進階搜尋


下載電子全文  
系統識別號 U0026-1608201400482700
論文名稱(中文) 太陽能矽晶片之採購選擇模式
論文名稱(英文) The procurement selection models of solar silicon wafer
校院名稱 成功大學
系所名稱(中) 工業與資訊管理學系碩士在職專班
系所名稱(英) Department of Industrial and Information Management (on the job class)
學年度 102
學期 2
出版年 103
研究生(中文) 簡振安
研究生(英文) Cheng-An Chien
學號 R37011320
學位類別 碩士
語文別 中文
論文頁數 69頁
口試委員 指導教授-陳梁軒
口試委員-王泰裕
口試委員-謝中奇
中文關鍵字 太陽能矽晶片採購  AHP層級分析法  模糊TOPSIS 
英文關鍵字 Solar silicon wafer procurement  AHP  Fuzzy TOPSIS 
學科別分類
中文摘要 由於替代能源需求與環保議題的重視,太陽能產業近年備受全球關注,市場亦呈現高度成長,刺激各國廠商積極投入,各先進國家政府紛紛提供多項補貼政策,以促進替代性能源產品之研究與相關產業之發展。我國經濟部能源局於2012年所公告發布的「經濟部太陽光電發電設備競標作業要點」,成立「陽光屋頂百萬座」專案,此計畫是規劃在2030年之前推廣太陽能發電系統設置量到達6,200MW,目標是要建立我國太陽能應用的完善環境,積極推動太陽光電的發電裝置,推動策略是採取逐步擴大、先安裝屋頂後應用於地面的方式,以穩健成長跟負責的態度來帶領我國太陽能產業,使之能夠永續的發展,讓該方案成為台灣發展綠色能源產業的重要方向,近幾年有許多的企業與人才投入太陽能電池產業,因此也產生了許多太陽能電池產業所必須面臨之決策問題。本研究將探討與分析太陽能電池產業鏈中的矽晶片採購,研究方法會先透過層級分析法(Analytical hierarchy process, AHP)來將影響太陽能矽晶片採購評估的因子,以相互比較的方式求出各準則權重,之後由模糊TOPSIS法(Fuzzy Technique for Order Preference by Similarity to Ideal Solution Method)來分析受評估廠商對應於理想解之相對近似程度,最後把太陽能晶片的售價滿意度與最終發電瓦數這些重要指標納入考量,透過整體指標來找出最佳的太陽能矽晶片採購選擇方案。
英文摘要 「英文延伸摘要」SUMMARY
This study will explore the purchasing decisions of silicon wafers in the solar industry. Two targets to be met are as follows: a. using the multi-criteria decision analysis model to find the most competitive and efficient silicon wafers; b. setting up an evaluation process to determine the suitable standard for silicon wafer procurement.
This study will first identify the criterion which will affect the procurement assessment and be weighed throughout the Analytical hierarchy process (AHP), then the relative closeness degree of manufacturers by Fuzzy TOPSIS (Fuzzy Technique for Order Preference by Similarity to Ideal Solution Method) will be used for analysis.
This analysis will identify the best selection model for the procurement of solar silicon wafer by Fuzzy TOPSIS and also will consider the 2 of important indices (power wattage of silicon wafer and price satisfaction).
Key words: Solar silicon wafer procurement, AHP, Fuzzy TOPSIS
INTRODUCTION
Because of recent increasing focus on environmental issues, the solar industry is expanding rapidly. Most cell manufactures have now increased their competitiveness by cost controls and increased efficiency. This study will be based the on procurement model, to identify the best suitable model for silicon wafer procurement.
Vendor selection is one kind of non-systematic and complicated multi-criteria problem, which is usually divided into two categories: MADM (multiple attribute decision making) and MODM (multiple objective decision making).Based on the nature of problem, this study will focus on MADM (multiple attribute decision making). According to the literature, we know that quality, delivery date, price and customer service are the most common evaluation criterion.
Purchase assessment model introduction:
1.To simplify the complicated problem into hierarchy system via cross-compare the elements by related experts form several pairwise comparison matrixes, and choose the highest weight for the best selection.
