目前台灣汽車及其零件製造業之生產價值有逐年成長的趨勢，其中汽車零件製造業在2018年的生產價值為新台幣1908億元，為汽車產業的52.84%，佔有舉足輕重的地位。其中，汽車用電燈組又佔了汽車零件製造業產品項目44%，非常具有代表性。
本文探討之案例公司為一間車燈製造廠，目前正面臨工廠內部物料搬運之問題，其導致組裝線停線為了等待物料、換料等問題，因此設計一套符合工廠現況需求的物料搬運系統將會是關鍵。
本研究首先以精實物流和自動化系統中的物料分析考量的原則以及步驟分析現況物流方案，並在考量實務的限制之下，將所有可能的方案組合發展出了48種替代方案。現實的情況中，很少的機會只有考慮單一個指標，因為有多種的因素都會影響到物料搬運系統的優劣，且在大多數的真實情形之下，決策者所提供的意見或資訊通常會以不明確的方式說明，並非一定為明確值，因此利用模糊多屬性決策分析本研究之物料搬運系統之問題，以權衡所有指標並在各種的組合考量之下選擇最佳的方案。
因此本研究進一步以模糊多屬性決策分別對所提出的指標進行權重的計算，以及對方案進行排序。根據方案分析之結果，以替代方案48為最佳之選擇，在敏感度分析中顯示了此項決策的可信度以及穩健性，也表示管理者能在此問題的選擇上具有彈性，此方法具有相當的潛力提供管理者在物料搬運系統設計時的重要參考。

At present, the production value of Taiwan's automobile and parts manufacturing industry is growing year by year. Among them, the production value of the automobile parts manufacturing industry in 2018 was NT $ 190.8 billion, 52.84% of the automobile industry, and it occupies a pivotal position. Among them, the automobile electric light group accounts for 44% of the auto parts manufacturing products project, which is very representative.The case company discussed in this article is a car light manufacturing plant, which is currently facing the problem of material handling inside the factory, which leads to the suspension of the assembly line in order to wait for materials, refueling, etc., so it designed a material handling system that meets the needs of the factory will be the key.This research first analyzes the current logistics solutions based on the principles and steps of material analysis in lean logistics and automation systems, and under the constraints of practical considerations, 48 possible solutions have been developed by combining all possible solutions. In reality, there are very few opportunities to consider only a single indicator, because there are many factors that will affect the quality of the material handling system, and in most real situations, the opinions or information provided by the decision makers usually take the unclear method means that it is not necessarily a clear value. Therefore, fuzzy multiple attribute decision making is used to analyze the problem of the material handling system in this study, to weigh all indicators and select the best solution under various combinations. Therefore, this study further uses fuzzy multiple attribute decision making to calculate the weights of the proposed indicators and rank the schemes. According to the results of the experimental analysis, alternative 48 is the best choice. The sensitivity analysis shows the credibility and robustness of this decision. It also means that managers can be flexible in the choice of this problem. This method It has considerable potential to provide managers with an important reference when designing material handling systems.

李輝煌, 2004, 田口方法-品質設計的原理與實務, 高立圖書有限公司, 台北
經濟部統計處(2019)，「工業產銷存價值統計調查」，(取得日期：2019年12月20日)，[available at https://dmz26.moea.gov.tw/GMWeb/investigate/InvestigateDB.aspx]
Bellman, R. E., & Zadeh, L. A., 1970, Decision-making in a fuzzy environment, Management Science, 17(4), B-141-B-164.
Braglia, M., Frosolini, M., & Montanari, R., 2003, Fuzzy TOPSIS approach for failure mode, effects and criticality analysis, Quality and Reliability Engineering International, 19(5), 425-443.
Chen, C.-T, 2000, Extensions of the TOPSIS for group decision-making under fuzzy environment, Fuzzy Sets and Systems, 114(1), 1-9.
Chen, S.-J., & Hwang, C.-L., 1992, Fuzzy Multiple Attribute Decision Making Methods, Springer, New York City.
Cheng, C.-H., 1999, Evaluating weapon systems using ranking fuzzy numbers, Fuzzy Sets and Systems, 107(1), 25-35.
