本研究提出的仿真模型，證明使用2線和4線的拼接面積常用於制鞋業鞋類工廠。雖然常見的做法有沒有調查關於使用這兩種配置的好處和缺點。因此，本研究旨在證明這行配置具有較好的抗衝擊，為公司。單位每工時（UPMH）被選中作為研究的性能參數。在真實系統上存在的其他因素也包括在模型中。
從觀察到的公司收集的數據為基礎，構建仿真模型，選定的性能參數和設計因素方面。所有72個場景進行了仿真實驗和分析UPMH。因子設計的分析表明，2 - 線的使用比4線是更為有利，因為它產生較高的UPMH。其各自的水平也有顯著的影響上UPMH與其他實驗因素。

This research presents simulation modelling to justify the usage of 2-lines and 4-lines of stitching area in footwear factory which commonly used on footwear industry. Despite the common practice there are no investigation about benefit and drawback of using those two configuration. Therefore, this research aims to justify which line configuration has better impact for the company. Unit per Man Hour (UPMH) is selected as research's performance parameter. Other factors that exist on the real system are also included in the model.
Simulation model was constructed based on collected data from observed company, with regards to selected performance parameter and design factors. Experimental simulation was done for all 72 scenarios and UPMH is analyzed. Factorial design analysis shows that the usage of 2-lines is more beneficial than 4-lines as it yields higher UPMH. The other experimental factors also have significant effect on UPMH with its own respective level.

1. APICCAPS, World footwear yearbook, CEGEA and P.C. University, Editors. 2012, Portugese Footwear, Components and Leather Goods Manufacturers Association: Porto.
2. IBISWorld, Global footwear manufacturing, in Global Industry Research. 2010, IBISWorld: Santa Monica.
3. Institute, T.E., Thailand case study on environment requirements, market access and competitiveness in the leather and footwear sector. 2003, Thailand Environment Institute: Bangkok.
4. BBC. China promises rise in minimum wage to close income gap. BBC Business news 2013 [cited 2013 June 12].
5. TOI. Minimum wage to be increased to Rs 215 a day. Times of India : City News 2012 [cited 2013 June 12].
6. Martin, N. Asian countries to boost growth by hiking minimum wages. Deustche Welle : Globalization 2013 [cited 2013 June 12].
7. Subramanian, A.K., Cell loading and scheduling in a shoe manufacturing company, in College of Engineering and Technology. 2004, Ohio University: Lancaster. p. 130.
8. Zangiacomi, A., et al., Process planning and scheduling for mass customised shoes manufacturing. International Journal of Computer Integrated Manufacturing, 2004. 17(7): p. 613-621.
9. Parwananta, H., Branch and bound method for solving resource-constrained assembly line balancing problem in footwear sewing line, in Industrial Management. 2012, National Taiwan University of Science and Technology: Taipei.
10. Eryilmaz, M.S., et al., Analysis of shoe manufacturing factors by simulation of production processes. Southeast Europe Journal of Soft Computing, 2012. 1: p. 120-127.
11. Nisanci, H.I. and R.J. Sury, Production analysis by simulation in a shoe manufacturing factory. International Journal of Production Research, 1980. 18(1): p. 31-41.
12. Smith, J.S., Survey on the use of simulation for manufacturing system design and operation. Journal of Manufacturing System, 2003. 22(2): p. 157-171.
13. Gelenbe, E. and H. Guennouni, FLEXSIM : A flexible manufacturing system simulator. European Journal of Operational Research, 1991. 53: p. 149-165.
14. Chan, F.T.S. and H.K. Chan, Design of a PCB plant with expert system and simulation approach. Expert Systems with Applications, 2005. 28(3): p. 409-423.
15. Um, I., H. Cheon, and H. Lee, The simulation design and analysis of a Flexible Manufacturing System with Automated Guided Vehicle System. Journal of Manufacturing Systems, 2009. 28(4): p. 115-122.
16. Padhi, S.S., et al., A simulation-based methodology to analyse production line disruptions. International Journal of Production Research, 2013. 51(6): p. 1885-1897.
17. Greasley, A., Using simulation for facility design : A case study. Simulation Modelling Practice and Theory, 2008. 16: p. 670-677.
18. Altuntas, S. and H. Selim, Facility layout using weighted association rule-based data mining algorithms : Evaluation with simulation. Experts System with Application, 2012. 39: p. 3-13.
19. Chen, J.C., et al., Capacity improvement for color-filter fabs through computer modeling and iterative simulation. Journal of the Society for Information Display, 2011. 19(7): p. 480-487.
20. Chen, J.C., et al., Simulation-based look-ahead release panning for color filter fabs. SID Symposium Digest of Technical Papers, 2012. 43(1): p. 1264-1267.
21. Sargent, R.G., Verification and validation of simulation models, in Proceedings of the 37th conference on Winter simulation. 2005, Winter Simulation Conference: Orlando, Florida. p. 130-143.
22. Troitzsch, K.G. Validating simulation models. in 18th European Simulation Multiconference on Networked Simulation and Simulation Networks. 2004. Magdeburg, Germany: SCS Publishing House.
23. Law, A.M., Simulation modeling and analysis. 4th ed. 2007, New York: McGraw-Hill.
24. Jannat, S. and A.G. Greenwood. Estimating parameters of the triangular distribution using non-standard information. in 2012 Winter Simulation Conference. 2012. Berlin, Germany.
25. Montgomery, D.C., Design and analysis of experiment. 7th ed. 2009, New Jersey: John Wiley & Sons, Inc.
26. Stevenson, W.J., Operations management. 10th ed. Operations and Decision Management. 2008, New York: McGraw-Hill.