隨著台灣汽車零組件工業的發展，台灣汽車零組件的自製比率越來越高，已經成為台灣重要的產業之一，然而面對國際化與顧客需求呈現多樣少量的影響，企業必須推動經營改革導入低成本與高彈性的生產系統，朝向低成本、高品質與更短交期為目標，以提升企業競爭力與滿足顧客需求。
本研究將以汽車燈具製造廠為實際案例，將精實物流的概念應用於物流方案之設計，提出適合且實際可行的方案，作為企業未來改善之目標。首先利用價值流圖分析案例公司現況，找出現況浪費之所在作為改善的契機，接著利用本研究提出的六大步驟，發展出四個物流方案，以提供案例公司做為未來物流改善的方向。
為了考慮物流方案的周全性，除了利用模擬的方式蒐集平均物料搬運距離、平均搬運週期時間、作業員人數三項定量指標，本研究也將提出四個定性指標，並且藉由專家評定四個定性指標，最後透過一套有系統的、客觀的，且可以同時考量多個目標的模糊多屬性決策方法，來評估四種物流方案的優劣。
最後評選結果方案4為排序最佳之方案，與排序最差的方案相比較，在移動距離指標減少了35%，在作業員人數也比最差的方案少1人，方案4在定量指標與定性指標分別評比的狀況下皆為最佳，但在改變難易度的指標中被認定為最高，雖然近期內無法達成，但管理者可以藉由循序漸進的方式進行改善，以方案4做為未來改善之目標。

With the development of Taiwan's motor vehicle parts manufacturing, Taiwan's motor vehicle parts of the self-made rate is higher and higher, has become one of Taiwan's important industries, but the face of international and customer demand presents a variety of small impact, enterprise must promote business reform import low-cost and high-flexible production system, towards low-cost, high quality and shorter delivery as the goal to enhance the competitiveness of enterprises and meet customer needs.
This study is conducted to the case firm, a automotive lighting manufacturing, and try to impel material flow in design of automotive lighting’s production line and propose the improvement plan and the future plan which can be referred by case firm. At first, we use the Value Stream Mapping (VSM) to analyze the current situation of the case company, and find where it can be our turning point of improvement. Second, taking advantage of the six step submitted from this study, we can develop four feasible logistic plan to offer them the direction of the logistic plan in the future.
As considering the completeness of the logistic plan, we not only collect equal the moving distance of material, equal the cycle time, and number of operator three quantitative indexes by simulation, the study will present four qualitative indexes which are judged by expert, and through a systematic, fuzzy multiple attribute decision-making method at the same time to judge the pros and cons of the four logistic plan.
Finally, the results of the selection case 4 for the best sort of the case. Compared with the worst-case scenario, the moving distance index is reduced by 35%, and the number of operators is reduced by one person. The case 4 between quantitative indicators and qualitative indicators are all the best, however, changing the difficulty in the index is considered the highest. Although it can’t be achieved in the near future, manager can improve it step by step and use case 4 as the future goal.

上海坤道信息技術有限公司，2016，Available：
http://www.simu-cad.com/product-1733.aspx（取得日期:2016.8.7）
李克．哈里斯、克里斯．哈里斯、艾爾．威爾森，陳起譯，2008，精實物流 Making Materials Flow，財團法人中衛發展中心，台北。
梁宜峰，2016，汽車零件及車體製造業基本資料，台經院產經資料庫，研究分析報告。
許志義，2003，多目標決策，五南圖書出版股份有限公司，台北。
麥克．魯斯、約翰．舒克，趙克強譯，2006，學習觀察 : 增加價值、消除浪費的價值流圖，財團法人中衛發展中心，台北。
楊大和，2016，精實系統知識體系導入流程，國立成功大學製造資訊與系統研究所教材。
楊大和、謝瓊嬉，2008，電子業向汽車業取經―精實生產在TFT-LCD產業成功的應用，品質月刊，民國97年，9月。
陳文哲、劉樹童，2009，設施規劃：工廠佈置與物料搬運，鼎茂圖書出版有限公司，台北。
經濟部統計處，2016，Available：
http://www.moea.gov.tw/MNS/dos/home/Home.aspx（取得日期：2016.8.7）
Alnahhal, M., and Noche, B., 2015, Dynamic material flow control in mixed model assembly lines, Computers and Industrial Engineering, 85, 110-119.
Arena user’s manual, 2013, Rockwell Automation, Inc., Milwaukee, WI, U.S.A.
Bellman, R.E., and Zadeh, L.A., 1970, Decision-making in a fuzzy environment, Management Science, 17 (4), 141-164.
