產品模組分析的目標在於發展一個實體上可分開成許多獨立單元的產品架構，使得單元內互動關係緊密，而單元間的相互影響能降低。以因應大量的客製化，並維持經濟規模。而模組分析可經由資料群集方法來實現。所以，本文研究的重點分為資料群集方法與系統化模組分析方法。首先，資料群集方法的研究，將發展非參數型式，以確合自然群集的原則與目的；因此，依資料型態，本文發展兩種資料群集法，分別為多中心群集法與混合型資料群集法。多中心群集法適於解決區間尺度的資料型態；而混合型資料群集法則處理，區間尺度與等級尺度混合的方法。其次，系統化模組分析方法，分別針對平台產品與單一產品，發展出三種方法：其一為應用多目標數學規劃，並運用混合型資料群集法，求解平台產品的模組分析；其二為應用線性數學規劃，並整合品質機能展開，處理單一產品的模組分析；最後，以混合型資料群集法處理單一產品的模組分析。
多中心群集法包含兩個階段--分割與合併階段。於分割階段，採用基因演算法來搜尋合適的中心點，好將資料區分成數個群組；於合併階段，則運用一全新的判斷演算法，來整合多個中心點形成一適當的群組。而混合型群集法，整合熵與變異數的概念，計算群組內資料的一致性；而後，運用群集基因演算法來搜尋群組數，與合適的群集資料。
平台技術助益產品的衍生與變異，而模組設計則加增企業的彈性，二者均著手於產品零組件的配置問題。所以，本文於平台產品模組分析的目標，在於同時尋求平台共用最大化，與模組設計最佳化，使得產品家族能滿足個別市場需求；並且以系統化的流程，協助產品設計師，由單一產品開發，擴張為產品家族同步開發，從產品家族的衍生與變異需求擬定產品計劃，進而以功能模型分解產品功能類別與實體關係分析，再以零組件關係矩陣與設計需求分析，經由多目標規劃於群集基因演算法中，求解模組設計與建構平台。另一方面，當進行單一產品的模組分析時，主要經由市場競爭分析，藉由模組化驅動力來反映較重要的設計需求，其目的在於尋求產品改善的方向。此一方法中，應用數學規劃來協助產品設計師進行模組的最佳化分析。本文所發展的方法，皆藉由實際案例來說明方法的效用。

Cluster analysis aims at discovering groups and identifying meaningful distributions and patterns in data sets. The aim in this study is to develop two nonparametric clustering techniques that will not assume any particular shape of a data set, since they are able to evolve a proper number of clusters as well as provide the appropriate clustering automatically through the following procedures: firstly, a multi-seed data clustering technique which consists of splitting and merging algorithms to deal with complex shapes of clusters is developed. Experimental results show the problem of one-seed-per-cluster and the effectiveness of this clustering technique. Secondly, a heuristic grouping genetic algorithm is adopted to search for the optimal or near-optimal modular architecture.
Modular products are products that fulfill various functions through the combination of distinct modules. These detachable modules are constructed by both according to the maximum physical and functional relations among components and maximizing the similarity of specifically modular driving forces. In this study, two modular product design methodologies are proposed. Firstly, Quality Function Deployment is deployed to accomplish modular product design with linear integer programming. Secondly, a non-linear programming with the heuristic grouping genetic algorithm is proposed to identify separable modules and simultaneously optimize the number of modules Furthermore, modular product design to form product platform is, thus, important to enhance resource allocation and promote mass customization. In this study, a mathematic programming is proposed to simultaneously conduct platform and modular design as well as to optimize the number of modules. Hence, a systematic design approach is developed according to product planning, functional relationships analysis, and physical relationships analysis, in which functional flow chart, interaction matrices, and computer programming are adopted to visualize the design process.

1. Ankerst, M., Breunig, M. M., Kriegel, H. P., and Sander, J., (1999), OPTICS: ordering points to identify the clustering structure, Proc. ACM SIGMOD’99 Int. Conf. On Management of Data, Philadelphia, PA.
