簡易檢索 / 詳目顯示

回結果列表
研究生： 張耀輝
Yao-hui Chang
論文名稱： 整合性模糊群體決策方法應用於供應評選之研究
A Study on Applying Integrated Fuzzy Group Decision Approaches for Supply Evaluation and Selection
指導教授： 周碩彥
Shuo-yan Chou
口試委員: 王泰裕
Tai-yue Wang
王福琨
Fu-kwun Wang
陳振明
Jen-ming Chen
張聖麟
Sheng-lin Chang
謝光進
Kong-king Shieh
鐘崑仁
Kun-jen Chung
學位類別： 博士
Doctor
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2007
畢業學年度： 95
語文別： 英文
論文頁數： 97
中文關鍵詞： 供應評選 、模糊因素評分系統 、模糊簡單加權評分系統 、模糊簡單多屬性評分技術 、群體決策 、供應鏈管理
外文關鍵詞： Supply evaluation and selection, Fuzzy factor rating system, Fuzzy simple additive weighting system, Fuzzy simple multi-attribute rating technique, Group decision-making, Supply chain management
相關次數： 點閱：400  下載：9 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 本論文提出三個整合性模糊群體決策方法以解決在模糊同質/異質群體決策環境下的供應評選問題。在這三個方法裡，模糊因素評分系統(FFRS)與模糊簡單加權評分系統(FSAWS)係整合模糊集合理論(FST)與因素評分系統(FRS)與/或簡單加權評分法(SAW)用以解決設施位址選擇的問題。另外藉由整合模糊集合理論(FST)與簡單多屬性評分技術(SMART)提出模糊簡單多屬性評分技術(fuzzy SMART)用以解決供應商評選的問題。在FFRS中的個別準則的權重與關於各準則對不同位址方案的評分係以語意變數加以評價且以模糊數表示而非精確值。另外，在FSAWS 與fuzzy SMART中所有準則的權重與關於各主觀準則對不同方案的評分或結果亦係以語意變數加以評價並以模糊數表示。另一方面，在評價有關於各客觀準則對不同方案的評分係以模糊成本或利益表示以確保能與主觀準則之語意表示的評分相容。同時，本研究在聚集意見程序中考量個別決策者的重要性程度與最終決策者的工作本質。最後，本論文提出三個所建議方法之長處以及共同特性與相異點。

    This study presents three integrated fuzzy group decision approaches for solving supply evaluation and selection problems under fuzzy homo/heterogeneous group-decision environments. In these three approaches, the fuzzy factor rating system (FFRS) and fuzzy simple additive weighting system (FSAWS) are utilized to solve the facility location selection problem by integrating fuzzy set theory (FST) and the factor rating system (FRS) and/or the simple additive weighting (SAW) method. Additionally, the fuzzy simple multi-attribute rating technique (fuzzy SMART) is proposed for solving the supplier evaluation and selection problem by integrating FST and SMART. In FFRS, the importance weights of individual criteria and rating of alternative locations with respect to criteria are assessed linguistic variables represented by fuzzy numbers rather than crisp values. In the FSAWS and fuzzy SMART, the importance weights of all criteria and ratings of different alternatives with respect to subjective criteria are also assessed using linguistic variables represented by fuzzy numbers. On the other hand, the ratings of different alternatives with respect to objective criteria are assessed and represented as fuzzy costs or benefits, thus ensuring the compatibility of representation with linguistic ratings as the subjective criteria. In the aggregating process, this study considers the degree of importance of individual decision-makers (DMs) and the inherent essences in the final DM’s job. Finally, this study presents the primary merits and describes common characteristics and difference of proposed approaches.

