ICA  Vol.5 No.4 , November 2014
Study on Module Selection Method for Customized Products
Abstract: Modularization is the key technique for modern manufacturing system, which resolves the conflict between flexibility and productivity. The challenge of deciding which modules should choose under resource limitation from a large amount of available alternative modules has been well recognized in academia and industry correspondingly in producing customized production. For this reason, this paper proposes a new module selection method to deal with the problem, which combines rough set theory into total quality development (QFD) framework. First of all, a decision table is build up and then be modified through examining the importance of each attribute. Afterwards, the basic importance rating vector is calculated and the modifying index of the importance will be determined to get the final result. Finally, the feasibility and efficiency of the proposed method is manifested by a case study.
Cite this paper: Liu, H. (2014) Study on Module Selection Method for Customized Products. Intelligent Control and Automation, 5, 245-252. doi: 10.4236/ica.2014.54026.

[1]   Tseng, M.M. and Jiao, J. (2001) Mass Customization, Handbook of Industrial Engineering. 3rd Edition, Wiley, New York.

[2]   Jarratt, T.A.W., et al. (2011) Engineering Change: An Overview and Perspective on the Literature. Research in Engineering Design, 22, 103-124.

[3]   Rekiek, B., Dolgui, A., Delchambre, A. and Bratcu, A. (2002) State of Art of Optimization Methods for Assembly Line Design. Annual Reviews in Control, 26, 163-174.

[4]   Fujita, K. (2002) Product Variety Optimization under Modular Architecture. Computer-Aided Design, 34, 953-965.

[5]   Gonzalez, J.P. and Otto, K.N. (2000) Modular Platform-Based Product Family Design. Proceedings of the DETC’00 ASME Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Baltimore, 677-686.

[6]   Yigit, A.S. and Allahverdi, A. (2003) Optimal Selection of Module Instance for Modular Products in Reconfigurable Manufacturing System. International Journal of Production Research, 41, 4063-4074.

[7]   Swaminathan, K.M. and Tayur, S.R. (1998) Managing Broader Product Line through Delayed Differentiation Using Vanilla Boxes. Management Science, 44, 161-172.

[8]   Pawlak, Z. (1991) Rough Classication. International Journal of Human-Compute Studies, 5, 369-383.

[9]   Pawlak, Z. (1995) Vagueness and Uncertainty—A Rough Set Perspective. Computational Intelligence, 11, 227-232.

[10]   Almannai, B., Greenough, R. and Kay, J. (2008) A Decision Support Tool Based on QFD and FMEA or the Selection of Manufacturing Automation Technologies. Robotics and Computer Integrated Manufacturing, 24, 501-507.

[11]   Bottani, E. (2009) A Fuzzy QFD Approach to Achieve Agility. International Journal of Production Economics, 119, 380-391.

[12]   Shah, R. and Ward, T. (2007) Defining and Developing Measures of Lean Production. Journal of Operations Management, 25, 785-805.