2. Bandyopadhyay, S., and Maulik, U., (2001), Nonparametric genetic clustering: comparison of validity indices, IEEE Transactions on Systems, Man, and Cybernetics--Part C: Applications and Reviews, 31(1), February 120-125.
3. Bandyopadhyay, S., and Maulik, U., (2002), Genetic clustering for automatic evolution of clusters and application to image classification, Pattern Recognition, 35, 1197-1208.
4. Bezdek, J. C., and Pal, N. R., (1998), Some new indexes of cluster validity, IEEE Transactions on Systems, Man, and Cybernetics--Part B: Cybernetics, 28(3), 301-315.
5. Bicknell, B.A., and Bicknell, K.D., (1995), “The Road Map to Repeatable Success: Using QFD to Implement Change,” CRC press.
6. Chaudhuri, D., and Chaudhuri, B. B., (1997), A novel multiseed nonhierarchical data clustering technique, IEEE Transactions on Systems, Man, and Cybernetics--Part B: Cybernetics, 27(5), 871-877.
7. Chiou, Y. C., and Lan, L. W., (2001), Genetic clustering algorithms, European Journal of Operational Research, 135, 413-427.
8. Clark, K. B., and Wheelweight, S., (1996), “Leading Product Development,” The Free Press.
9. Dahmus, J. B., Gonzalez-Zugasti J. O., and Otto, K. N., (2001), Modular product architecture, Design Studies, 22, 409-424.
10. Davies, D. L., and Bouldin, D. W., (1979), A cluster separation measure, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(4), 224-227.
11. Duray, R., Ward, P. T., Milligan, G. W., and Berry, W. L., (2000), Approaches to mass customization: configurations and empirical validation, Journal of Operations Management, 18(6), 605-625.
12. Ericsson, A., and Erixon, G., (1999), “Controlling Design Variants: Modular Product Platforms,” ASME Press.
13. Ester, M., Kriegel, H. P., Sander, J., and Xu, X., (1996), A density-based algorithm for discovering clusters in large spatial databases with noise, Proc. 2nd Int. Conf. On Knowledge Discovery and Data mining, Portland, OR, 226-231.
14. Falkenauer, E., (1998), “Genetic Algorithms and Grouping Problems,” Chichester: John Wiley & Sons.
15. Fisher, R. A., (1936), The use of multiple measurements in taxonomic problems, Annual Eugenics, 7, 179-188.
16. Gonzalez-Zugasti, J. P., Otto, K. N., and Baker, J. D., (2000), A method for architecting product platforms, Research in Engineering Design, 12, 61-72.
17. Griffin, A., and Hauser, J. H., (1993), The voice of the customer, Marketing Science, 12(1), 1-27.
18. Gu, P., and Sosale, S., (1999), Product modularization for life cycle engineering, Robotics and Computer Integrated-Manufacturing, 15, 387-401.
19. Hau, L. L., and Corey, B., (1994), Designing products and processes for postponement, in Management of design: Engineering and Management perspectives, Edited by Sriram Dasu and Charles Eastman, Kluwer Academic Publishers, pp.105-122.
20. He, D., Kusiak, A., and Tseng, T. L., (1998), Delayed product differentiation: a design and manufacturing perspective, Computer-Aided Design, 30(2), 105-113.
21. Hinneburg, A., and Keim, D., (1998), An efficient approach to clustering in large multimedia databases with noise, Proceeding of KDD Conference.
22. Holland, J. H., (1975), “Adaptation in Natural and Artificial Systems,” University of Michigan Press, Ann Arbor, MI.
23. Huang, C. C., (2000), Overview of modular product development, Proc. Natl. Sci. Counc. ROC(A), 24(3), 149-165.
24. Huang, C. C., and Liang, W. Y., (2001), A formalism for designing with modules, Journal of the Chinese Institute of Industrial Engineers, 18(3), 13-20.
25. Kaufman, L., and Rousseeuw, P. J., (1986), Clustering large data sets, in Pattern Recognition in Practice II, edited by E. S. Gelsema and L. N. Kanal, Elsevier/North-Holland, Amsterdam, pp.425-437.
26. Kaufman, L., and Rousseeuw, P. J., (1990), “Finding Groups in Data: an Introduction to Cluster Analysis”, John Wiley & Sons.