    中文摘要 .....................................................................I ABSTRACT ....................................................................II 誌謝 ...................................................................III Table of Contents ...........................................................IV Table of Glossary ............................................................VI List of Figures ..........................................................VII List of Tables .........................................................VIII Chapter 1 Introduction ...................................................1 1.1 Background ............................................................1 1.2 Research motivation and objectives .................................2 1.3 Research scope and structure ..........................................4 Chapter 2 Literature Review ...................................................8 2.1 Group decision-making and simple additive weighting method ...............8 2.2 Literature review of location selection ................................10 2.3 Literature review of supplier selection ................................13 Category ....................................................................18 Distinguishing attributes ..................................................19 Chapter 3 Fuzzy Set Theory ..................................................22 3.1 Triangular fuzzy numbers .........................................22 3.2 Trapezoidal fuzzy numbers .........................................27 Chapter 4 Integrated Fuzzy Group Decision Approaches .......................32 4.1 Fuzzy factor rating system (FFRS) ................................32 4.2 Fuzzy simple additive weighting system (FSAWS) .......................38 4.3 Fuzzy simple multi-attributes rating technique (Fuzzy SMART) ....43 4.3.1 Form a decision committee and identify supplier selection criteria ....43 4.3.2 Assessment and aggregation of the fuzzy weights ......................44 4.3.3 Fuzzy sub-criteria weights ........................................44 4.3.4 Fuzzy criteria weights ........................................45 4.3.5 Defuzzification of the fuzzy weights of criteria ......................45 4.3.6 Computation of the aggregated fuzzy ratings of criteria .............46 4.3.7 Aggregation of fuzzy ratings of individual sub-criteria .............46 4.3.8 Aggregation of fuzzy ratings of individual criteria.....................47 4.3.9 Construction of the fuzzy rating matrix ................................48 4.3.10 Computation of the total fuzzy values of individual alternatives ....48 4.3.11 Defuzzification of the total fuzzy values ......................48 Chapter 5 Applications in Supply Evaluation and Selection .............50 5.1 Application of FFRS for international facility location selection ....50 5.2 Application of FSAWS for facility location selection .............55 5.3 Application of fuzzy SMART for supplier selection ......................61 5.3.1 Empirical illustrations .........................................61 5.3.2 Sensitivity analysis ..................................................66 Chapter 6 Conclusions and Suggestions ................................69 6.1 Conclusions 69 6.1.1 Conclusions of FFRS and FSAWS for facility location selection ....69 6.1.2 Conclusions of fuzzy SMART for supplier selection .............70 6.1.3 Comparison of the proposed approaches ................................72 6.2 suggestions for future research .........................................72 References ...........................................................74 作者簡介 ....................................................................83

    Agarwal, A., Shankar, R. and Tiwari, M. K., 2006, Modeling the metrics of lean, agile and leagile supply chain: An ANP-based approach. European Journal of Operational Research 173, 211-225.
    Badri, M. A., 1999, Combining the analytic hierarchy process and goal programming for global facility location-allocation problem. International Journal of Production Economics 62, 237-248.
    Benjamin, S. C., Chi, T. and Riordan, C. A., 1995, Comparing BP and ART II neural network classifiers for facility location. Computers and Industrial Engineering 28 (1), 43-50.
    Berman, O., Drezner, Z. and Wesolowsky, G. O., 2001, Location of facilities on a network with groups of demand points. IIE Transactions, 33(8), 637-648.
    Buckley, J. J., 1984, The multiple-judge, multiple-criteria ranking problem: A fuzzy-set approach. Fuzzy Sets and System 13 (1), 25-38.
    Buffa, E. S., 1984, Meeting the Competitive Challenge (Homewood, Illinois).
    Butt, S. E. and Cavalier, T. M., 1996, Efficient algorithm for facility location in the presence of forbidden regions. European Journal of Operational Research 90 (1), 56-70.
    Cakravastia, A. and Takahashi, K., 2004, Integrated model for supplier selection and negotiation in a make-to-order environment. International Journal of Production Research 42(21), 4457-4474.
    Canel, C., Khumawala, B. M. and Law, J. and Loh, A., 2001, Algorithm for the capacitated, multi-commodity multi-period facility location problem. Computers and Operations Research 28 (5), 411-427.
    Chang, Y. H. and Yeh, C. H., 2001, Evaluating airline competitiveness using multiattribute decision making. Omega 29, 405-415.
    Chang, H. C., 2004, An application of fuzzy sets theory to the EOQ model with imperfect quality items. Computers and Operations Research 31, 2079-2092.
    Chang, P. C. and Wang, Y. W., 2006, Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry. Expert Systems with Applications 30(4), 715-726.
    Chase, R. B. and Aquilano, N., 1995, Production and Operations Management (Richard D. Irwin, Inc., Chicago).
    Chen, C. T., 2001, A fuzzy approach to select the location of the distribution center. Fuzzy Sets and Systems 118, 65-73.
    Chen, C. T., Lin, C. T. and Huang, S. F., 2006, A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics 102, 289-301.
