Allocation of manufacturers through internet-based collaboration for distributed process planning

The pursuit of lower cost, shorter time-to-market, and better quality has led to a shift toward global production in today's competitive business environment. This shift however, forces manufacturing enterprises to have separate design houses and manufacturing facilities. In general, design houses are located in the same regions as customers to enable them to respond to the rapidly changing demands of customers. By contrast, manufacturing facilities can be placed in regions in which production costs are lower. However, this physical and logical separation between designers and manufacturers (or between upstream manufacturers and downstream manufacturers) raises various integration issues. The present paper addresses two of these issues: the framework for representing the data necessary to communicate requirements and objectives of the designer, and the methodology for utilizing such data to optimize the business objectives related to production cost and quality. The proposed representation, collaboration framework, and methodology will enable design houses and manufacturing facilities to realize the benefits of global production and to accommodate the management of loosely integrated supply chains.

[1]  Nenad Ivezic,et al.  Integrated Product and Process Data for B2B Collaboration | NIST , 2003 .

[2]  Claude Godart,et al.  A model to support collaborative work in virtual enterprises , 2003, Data Knowl. Eng..

[3]  George Harhalakis,et al.  Cell controllers: Analysis and comparison of three major projects , 1991 .

[4]  Fei Renyuan,et al.  Study of an intelligent micro-manipulator , 2003 .

[5]  Hyunbo Cho,et al.  A semantic web service framework to support intelligent distributed manufacturing , 2005, Int. J. Knowl. Based Intell. Eng. Syst..

[6]  Hyunbo Cho,et al.  Intelligent workstation controller for computer-integrated manufacturing: Problems and models , 1995 .

[7]  Boonserm Kulvatunyou,et al.  Integrated product and process data for business to business collaboration , 2003, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[8]  Katta G. Murty,et al.  Operations Research: Deterministic Optimization Models , 1994 .

[9]  Qi Zhang Liu,et al.  Collaborative Model and Algorithms for Supporting Real-Time Distribution Logistics Systems , 2000, Electron. Notes Discret. Math..

[10]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[11]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[12]  Wang Xiankui,et al.  Research on manufacturing resource modeling based on the O–O method , 2003 .

[13]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[14]  Hans Weigand,et al.  Modelling electronic commerce transactions - A layered approach , 1998 .

[15]  Hyunbo Cho,et al.  Adaptive and dynamic process planning using neural networks , 2001 .

[16]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[17]  Weiming Shen,et al.  A multi-resolution collaborative architecture for web-centric global manufacturing , 2000, Inf. Sci..

[18]  조현보 Adaptive and dynamic process planning using neural networks , 1999 .

[19]  Petia Wohed,et al.  Collaborative process patterns for e-Business , 2001, SIGG.

[20]  SonYoung Jun,et al.  A semantic web service framework to support intelligent distributed manufacturing , 2005 .