27. Krishna, K., and Murty, M. N., (1999), Genetic K-means algorithm, IEEE Transactions on Systems, Man, and Cybernetics--Part B: Cybernetics, 29(3), 433-439.
28. Kusiak, A., and Huang, C. C., (1996), Development of modular products, IEEE Transactions on Components, Packaging, and Manufacturing Technology-Part A,19(4), December, 523-538.
29. Lee, G. H., and Kusiak, A., (2001), The house of quality for design rule priority, The International Journal of Advanced Manufacturing Technology, 17, 288-296.
30. Lit, P. D., Falkenauer, E., and Delchambre., (2000), Grouping genetic algorithms: an efficient method to solve the cell formation problem, Mathematics and Computers in simulation, 51, 257-271.
31. Little, A., Wood, K., and McAdams, D., (1997), Functional Analysis: A Fundamental Empirical Study for Reverse Engineering, Benchmarking and Redesign. Proceedings of Design Engineering Technical Conferences, 97-DETC/DTM-3789, Sacramento, C.A., New York: American Society of Mechanical Engineers.
32. MacQueen, J., (1967), Some methods for classification and analysis of multivariate observations, 5th Berkeley Symp. Math. Statist. Prob., edited by L. Le Cam and J. Neyman, Vol.1, pp.281-297.
33. Maulik, U., and Bandyopadhyay, S., (2000), Genetic algorithm-based clustering technique, Pattern Recognition, 33, 1455-1465.
34. Mckay, A., Erens, F., and Bloor, M. S., (1996), Relating product definition and product variety, Research in Engineering Design, 2, 63-80.
35. Meyer, M. H., and Utterback, J. M., (1993), The product family and the dynamics of core capability, Sloan Management Review, 34(3), 29-47.
36. Michalewicz, Z., (1999), “Genetic Algorithms + Data structures = Evolution Programs,” Springer.
37. Moss, L., and Engelhardt., (1991), “Green Design: Design for the Environment,” Laurence King, pp.68-89.
38. Muffato, M., (1999), Introducing a platform strategy in product development, International Journal of Product Economics, 60-61, 145-153.
39. Muffatto, M., and Roveda, M., (2000), Developing product platforms: analysis of the development process, Technovation, 20, 617-630.
40. Ng, R. T., and Han, J., (1994), Efficient and effective clustering methods for spatial data mining, Proc. 20th Int. Conf. On Very Large Data Bases, Santiago, Chile, pp. 144-155.
41. Pahl, G., and Beitz, W., (1988), “Engineering Design: A Systematic Approach,” Springer-Verlag.
42. Pine, B. J. II., (1993) “Mass Customization: The New Frontier in Business Competition,” Harvard Business School Press, Boston.
43. Roberston, D., and Ulrich, K., (1998), Planning for product platforms, Sloan management Review, summer, 19-31.
44. Salhieh, S. M., and Kamrani, A. K., (1999), Macro level product development using design for modulariety, Robotics and Computer Integrated-Manufacturing, 15, 319-329.
45. Stone, R. B., Wood, K. L., and Crawford, R. H., (2000), A heuristic method for identifying modules for product architectures, Design Studies, 21, 5-31.
46. Ulrich, K. T., and Eppinger, S. D., (1995), “Product design and development,” New York: McGraw-Hill.
47. Ulrich, K.T., and Tung, K., 1991, Fundamentals of product modularity. Proceedings of the ASME Winter Annual Meeting Symposium on Issues in Design/ Manufacturing Integration (New York: American Society of Mechanical Engineers), pp.73-79.
48. Vinod, H., (1969), Integer Programming and the theory of grouping, J. Amer. Statist. Assoc. 64, 506-517.
49. Wang, J., (1999), Fuzzy outranking approach to prioritize design requirement in quality function deployment, International Journal of Production Research, 37(4), 896-916.
50. Zhang, T., Ramakrihnan, R., and Linvy, M., (1997), BIRCH: An efficient data clustering method for very large database, Data Mining and Knowledge Discovery, 1(2), 141-182.