.231.230.177) 您好!臺灣時間:2021/07/27 13:54
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:陳宏尉
研究生(外文):Hung-WeiChen
論文名稱:開發一基於多階層整數規劃之LED最佳化配置系統
論文名稱(外文):Developing an Optimal LED Allocation System based on Multi-level Integer Programming
指導教授:鄭芳田鄭芳田引用關係
指導教授(外文):Fan-Tien Cheng
學位類別:碩士
校院名稱:國立成功大學
系所名稱:製造資訊與系統研究所碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:57
中文關鍵詞:多階層整數規劃最佳化配量邊料去化
外文關鍵詞:multi-level integer programmingoptimal allocationside bin reduction
相關次數:
  • 被引用被引用:0
  • 點閱點閱:261
  • 評分評分:
  • 下載下載:47
  • 收藏至我的研究室書目清單書目收藏:0
當應用LED於平面顯示器如燈條(Light Bar)之顯示時,由於LED在色度、亮度與電壓等三大特性上,產出呈常態分佈,一般依其特性區間以組合碼(BIN 碼)代表之。一燈條可從多組LED項目取一所組成,一LED項目可由不同BIN碼所組成,相同LED項目可共用不同BIN碼。因此,在庫存管制上,共用性高之LED BIN料易於使用,而共用程度低之邊BIN料庫存難以去化的問題。尤其LED的跌價損失快,對於多廠區生產時,如何達到跨廠區庫存管制的最佳化實為挑戰。
本研究基於多階層整數規劃法,開發一web化之多階層最佳化LED配置系統(Optimal LED Allocation System, OLAS),提供生產工單所需之領料配量,以達到LED全產全銷的目標。在計算效益上,本研究提出一基於現有庫存的簡化BIN控制矩陣法,可大幅減少最佳化運算所需之計算時間。在庫存管制上,依低共用性先用的原則,以多階層整數規劃求解,階層零採用從邊BIN碼先用,階層一僅限兩種BIN交錯使用之混打,階層二採混打加僅用單一BIN碼之單打。最後,階層三為補量以預防不良率損失等四大階層,以逐步去化邊餘BIN。此外,並提供多邊顯示的連板倍率限制,符合大尺寸對稱BIN顯示的需求。
結果顯示,就某面板大廠近一個月的使用情況而言,在計算效益上,應用所提之簡化控制矩陣法,可將所需處理之資料量縮減為原先之29%。在求解速度上,應用ILOG引擎求解,平均可於2分鐘內求解完成。在LED庫存管制上,應用四階層的最佳化配量,本系統可持續優先使用低共用性邊BIN碼量。由於LED的全產全銷,更可進一步降低指定LED BIN碼的採購成本。
When adopting LED as lighting source of light-bar for panel display, due to LED characteristics being normal distributed in chromaticity, illumination and voltage, a LED is characterized by BIN codes. In light-bar combination, a light-bar can be combined from one of several LED items; a LED item can be consisted from various BIN codes; the same LED item can use different BIN codes. Hence, high exchangeable BINs are easily used than low exchangeable BIN codes, a.k.a. side-BIN codes, such that a challenge is how to optimize cross-site inventory of side-BINs under fast inventory falling price loss.
This work based on multi-level integer programming method proposes an optimal LED allocation system (OLAS) to allocate LED quantities by work-order, while reaching the objectives of maximizing order fulfillment and minimizing side BINs. In computing efficiency, this work based available stock proposes a simplified bin Control Matrix method to significantly reduce required computation time for allocation optimization. In controlling inventory, according to low exchangeability first policy, the allocating policy by levels of OLAS are as follows: level zero is to use the side BIN codes; level one is to interlace two BIN codes for allocation; level two can allocate single or two interlaced BIN codes to order; and level three reserves extra quantities to prevent production loss. Additionally, this system can satisfy pallet constraints to meet requirements of symmetric BIN codes usages.
After applying OLAS to a panel display company for one month, the result shows that the required data can be reduced more than 71% than the original data amount by using the simplified Control Matrix method; the mean solving time is less than two minutes by utilizing ILOG engine; finally, first allocated the low exchangeable BIN code’s inventory after adopting the four-level integer programming model.
中文摘要
英文摘要
致謝
目錄 v
圖目錄 vii
表目錄 ix
符號定義表 x
第一章 緒論 1
1.1 研究背景 1
1.2 研究目的 5
1.3 論文架構 6
第二章 理論方法 7
2.1 文獻探討 7
2.1.1 最佳化方法-線性規劃 7
2.1.2 多階層線性規劃 7
2.1.3 結論 9
2.2 研究方法 10
2.2.1 系統架構 10
2.2.2 資料型態 11
2.2.3 系統模型 19
第三章 系統開發 26
3.1 系統分析 26
3.2 系統設計 29
3.2.1 OLAS系統流程 29
3.2.2 IBM ILOG Modeling 31
3.2.3 OLAS資料結構 33
3.2.4 MVC架構 35
3.2.5 循序圖 37
第四章 實驗與驗證 40
4.1 供需資料 40
4.2 結果分析 50
4.2.1 配置結果分析 50
4.2.2 配置時間分析 52
第五章 結論 54
5.1 結論 54
5.2 未來研究方向 55
參考文獻 56
附錄一 I
附錄二 III
[1]Display research, www.displaysearch.com, 2012.
[2]Linear programming. Available:http://zh.wikipedia.org .
[3]Vašek Chvátal, Linear programming, W. H. Freeman, 1983.
[4]A. Schrijver, “Theory of Linear and Integer Programming, John Wiley & Sons Ltd, Chichester, England, 1998.
[5]W. F. Bialas and M. H. Karwan, “Two-Level Linear programming, Management Science, Vol. 30, No. 8, pp. 1004-1020, 1984.
[6]W. F. Bialas and M. H. Karwan, “Multilevel Linear Programming, Industrial Engineering SUNY at Buffalo, Research Report, no. 78-1, 1978.
[7]Paul Shaw & Constraint Programming Group, IBM ILOG CP Optimizer CP-AI-OR 2009 Master class, 2009.
[8]H.-C. Yang, Y.-L. Chen, M.-H. Hung, and F.-T. Cheng, “Virtual Production Control System, Proc. of the IEEE Conference on Automation Science and Engineering, pp. 984-989, 2010.
[9]E. A. Demirtas and O. Ustun, “An integrated multi-objective decision making process for supplier selection and order allocation, Omega, vol. 36, no. 1, pp. 76-90, 2008.
[10]C. Kasemseta and V. Kachitvichyanukul, “Bi-level multi-objective mathematical model for job-shop scheduling: the application of Theory of Constraints, International Journal of Production Research, Vol. 48, Iss. 20, pp. 6137-6154, 2010.
[11]R. J. Kuo and Y. S. Han, “A hybrid of genetic algorithm and particle swarm optimization for solving bi-level linear programming problem– A case study on supply chain model, Applied Mathematical Modelling, vol.5, Iss. 8, pp. 3905-3917, 2011.
[12]Y.-H. Lin, J.-R. Shie, and C.-H. Tsai, “Using an artificial neural network prediction model to optimize work-in-process inventory level for wafer fabrication, Expert Systems with Applications, vol. 36, pp. 3421-3427, 2009.
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top