Boran, F.E., Genç, S., Kurt, M., and Akay, D., 2009, A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method, Expert Systems with Applications, 36 (8), 11363-11368.
Chen, C., 2000, Extensions of the TOPSIS for group decision making under fuzzy environment, Fuzzy Sets and Systems, 114 (1), 1-9.
Cheng, C.H., 1999, Evaluating weapon systems using ranking fuzzy numbers, Fuzzy Sets and Systems, 107 (1), 25-35.
Chou, S.Y., Chang, Y.H., and Shen, C.Y., 2008, A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes, European Journal of Operational Research, 189 (1), 132-145.
Das, B., Venkatadri, U., and Pandey, P., 2014, Applying lean manufacturing system to improving productivity of airconditioning coil manufacturing, The International Journal of Advanced Manufacturing Technology, 71 (1-4), 307-323.
Domingo, R., Alvarez, R., Melodía Peña, M., and Calvo, R., 2007, Materials flow improvement in a lean assembly line: a case study, Assembly Automation, 27 (2), 141-147.
Dubois, D., and Prade, H., 1979, Fuzzy real algebra：some results, Fuzzy Sets and Systems, 2 (4), 327-348.
Ezema, C. N., Okafor, E. C., and Okezie, C. C., 2017, Industrial design and simulation of a JIT material handling system, Cogent Engineering, 4 (1), 1292864.
Hodge, G. L., Goforth Ross, K., Joines, J. A., and Thoney, K., 2011, Adapting lean manufacturing principles to the textile industry, Production Planning and Control, 22 (3), 237-247.
Hwang, C.L., and Yoon, K., 1981, Multiple Attribute Decision Making, Springer, New York.
Kelton, W.D., Sadowski, R.P., and Swets, N.B., 2014, Simulation with Arena, 6th edition, McGraw-Hill Education, New York.
Kim, G., Park, C. S., and Yoon, K. P., 1997, Identifying investment opportunities for advanced manufacturing systems with comparative-integrated performance measurement, International Journal of Production Economics,50 (1), 23-33.
Liker, J.K., 2004, The Toyota Way：14 Management Principles from The World's Greatest Manufacturer, McGrawHill, New York.
Lu, J. C., Yang, T., and Wang, C. Y., 2011, A lean pull system design analysed by value stream mapping and multiple criteria decision-making method under demand uncertainty, International Journal of Computer Integrated Manufacturing, 24 (3), 211-228.
Ohno, T., 1988, Toyota Production System: Beyond Large-Scale Production, Productivity Press, Portland.
Ribeiro, R.A., 1996, Fuzzy multiple attribute decision making: Areview and new preference elicitation techniques, Fuzzy Sets and Systems, 78 (2), 155-181.
Smalley, A., 2004, Creating Level Pull, Lean Enterprise Institute, Brookline.
Womack, J.P., and Jones, D.T. and Roos, D., 1990, The Machine That Changed the World, Harper Collins, New York.
Womack, J.P., and Jones, D.T., 1996, Lean Thinking : Banish Waste and Create Wealth in Your Corporation, Simon and Schuster, New York.
Womack, J.P., Jones, D.T., 1994, From lean production to the lean enterprise, Harvard Business Review, 72 (2), 93-103.
Yang, T., and Hsieh, C.H., 2009, Six-Sigma project selection using national quality award criterial and Delphi fuzzy multiple criterial decision-making method, Expert Systems with Applications, 36 (4), 7594-7603.
Yang, T., and Hung, C.C., 2007, Multiple-attribute decision making methods for plant layout design problem, Robotics and Computer Integrated Manufacturing, 23 (1), 126-137.
Yang, T., and Kuo, C., 2003, A hierarchical AHP/DEA methodology for the facilities layout design problem, European Journal of Operational Research, 147 (1), 128-136.
Yang, T., and Lu, J. C, 2011, The use of a multiple attribute decision-making method and value stream mapping in solving the pacemaker location problem, International Journal of Production Research, 49 (10), 2793-2817.
Yang, T., Hsieh, C. H., and Cheng, B. Y., 2011, Lean-pull strategy in a re-entrant manufacturing environment: a pilot study for TFT-LCD array manufacturing, International Journal of Production Research, 49 (6), 1511-1529.
Zadeh, L. A., 1965, Fuzzy Sets. Information and control, 8 (3), 338-353.
Zimmermann, H.J., 1987, Fuzzy Sets, Decision Making, and Expert Systems, Kluwer Academic Publishers, Boston.