(44.192.112.123) 您好!臺灣時間:2021/02/28 06:40
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:::

我願授權國圖
: 
twitterline
研究生:陳記成
研究生(外文):Ji-Cheng Chen
論文名稱:供應鏈存貨模式在不確定性因素下的採購決策支援系統
論文名稱(外文):A Purchasing Decision Support System of Supply Chain Inventory Models under Uncertain Environment
指導教授:安揚龍邱垂昱邱垂昱引用關係
指導教授(外文):Yang-Long AnChui-Yu Chiu
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:生產系統工程與管理研究所
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2001
畢業學年度:89
語文別:中文
論文頁數:80
中文關鍵詞:供應鏈管理不確定性模糊理論類神經網路
外文關鍵詞:Supply Chain Managementuncertainfuzzyneural networks
相關次數:
  • 被引用被引用:17
  • 點閱點閱:467
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:133
  • 收藏至我的研究室書目清單書目收藏:6
供應鏈管理是企業最近相當重視的一個領域。就供應鏈管理而言,一向被是認為從原物料的供應,透過生產製造,到最終消費者手中的一個網路架構,其間也包含了各項相關資訊的傳遞。因此,為了去提升整體供應鏈的競爭力,企業不僅要整合內部的資源,也要減少外部不確定性因素的來源。然而在大部分存貨模式中,牽涉到環境中不確定性因素時,許多的注意力仍集中在顧客需求的機率模式上,直到最近的幾年,外部供應的不確定性才逐漸被重視。所以在供應鏈管理的外部環境中主要兩個不確定性來源,一個是來自顧客的需求,另一個則是來自外部原物料的供應。
要根除推行供應鏈管理時的不確定性因素是不可能的,但是適當策略的採行則可以降低受到不確定性因素的波及。所以本研究目的是解決供應鏈存貨在不確定性因素下的採購問題,並使建構出來之採購決策支援系統在不確定性因素發生時,能決定在供應鏈管理所規劃的時間範圍裡每次存貨管制的採購量。並期望透過公司在以往物料採購和管理的經驗,建構出一套結合模糊理論與類神經網路的採購決策支援系統。
經由實例的研究,實驗的結果顯示模糊類神經網路能有效的學習已存在的採購管理規則。除此之外,模糊類神經網路也能夠快速的處理大量的資料,所以採購人員可以定期的評核採購項目和採購決策,使決策在採購件的數量或品質發生變化時能做必要的修正,以讓企業能快速的反應市場的變化。並提供給較無經驗的採購人員一個快速的依循準則。
Supply chain management is an area that has recently received a great deal of attention in the business. Supply chain management is generally viewed as a network of actions linked by a material flow from suppliers through production to end customers and containing an information flow. To raise the competitiveness of a supply chain, enterprises not only integrate the internal resources but also decrease the sources of external uncertainty. In most of the inventory models that involve uncertainties in the environment, the attention has been focused on the probabilistic modeling of the customer demand side. Up until the recent years the uncertainties in the supply side have not received the amount of treatment they deserved. Two sources of uncertainty inherent in the external environment in which the supply chain operates were identified: customer demand and external supply of raw material.
It is impossible to root the sources of uncertainties out, while supply chain management is carried out. But adopting the suitable tactics can reduce the influences of uncertainties. The objective of this research is to solve the procurement problems with the uncertain factors of supply chain inventory, and to develop a decision support system, that would determine the order quantities of each inventory in the presence of uncertainty for the whole supply chain management during a finite time horizon. A decision support system integrating fuzzy theory and neural network by past experiences of material purchasing and managing is developed.
Through a real world case study, the experimental result shows that the fuzzy neural networks can learn the existing procurement management rules effectively. Besides, fuzzy neural network is capable of processing a lot of data quickly. A buyer can periodically review purchasing items as well as their procurement strategies and make necessary adjustments if quantity or quality of products altered. Therefore, the enterprises can react to market fluctuations faster. For those who do not have much experience in procurement, this model can provide a guideline to make the decisions.