    Chen, S. J. and Hwang, C. L., 1992, Fuzzy Multiple Attribute Decision Making: Methods and Applications (Springer-Verlag, New York).
    Chiou, H. K., Tzeng, G. H. and Cheng, D. C., 2005, Evaluating sustainable fishing development strategies using fuzzy MCDM approach. Omega 33, 223-234.
    Choi, T. Y. and Hartley, J. L., 1996, An exploration of supplier selection practices across the supply chain. Journal of Operations Management 14, 333-343.
    Choy, K. L., Lee, W. B. and Lo, V., 2003, Design of a case based intelligent supplier relationship management- the integration of supplier rating system and product coding system. Expert Systems with Applications 25, 87-100.
    Chu, T. C., 2002, Facility location selection using fuzzy TOPSIS under group decisions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10 (6), 687-701.
    Comley, W. J., 1995, Location of ambivalent facilities: use of a quadratic zero-one programming algorithm. Applied Mathematical Modeling 19 (1), 26-29.
    Coyle, J. J., Bardi, E. J. and Langley, C. J., 2003, The Management of Business Logistics: A Supply Chain Perspective 7th ed. (Thomson, South-Western).
    Dubois, D. and Prade, H., 1978, Operations on fuzzy numbers. International Journal of Systems Science 9, 613-626.
    de Boer, L., Labro, E. and Morlacchi, P., 2001, A review of methods supporting supplier selection. European Journal of Purchasing & Supply Management 7, 75-89.
    Deng, H., 1999, Multicriteria analysis with fuzzy pairwise comparison. International Journal of Approximate Reasoning 21, 215-231.
    Dickson, G. W., 1966, An analysis of vendor selection system and decisions. Journal of Purchasing 2(1), 28-41.
    Dubois, D. and Prade, H., 1978, Operations on fuzzy numbers. International Journal of Systems Science 9, 613-626.
    Dubrin, A. J., 2002, Fundamentals of Organizational Behavior 2th ed. (Thomson , South-Western).
    Dyer, R. F. and Forman, E. H., 1992, Group decision support with the analytic hierarchy process. Decision Support Systems 8, 99-124.
    Edwards, W., 1977, How to multiattribute utility measurement for social decision making. IEEE Transactions on Systems, Man, and Cybernetics SMC-7, 326-340.
    Edwards, W. and Barron, F. H., 1994, SMARTS and SMARTER: improved simple methods for multiattribute utility measurement. Organizational Behavior and Human Decision Processes 60(3), 306-325.
    Ellram, L. M., 1990, The supplier selection decision in strategic partnerships. Journal of Purchasing and Material Management 26(4), 8-12.
    Farmer, R. N. and Richman, B. M., 1964, A model for research in comparative management. California Management Review Winter, 58-68.
    Finch, B. J., 2003, OperationsNow.com: Processes, Value, and Profitability (McGraw-Hill, New York).
    Ghobadian, A., Stainer, A. and Kiss, T., 1993, A computerized vendor rating system. Proceedings of the First International Symposium on Logistics (pp.321-328). The University of Nottingham, Nottingham, UK.
    Ghodsypour, S. H. and O’Brien, C., 1998, A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming. International Journal of Production Economics 56-57(1-3), 199-212.
    Giunipero, L. C. and Brand, R. R., 1996, Purchasing’s role in supply chain management. International Journal of Logistics management 7(1), 29-38.
    Gorry, G. A. and Scott-Morton, M., 1971, A Framework for Management Information Systems. Sloan Management Review Fall, 55-57, republished Spring, 1989.
    Guzzo, R. A., 1982, Improving Group Decision Making in Organizations: Approaches From Theory and Research (Academic Press, New York).
    Hambrick, D. C. and Fredrickson, J.W., 2001, Are you sure you have a strategy？. Academy of management executive 15(4), 48-59.
    Heizer, J. and Render, B., 2004, Principles of Operations Management (Prentice Hall, New Jersey).
    Henry, D. V., Donald, S. and Martin, W., 1968, Evaluating location alternatives. Journal of Accountancy 125(4), 82-85.
    Heragu, S., 1997, Facilities Design (PWS Publishing, Boston).
    Hockstra, S. and Romme, J., 1992, Integral Logistics Structures: Developing Customer Oriented Goods Flow (London: McGraw-Hill).