2.Holding meetings for related experts for them to put forward possible solutions.
3.The closest from the positive fuzzy ideal solution and the furthest from the negative fuzzy ideal solution is the best method.
4.Create a model framework of this study is to analyze the study result by proportion matching, sensitivity and also to combine 2 indices (power wattage of solar cell and price satisfaction) of solar cell industry.
The result shows that combining fuzzy TOPSIS and price satisfaction index will be more appropriate method.
MATERIALS AND METHODS
The purchasing models in this study are followings:
Model 1: Using the Analytic Hierarchy Process (AHP) method to evaluate the vendor;
Model 2: Formula of solar silicon wafer purchase estimation = solar silicon wafer satisfaction / actual power wattage of solar silicon wafer;
Model 3: Formula of solar cell purchase estimation = solar silicon wafer satisfaction / price satisfaction.
Satisfaction degree of silicon wafer: Calculating the relative degree of closeness (1-Ci*) between suppliers and positive fuzzy ideal solution by Fuzzy TOPSIS to represent the silicon wafer satisfaction.
Satisfaction degree of price: Changing the actual price to degree of membership means that the acceptable price of customers will be showed as degree of membership formation.
RESULTS AND DISCUSSION
This study will apply the purchase behavior of solar cell manufacturers M into the model to verify the feasibility.
Model 1. Supplier selection by AHP
Dividing the procurement factors into four dimensions and identifying the secondly guidelines:
●Cost: solar cell price, inspection cost
●Quality: incoming material quality, technical capacity
●Schedule: the speed of delivery, delivery performnce
●Management: attitude of customer service, cooperation impression, communication
We compared 4 suppliers with these 9 indexes via Consistency Ratio≦0.1 and got the result as C2 > C1 > C3 > C4.
Model 2. Formula of silicon wafer purchase estimation = solar silicon wafer satisfaction / actual power wattage of solar silicon wafer
1. Calculate the relative degree of closeness (1-Ci*) between suppliers and positive fuzzy ideal solution by Fuzzy TOPSIS to represent the silicon wafer satisfaction.
2. Power wattage is based on actual power efficiency of each supplier.
Result: C2 > C3 > C4 > C1
Model 3: Formula of solar silicon wafer purchase estimation = solar silicon wafer satisfaction / price satisfaction
Statistical the acceptant prices of power wattage from customers and render the price satisfaction by degree of membership.
Result: C2 > C1 > C4 > C3
CONCLUSION
Based on these 3 kinds of results, we can find that model 3 has high degree of comprehensiveness and can be applied into the selection of solar wafer. Recommendations for future research:
1.The new assessment program will cause reversal problem for suppliers, so this study will re-sort suppliers before the purchase, and then will use the estimated re-sorting result as purchasing basis; if any other researchers want to use this model into other industries, it is recommended that the reversal problem of sorting should be considered.
2.TOPSIS analysis requires prior assessing of the criteria to avoid influence from individuals.
3.If this study will be used into other industries, the key measure factors should be found first, and set up the suitable assessment model.
論文目次 中英文摘要 I
誌謝 VI
目錄 VII
表目錄 IX
圖目錄 X
第一章 緒論 1
第一節 研究背景及動機 1
第二節 研究目的 2
第三節 研究架構及流程 2
第四節 研究範圍及對象 3
第二章 文獻探討 4
第一節 太陽能電池介紹 4
第二節 供應鏈管理 7
第三節 評估準則與評選方法 10
第四節 模糊理論 21
第五節 小結 28
第三章 研究方法 30
第一節 問題描述 30
第二節 模式建構流程 31
第四章 數值案例分析 44
第一節 層別分析法(AHP)評選供應商 44
第二節 本研究模式之案例演算 51
第三節 研究結果比較 59
第五章 結論與建議 62
第一節 結論 62
第二節 未來研究方向 63
參考文獻 64
參考文獻 中文部分
王雅萍(民93),模糊多屬性決策應用於政府採購法最有利標評選之研究,義守大學碩士論文。
李俊佳(民92),網路學習系統評估模式之研究-模糊多屬性決策之應用,中原大學資訊管理學系碩士論文。
孫嘉鴻 (民89),會計資訊應用於共同基金經理人擇股決策之研究,國立政治大學會計學研究所碩士論文。
張自立 (民90),資料包絡分析及模糊多屬性決策應用於綜合評估國力之研究,國防管理學院資源管理研究所碩士論文。
張志向(1997),應用模糊理論於中小企業信用評等表改善建立之研究,義守大學管理科學研究所碩士論文
張紹勳 (民101),模糊多準則評估法與統計,台北:五南書局
梁添富 (民88),物料管理,頁10-27,台北:育友圖書公司
陶治中、劉文龍 (民97),多準則評估法應用於都市交通現場設備之無線通訊網路方案評選,運輸計劃季刊,頁 39 -78
黃惠民、謝志光(民89),物料管理與供應鏈導論,頁2-29,台北:滄海書局
鄧振源 (民101),多準則決策分析-方法與應用,台北:鼎茂圖書

西文部分
Beamon, B.M. (1999), “Measuring Supply Chain Performance,” International Journal of Operations & Production Management, 19(3), 275-92.