中文摘要.......................................................i
Abstract......................................................ii
誌 謝.........................................................iv
目 次..........................................................v
表目錄.......................................................vii
圖目錄......................................................viii
第一章 緒論....................................................1
1.1 研究背景與動機............................................1
1.2 研究目的...................................................2
1.3 研究範圍與限制............................................3
1.4 研究流程...................................................4
第二章 文獻探討................................................6
2.1供應鏈管理..................................................6
2.1.1 供應鏈管理的定義.........................................6
2.1.2 供應鏈管理整體目標.......................................7
2.1.3 供應鏈管理中不確定性因素之探討...........................8
2.2存貨管理...................................................13
2.2.1 存貨的意義..............................................13
2.2.2 傳統存貨管理系統的理論..................................14
2.2.3 存貨政策................................................14
2.3模糊理論...................................................16
2.3.1 模糊集合................................................16
2.3.2 語意變數................................................18
2.3.3 模糊化與解模糊化........................................19
2.3.4 模糊邏輯推論模式........................................23
2.4類神經網路.................................................25
2.4.1 類神經網路的基本架構....................................26
2.4.2 類神經網路的分類........................................28
2.4.3 類神經網路的特性........................................29
2.4.4 倒傳遞神經網路..........................................29
2.5模糊類神經網路.............................................33
2.5.1 模糊類神經網路發展......................................33
2.5.2 模糊類神經網路基本架構..................................35
2.6決策支援系統...............................................35
2.7模糊德爾菲法...............................................37
第三章 研究方法...............................................38
3.1模式決策因子之建立.........................................38
3.2資料整理與建構.............................................40
3.3建構模糊類神經網路模型.....................................41
3.3.1 模糊類神經網路架構......................................42
3.3.2 模糊類神經網路演算法....................................45
3.3.3 網路學習過程............................................48
3.4決策支援系統之建構.........................................50
第四章 實例探討...............................................51
4.1資料收集...................................................51
4.1.1 採購作業因素的選取......................................51
4.1.2 問卷資料的進行方式......................................53
4.2模糊類神經網路模型建構.....................................54
4.2.1網路模型.................................................54
4.2.2網路架構初始加權值設定...................................56
4.3網路參數分析與設定.........................................56
4.4測試與評估.................................................59
4.4.1模糊類神經網路模式.......................................59
4.4.2複迴歸分析模式...........................................60
4.4.3模糊類神經網路與複迴歸模式的比較.........................62
4.5決策支援系統操作與功能.....................................63
第五章 結論與建議.............................................68
5.1結論.......................................................68
5.2研究貢獻...................................................69
5.3建議.......................................................69
參考文獻......................................................71
附 錄.........................................................76
[1]Petrovic, D., Roy, R., and Petrovic, R., “Modelling and Simulation of A Supply Chain in An Uncertain Environment”, European Journal of Operational Research (109), pp.299-309, 1998.
[2]Refik Gullu, Ebru Onol, and Nesim Erkip, “Analysis of an Inventory System Under Supply Uncertainty”, Int. J. Production Economics, pp.377-385, 1999.
[3]Thomas, D. J., and Griffin, P. M., ”Coordinated Supply Chain Management”, European Journal of Operational Research, Vol: 94, No.1, pp.1-15, 1996.
[4]Houlihan, J. B., ”International Supply Chain Management”, Proceedings of the 19th International Technical Conference of the British Production and Inventory Control Society, pp.101-110, 1984.
[5]Christopher, M., Logistics and Supply Chain Management: Strategies for Reducing Costs and Improving Services, (2th ed., Financial Times/Pitman, London, 1992).
[6]Fisher, M. L., “What is the Right Supply Chain for Your Products?”, Harvard Business Review, pp105-116, 1997/3-4.
[7]蘇雄義,“供應鏈管理國內外發展”,資訊與電腦雜誌,pp.55-58, 1999/8.
[8]蔡翠旭 譯,Charles C. Poirier, and Stephen E. Reiter 著,強勢供應鏈 (Supply Chain Optimization),(書華出版事業有限公司 ,民國87年)。
[9]Larson, Paul D., Rogers, Dale S., “Supply Chain Management: Definition, Growth and Approaches”, Journal of Marketing Theory and Practice, pp.1-5, 1999.
[10]Strader, T. J., Lin, F. R., and Shaw, M. J., “The Impact of Information Sharing on Order Fulfillment in Divergent Differentiation Supply Chains”, Journal of Global Information Management, Vol: 7, No.1, pp.16-24, 1998.
[11]Whybark, D. C., and William, J. G., “Material Requirement Planning under Uncertainty”, Decision Science, Vol: 7, No.7, pp.595-606, 1976.
[12]Lee, H. L., Billington, C., ”Material Management in De-centralized Supply Chains,” Operations Research, Vol: 41, No.5, pp.835-847, 1993.
[13]Davis, T., ”Effective Supply Chain Management”, Solan Management Review, pp.35-46, 1993.
[14]Kalpakam, S., Sapna, K. P., “A Lost Sales Inventory System with Supply Uncertainty”, Computers & Mathematics with Applications, Vol: 33, February, pp.81-93, 1997.
[15]姜智偉,快速回應系統之二階層多次折扣存貨模式,國立中央大學工業管理研究所碩士論文,民國88年。
[16]Petrovic, D., Roy, R., and Petrovic, R, “Supply Chain Modelling Using Fuzzy Sets,” International Journal of Production Economics, Vol: 59, pp.443-453, 1999.