    Hong, G. H., Park, S. C., Jang, D. S. and Rho, H. M., 2005, An effective supplier selection method for constructing a competitive supply-relationship. Expert Systems with Applications 28, 629-639.
    Hoffman, J. J. and Schniederjans, M. J., 1994, A two-stage model for structuring global facility site selection decisions. International Journal of Operations & Production Management 14(4), 79-96.
    Hsieh, T. Y., Lu, S. T. and Tzeng, G. H., 2004, Fuzzy MCDM approach for planning and design tenders selection in public office buildings. International Journal of Project Management 22, 573-584.
    Hunt, J. R. and Koulasmas, C. P., 1989, A model for evaluating potential facility locations on a global basis. SAM Advanced Management Journal Summer, 19-23.
    Kabassi, K. and Virvou, M., 2004, Personalised adult e-training on computer use based on multiple attribute decision making. Interacting with Computers 16, 115-132.
    Kahraman, C., Ruan, D. and Dogan, I., 2003, Fuzzy group decision-making for facility location selection. Information Sciences 157, 135-153.
    Keufmann, A. and Gupta, M. M., 1991, Introduction to Fuzzy Arithmetic: Theory and Application (Van Nostrand Reinhold, New York).
    Klic, G. I. and Yan, B., 1995, Fuzzy Sets and Fuzzy Logic Theory and Applications (Prentice-Hall, London).
    Kugel, Y., 1973, A decision model for the multinational firm. International Management Review 13, 3-14.
    Kuo, R. J., Chi, S. C. and Kao, S. S., 1999, A decision support system for locating convenience store through fuzzy AHP. Computers and Industrial Engineering 37, 323-326.
    Kuo, R. J., Chi, S. C. and Kao, S. S., 2002, A decision support system for selecting convenience store location through integration of fuzzy AHP and artificial neural network. Computers in Industry 47, 199-214.
    Kuo, R. J. and Chen, J. A., 2004, A decision support system for order selection in electronic commerce based on fuzzy neural network supported by real-coded genetic algorithm. Expert Systems with Applications 26(2), 141-154.
    Lee, W.B., Lau, H., Liu, Z. Z.and Tam, S., 2001, A fuzzy analytic hierarchy process approach in modular product design. Expert Systems 18(1), 32-42.
    Liang, G. S., 1999, Fuzzy MCDM based on ideal and anti-ideal concepts. European Journal of Operational Research 112, 682-691.
    Liang, G. S. and Wang, M. J. J., 1991, A fuzzy multi-criteria decision-making method for facility site selection. International Journal of Production Research 29(11), 2313-2330.
    MacCrimmon, K. R., 1968, Decision making among multiple attribute alternatives: A survey and consolidated approach. RAND Memorandum, RM-4823-ARPA.
    Melkote, S. and Daskin, M. S., 2001, Capacitated facility location/network design problems, European Journal of Operational Research 129(3), 481-495.
    Miller, G. A., 1965, The magic number seven, plus or minus seven. Psychological Review 63, 81-97.
    Min, H. and Melachrinoudis, E., 1996, Dynamic location and entry mode selection of multinational manufacturing facilities under uncertainty: A chance-constrained goal programming approach. International Transactions in Operational Research 3(1), 65-76.
    Monczka, J. P. and Morgan, J. P., 1992, Strategic sourcing management. Purchasing 13, 70.
    Monczka, R., Trent, R. and Handfield, R., 1998, Purchasing and Supply Chain Management (South-Western College Publishing, New York).
    Nanthavanij, S. and Yenradee, P., 1999, Predicting the optimum number, location, and signal sound level of auditory warning devices for manufacturing facilities, International Journal of Industrial Ergonomics 24(6), 569-578.
    Naylor, J. B., Naim, M. M. and Berry, D., 1999, Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain. International Journal of Production Economics 62, 107-118.
    Nobuaki, S., Akira, U., Atsushi, D., Akira, O., Seiji, H. and Satoshi, H., 1998, Commercial facility location model using multiple regression analysis. Computers Environment and Urban Systems 22(3), 219-240.
    Ölçer, A. İ. and Odabaşi, A. Y., 2005, A new fuzzy multiple attributive group decision making methodology and its application to propulsion/manoeuvring system selection problem. European Journal of Operational Research 166, 93-114.