Buckley, J.J. (1985), “Fuzzy hierarchical analysis,” Fuzzy Sets and Systems, 17, 343-350.
Charnes, A., & Cooper, W.W. (1985), “Preface topics in Data Envelopment Analysis,” Annals of Operations Research, Vol.2, pp. 59-94.
Charnes, A., Cooper, W.W., & Golany, B. (1985), “A Developmental Study of Data Envelopment Analysis in Measuring The Maintenance Units in the U.S. Air Forces,” The Annals of Operations Research, 2, 95-112.
Chen, C.T. (2000), “Extension of the TOPSIS for group decision-making under fuzzy environment,” Fuzzy Sets and Systems, 114(1), 1-9.
Choi, T.Y., & Hartley, J.L. (1996), “An exploration of Supplier Selection Practices Across the Supply Chain,” Journal of Operation Management, 14, 333-343.
Churchman, & Ackoff, R.L. (1954), “An approximate measure of value,” Journal of the Operations Research Society of America, 2(2), 172-187.
Cooper, M.C., & Ellram, L.M. (1993), “Characteristics of Supply Chain Management and the Implication for Purchasing and Logistics Strategy,” The International Journal of Logistics Management, 4(2), 13-24.
Deng, H., Yeh, C.H., & Willis, R.J. (2000), “ Inter-company comparison using modified TOPSIS with objective weights,” Computers & Operations Research, 27(10), 963-973.
Dickson, G.W. (1966), “An analysis of supplier selection system and decision,” Journal of Purchasing, 2(1), 5-17.
Ellram, L.M. (1991), “Supply chain management,” International Journal of Physical Distribution and Logistics Management, 21(1), 13-33.
Evans, R.H. (1982), “Product Involvement and Industrial Buying,” Journal of Purchasing and Materials Management, 18(2), Summer, 23-28.
Farmer, T.A. (1987), “Testing the Robustness of Multi attribute Utility Theory in An Applied Setting,” Decision Sciences, 18(2) , 178-193.
Fishburn (1976), “Utility independence on subsets of product sets,” Operations Research, 24(2), 245-255.
Ganeshan, R. (1999), “Managing supply chain inventories:A multiple retailer, one warehouse, multiple supplier model,” International Journal of Production Economics, 59, 341-354.
Houlihan, J.B. (1987), “International Supply Chain Management,” International Journal of Physical Distribution and Materials Management, 17(2), 51-66.
Hwang, C.L., & Yoon, K. (1981), Multiple Attributes Decision Making Method and Application, Springer, Berlin Heidelberg.
Lewin, A.Y., & Minton, J.W. (1986), “Determining Organization Effectiveness: Another Look and an Agenda for Research Management Science,” 32(5), 518~538.
Liang, G.S. (1999), “Fuzzy MCDM based on ideal and anti-ideal concepts,” European Journal of Operational Research, 112, 682-691.
Murray, (1985), “A Pilot Study of Fuzzy Set Modification of Delphi,” Human Systems Management, 76-80.
Narasimhan, R. (1983), “An Analytical Approach to Supplier Selection,” Journal of Purchasing and Materials Management, 19(4), 27-32.