[17]羅國正,推行供應鏈管理之不確定性因素及其因應策略之研究-以台灣資訊電子業為例,國立政治大學資訊管理學系碩士學位論文,民國89年。
[18]侯大偉,運用模糊類神經網路以建置訂單式存貨管理預警系統,大葉大學資訊管理研究所碩士論文,民國88年。
[19]蘇雄義 著,企業物流導論,(華泰文化事業公司,民國87年出版)。
[20]張煦逸,進銷存系統之建立與實務之應用,國立中興大學企業管理研究所碩士論文,民國81年。
[21]William J. Stevenson著,傅和彥 譯,生產管理,(前程企業管理有限公司,民國87年出版)。
[22]林宜憶,BPR與物流中心資訊系統之應用模式,國立中山大學資訊管理研究所碩士論文,民國85年。
[23]Zadeh, L.A., “The Concept of A Linguistic Variable and Its Application to Approximate Reasoning: Parts 1-3”, Inf. Sci. 8, 1965.
[24]Shan-Huo, Chen, Chih-Hsun, Hsieh, “Optimization of Fuzzy Simple Inventory Models”, IEEE International Fuzzy Systems Conference Proceedings, pp240-244, August 1999.
[25]Paolo, Dadone and Hugh, F., “Genetic-Based Fuzzy Adaptation”, The 1998 IEEE International Conference, Vol: 2, pp.1094-1099, 1998.
[26]Huey-Ming, Lee, Jing-Shing, Yao, “Economic Production Quantity for Fuzzy Demand Quantity and Fuzzy Production Quantity”, European Journal of Operational Research, Vol: 109, pp.203-211, 1998.
[27]Hiroaki, Ishii, Tutomu, Konno, “A Stochastic Inventory Problem with Fuzzy Shortage Cost”, European Journal of Operational Research, Vol: 106, pp.90-94, 1998.
[28]Roy, T. K., Maiti, M., “Multi-objective Inventory Models of Deteriorating Items with Some Constraints in a Fuzzy environment”, Computers & Operations Research, Vol: 25,No.12, pp.1085-1095, 1998.
[29]劉天祥與佟中仁 譯,向殿政男 著,FUZZY理論入門,(中國生產力中心,民國79年)。
[30]王文俊,認識FUZZY,(全華科技圖書股份有限公司,民國86年)。
[31]陳建安,整合類神經網路與遺傳演算法為輔之模糊神經網路於智慧型訂單選取之應用,國立台北科技大學生產系統工程與管理研究所碩士學位論文,民國89年。
[32]Zadeh, L. A., “A rationale for fuzzy control”, J. Dyn. Syst. Meas. Control Trans. ASME, Vol: 94, pp.3-4, 1972.
[33]Saaty, T., “Measuring the fuzziness of sets”, J. Cybern., Vol: 4, No.4, pp.53-61, 1974.
[34]Hadipriono, F., Sun, K., “Angular fuzzy set models for linguistic values”, Civ. Eng. Syst., Vol: 7, No.3, pp.148-156, 1990.
[35]Takagi, H., Hayashi, I., “NN-driven fuzzy reasoning”, Int. J. Approximate Reasoning, Vol: 5, pp.191-212, 1991.
[36]Forrest, S., “Genetic algorithms: principles of natural selection applied to computation”, Science, Vol: 261, pp.872-878, 1993.
[37]Chin-teng, Lin, C. S. George, Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems, (Prentice Hall International, Inc., 1999).
[38]葉怡成,類神經網路模式應用與實作,(儒林圖書有限公司,民國82年)。
[39]Shibata T., Fukuda T., Kosuge, K., and Arai, F., “Skill Based Control by using Fuzzy Neural Network For Hierachical Intelligent Control”, Proceedings of 1992 International Joint Conference on Neural Networks, Vol: 2, pp.81-86, 1992.
[40]Khan E., NeuFuz: An Intelligent Combination of Fuzzy Logic with Neural Nets, Proceedings of 1993 International Joint Conference on Neural Networks, pp.2945-2950, 1993.
[41]Chin-Teng, Lin, C.S. George, Lee, “Neural-Network-based Fuzzy Logic Control and Decision system”, IEEE Transactions on Computers, pp.1320-1336, Dec. 1991.
[42]Kasabov, Nikola K., “Learning Fuzzy Rules and Approximate Reasoning in Fuzzy Neural Networks and Hybrid Systems”, Fuzzy Sets and Systems, Vol: 82, pp.135-149, 1996.
[43]Lefteri, H. Tsoukalas, Robert, E. Uhrig, Fuzzy And Neural Approaches in Engineering, (A Wiley-Interscience Publication, 1997).
[44]單智君,The Structure Design and Learning Methodology for A Fuzzy-rule-based Neural Network,國立交通大學資訊工程研究所博士論文,民國83年。
[45]楊明峰,應用模糊理論建構彈性製造系統配置規劃之研究,國立台北科技大學生產系統工程與管理研究所碩士學位論文,民國88年。
[46]Ishikawa, A., Amagasa, M., Tomiqawa, G., Tatsuta, R. and Mieno, H., “The Max-Min Delphi Method and Fuzzy Delphi Method via Fuzzy Integration”, Fuzzy Sets and Systems, Vol: 55, pp.241-253, 1993.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