    Porter, M. E., 1980, Competitive Strategy: Techniques for Analyzing Industries and Competitors (The Free Press, New York).
    Porter, M. E. and Millar, V. E., 1985, How information gives you competitive advantage. Harvard Business Review 63 (4), 149-160.
    Saunders, M., 1994, Strategic Purchasing and Supply Chain Management (Pitman Publishing, London).
    Simchi-Levi, D., Kaminsky, P. and Simchi-Levi, E., 2003, Designing & Managing the Supply Chain (McGraw-Hill, New York).
    Stevenson, W. J., 2005, Operations Management 8th ed. (McGraw-Hill, New York).
    Tang, M.T., Tzeng, G. H. and Wang, S. W., 1999, A hierarchy fuzzy MCDM method for studying electronic marketing strategies in the information service industry. Journal of International Information Management 8(1), 1-22.
    Takeda, E., 1982, Interactive Identification of Fuzzy Outranking Relations in a Multicriteria Decision Problem. Fuzzy Information and Decision Processes. (North-Holland, Amsterdam).
    Tsaur, S. H., Tzeng, G. H. and Wang, G. C., 1997, Evaluating tourist risks from fuzzy perspectives. Annals of Tourism Research 24(4), 796-812.
    van Weele, A. J., 2000, Purchasing and Supply Chain Management (Thomson Learning, London).
    Virvou, M. and Kabassi, K., 2004, Evaluating an intelligent graphical user interface by comparison with human experts. Knowledge-Based Systems 17, 31-37.
    Vonderembse, M. A., Uppal, M., Huang, S. H. and Dismukes, J. P., 2006, Designing supply chains: Towards theory development. International Journal of Production Economics 100, 223-238.
    Wang, L. X., 1992, A Course in Fuzzy Systems and Control (Prentice Hall, New
    Jersey).
    Wang, G., Huang, S. H. and Dismuke, J. P., 2004, Product-driven supply chain selection using integrated multi-criteria decision-making methodology. International Journal of Production Economics 91, 1-15.
    Wang, Y. M. and Elhag, T. M. S., 2006, Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment. Expert Systems with Applications 31(2), 309-319.
    Watts, C. A., Kim, K. Y. and Hahn, C. K., 1992, Linking purchasing to corporate competitive strategy, International Journal of Purchasing and Materials Management, 4-7.
    Weber, C. A., Current, J. R. and Benton, W. C., 1991, Vendor selection criteria and methods. European Journal of Operational Research 50 (1), 2-18.
    Wisner, D. J., 2003, A structural equation model of supply chain management strategies and firm performance, Journal of Business Logistics 24(1), 1-25.
    Yager, R. R., 1982, Fuzzy Sets and Possibility Theory (Pergumon Press, Oxford).
    Yager, R. R.and Filev, D. P., 1994, Essentials of Fuzzy Modeling and Control (Wiley, New York).
    Yao, J. S. and Chiang, J., 2003, Inventory without backorder with fuzzy total cost and fuzzy storing cost defuzzified by centroid and signed distance. European Journal of Operational Research 148, 401-409.
    Yao, J. S. L., Ouyang, Y. and Chang, H. C., 2003, Models for a fuzzy inventory of two replaceable merchandises without backorder based on the signed distance of fuzzy sets. European Journal of Operational Research 150, 601-616.
    Yao, J. S. and Wu, K., 2000, Ranking fuzzy numbers based on decomposition principle and signed distance. Fuzzy Sets and Systems 116, 275-288.
    Yetton, P. and Botter, P., 1983, The relationships among group size, member ability, social decision schemes, and performance. Organizational Behavior and Human Performance October, 145-159.
    Yurimoto, S. and Masui, T., 1995, Design of a decision support system for overseas plant location in the EC. International Journal of Production Economics 41, 411-418.
    Zadeh, L. A., 1965, Fuzzy sets. Information and control 8, 338-353.
    Zanakis, S. H., Solomon, A., Wishart, N. and Dublish, S., 1998, Multi-attribute decision making: A simulation comparison of select methods. European Journal of Operational Research 107, 507-529.
    Zhao, R. and Govind, R., 1991, Algebraic characteristics of extended fuzzy numbers. Information Science 54(1), 103-130.
    Zimmermann, H. J., 2001, Fuzzy Set Theory and Its Applications 4th ed. (Kluwer Academic Publishers, Boston).

    QR CODE