Cheng, C.-H., & Lin, Y., 2002, Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation, European Journal of Operational Research, 142(1), 174-186.
Czajkowski, A. F., & Jones, S., 1986, Selecting interrelated R & D projects in space technology planning, IEEE Transactions on Engineering management, 1), 17-24.
Dubois, D., & Prade, H., 1979, Operations in a fuzzy-valued logic, Information and Control, 43(2), 224-240.
Groover, M. P., 2015, Automation, Production Systems, and Computer-Integrated Manufacturing, Pearson Education, New Jersey.
Harris, R., Harris, C., & Wilson, E., 2003, Making Materials Flow: A Lean Material-Handling Guide for Operations, Production-Control, and Engineering Professionals, Lean Enterprise Institute, Boston.
Hsieh, T.-Y., Lu, S.-T., & Tzeng, G.-H., 2004, Fuzzy MCDM approach for planning and design tenders selection in public office buildings, International Journal of Project Management, 22(7), 573-584.
Hwang, C.-L., & Yoon, K., 1981, Multiple criteria decision making, Lecture Notes in Economics and Mathematical Systems, 186(1), 58-191.
Jain, A., Nandakumar, K., & Ross, A., 2005, Score normalization in multimodal biometric systems, Pattern Recognition, 38(12), 2270-2285.
Karsak, E. E., 2002, Distance-based fuzzy MCDM approach for evaluating flexible manufacturing system alternatives, International Journal of Production Research, 40(13), 3167-3181.
Kengpol, A., Rontlaong, P., & Tuominen, M., 2013, A Decision Support System for Selection of Solar Power Plant Locations by Applying Fuzzy AHP and TOPSIS: An Empirical Study, Journal of Software Engineering and Applications, 6(1), 470-481.
Kumar, S., Singh, B., Qadri, M. A., Kumar, Y. S., & Haleem, A., 2013, A framework for comparative evaluation of lean performance of firms using fuzzy TOPSIS, International Journal of Productivity and Quality Management, 11(4), 371-392.
Olson, D. L., 2004, Comparison of weights in TOPSIS models, Mathematical and Computer Modelling, 40(7-8), 721-727.
Onut, S., Kara, S. S., & Mert, S., 2009, Selecting the suitable material handling equipment in the presence of vagueness, The International Journal of Advanced Manufacturing Technology, 44(7-8), 818-828.
Patro, S., & Sahu, K. K., 2015, Normalization: A preprocessing stage, arXiv preprint arXiv:1503.06462,
Peterson, P. P., & Fabozzi, F. J., 2002, Capital Budgeting: Theory and Practice, John Wiley & Sons, New Jersey.
Ranga, S., Jaimini, M., Sharma, S. K., Chauhan, B. S., & Kumar, A., 2014, A review on Design of Experiments (DOE), International Journal of Pharmaceutical and Chemical Sciences, 3(1), 216-224.
Ribeiro, R. A., 1996, Fuzzy multiple attribute decision making: a review and new preference elicitation techniques, Fuzzy Sets and Systems, 78(2), 155-181.
Ryan, P. A., & Ryan, G. P., 2002, Capital budgeting practices of the Fortune 1000: how have things changed, Journal of business and management, 8(4), 355-364.
Saaty, T. L., 1996, Decision Making with Dependence and Feedback: The Analytic Network Process, Rws Publications, Pittsburgh.
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.
Shivhare, M., & McCreath, G., 2010, Practical considerations for DoE implementation in quality by design, BioProcess Int, 8(6), 22-30.
Yüksel, İ., & Dagdeviren, M., 2007, Using the analytic network process (ANP) in a SWOT analysis–A case study for a textile firm, Information Sciences, 177(16), 3364-3382.
Yang, T., & Hung, C.-C., 2007, Multiple-attribute decision making methods for plant layout design problem, Robotics and Computer-Integrated Manufacturing, 23(1), 126-137.
Zadeh, L. A., 1965, Fuzzy sets, Information and Control, 8(3), 338-353.