Research on multi-level design method of collaboration innovation based on rough sets theory

被引：1

作者：

Ma, Jiaqi ^{[1
]}

Yang, Yu ^{[1
]}

Li, Fei ^{[1
]}

机构：

[1] State Key Lab of Mechanical Transmission, Chongqing University, Chongqing

来源：

Journal of Convergence Information Technology | 2012年 / 7卷 / 03期

关键词：

Customer collaboration; Design method; Multi-level; Product innovation; Rough sets;

D O I：

10.4156/jcit.vol7.issue3.1

中图分类号：

学科分类号：

摘要：

Customer collaborative design is a complex innovation process, with participation of many innovation main bodies with different innovation knowledge and motivation. To solve the problem that their multi-level innovation demands being satisfied difficultly, based on Rough Sets Theory, a multi-level design method of product collaboration innovation has being proposed. The method can transform knowledge of innovation main bodies into multi-level product innovation design schemes, which can be helpful to satisfy the multi-level innovation demands. An example has being given to illustrate the feasibility and effectiveness of the proposed method.

引用

页码：1 / 8

页数：7

共 12 条

[1]

Fox J.L., Anglim J., Wilson J.R., Mental models, team mental models, and performance: Process, development, and future directions, Human Factors and Ergonomics in Manufacturing, 14, 4, pp. 331-352, (2007)

[2]

Thomke S.H., Hippel E.V., Customers as innovators:A new way to create value, Harvard Business Review, 80, 4, pp. 74-81, (2002)

[3]

Xiaolei W., Yang Y., Jie Y., Customer knowledge identify and application in collaborative product creative design, Journal of Chongqing University, 33, 2, pp. 52-56, (2010)

[4]

Franke N., Piller F., Value creation by toolkits for user innovation and design: The case of the watch market, Journal of Product Innovation, 21, 4, pp. 401-415, (2004)

[5]

Tseng M., Jiao J., Customizability analysis in design of mass customization, Computer Aided Design, 36, 8, pp. 745-757, (2004)

[6]

Jie Y., Yang Y., Customer's Kansei Cognition Model in Product Shape Design, Journal of Computer Aided Design & Computer Graphics, 22, 3, pp. 538-544, (2010)

[7]

Kzkan B.Y., Zkan G., Feyziogluo, Group decision making better respond customer needs in software development, Computers & Industrial Engineering, 48, 2, pp. 427-441, (2005)

[8]

Ping W., Songlin Z., Multidisciplinary Design Optimization of Vehicle Body Structure Based on Collaborative Optimization and Multi-objective Genetic Algorithm, Chinese Journal of Mechanical Engineering, 47, 2, pp. 102-108, (2011)

[9]

Pawla K.Z., Rough set approach to knowledge based decision support, European Journal of Operational Research, 99, 1, pp. 48-57, (1997)

[10]

Dai J., Qing X., Wang W., A Comparative Study on Strategies of Rule Induction for Incomplete Data Based on Rough Set Approach, IJACT: International Journal of Advancements in Computing Technology, 3, 3, pp. 176-183, (2011)

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