Negi, D.S. (1989), Fuzzy Analysis and Optimization. Kansas State University: Ph. D. Thesis, Department of Industrial Engineering.
Ross, D.F. (1997), Competing Through Supply Chain Management: Creating Market-Winning Strategies Through Supply Chain Partnerships, London: Chapman & Hall.
Saaty, T.L. (1971), “On polynomials and crossing numbers of complete graphs,” Journal of Combinatorial Theory, 10 (2), 183-184.
Saaty, T.L. (1996), Decision Making with Dependence & Feedback: The Analytic Network.
Satty, T.L. (1977), “A Scaling Method for Priorities in Hierarchical Structure,” Journal of Mathematical Psychology, 15(3), 234-281.
Saunders, M.J. (1995), “Chains, Pipelines, Networks and Value Stream: the Role, Nature and Value of Such Metaphors in Forming Perceptions of the Task of Purchasing and Supply Management,” First Worldwide Research Symposium on Purchasing and Supply Chain Management, 476-485.
Shih, H.S., Shyur, H.J., & Lee, E.S. (2007), “An extension of TOPSIS for group decision making,” Mathematical and Computer Modelling, 45(7-8), 801-813.
Sinna, M.A., & Amer, A.H. (2005), “Extensions of TOPSIS for multi-objective large-scale nonlinear programming problems,” Applied Mathematics and Computation, 162(1), 243-256.
Stefan, M. (2003), “Multiple- Supplier Inventory Models in Supply Chain Management: A Review,” International Journal of Production Research, 81-82, 265-279.
Stevens, G.C. (1989), “Integrating the Supply Chain,” International Journal of Physical Distribution and Materials Management, 19(8), 3-8.
Tagaras, G., & Lee, H.L. (1996), ”Economic Models for Vender Evaluation with Quality Cost Analysis,” Management Science, 42(11), 1531-1543.
Thomas, D.J., & Griffin, P.M. (1996), ”Coordinated Supply Chain Management,” European Journal of Operational Research, 94(1), 1-15.
Thompson, K.N. (1990), “Vendor profile analysis,” Journal of Purchasing and Materials Management, 26(4), 11-18.
Thompson, K.N. (1991), “Scaling evaluative criteria and supplier performance estimates in weighted point pre-purchase decision models,” International Journal of Purchasing and Materials Management, 27(1), 27-36.
Timmerman, E. (1986), “An approach to vendor performance evaluation,” Journal of Purchasing and Materials Management, 22(4), 2-8.
Turner, J.R. (1993), “Integrated Supply chain Management: What’s Wrong With This Picture ? ,” Industrial Engineering, 25(12), 52-55.
Weber, C.A., & Desai, A. (1996), “Determination of paths to vendor market efficiency using parallel coordinates representation: a negotiation tool for buyers,” European Journal of Operational Research, 90(1), 142-155.
Weber, C.L., & Benton, W.C. (1991), “Vendor selection criteria and Methods,” European Journal of Operational Research, 50(1), 2-18.
Willis, T.H., & Houston C.R. (1990), “Vendor requirements and evaluation in a just-in-time environment,” International Journal of Operations and Production Management, 10(4), 41-50.
Wilson, E.J. (1994), “The Relative Importance of Supplier Selection Criteria: A Revies and Update,” International journal of Purchasing and Materials Management, 30(3), 35-41.
Youssef, M.A., Zairi, M., & Mohanty, B. (1996), “Supplier selection in an advanced manufacturing technology environment: an optimization model,” Benchmarking for Quality Management & Technology, 3(4), 60-72.
Zadeh, L.A. (1965), “Fuzzy sets,” Information and Control, (8), 338-353
Zadeh, L.A. (1975), “The concept of a linguistic variable and its application to approximate reasoning–I,”Information sciences, 8(3), 199–249.
Zanakis, S.H., Solomon, A., Wishart, N., & Dubilsh, S. (1998), “Multi-Attribute Decision Making: A Simulation Comparison of Select Methods,” European Journal of Operational Research, 107, 507-529.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2019-08-27起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2019-08-27起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw