Advertisement

Extensible multi-agent system for optimal design of complex systems using analytical target cascading

  • George Q. HuangEmail author
  • T. Qu
  • David W. L. Cheung
  • L. Liang
Original Article

Abstract

Analytical target cascading (ATC) has emerged as an integrated approach and methodology for optimal design of complex hierarchical systems with good convergence. It facilitates collaborative design problem solving and enables distributed computing to extend both the capability and capacity. ATC is very powerful yet requiring comprehensive understanding and efforts in setting up specific projects. This paper presents a generic and extensible information infrastructure called atcPortal in the form of a web portal where the ATC analyst defines the ATC problem using a special-purpose XML-based language called atcXML, conducts the ATC analysis, and obtains the analytical results. atcPortal not only reduces the overheads for analysts to set up the projects for practical applications, but also allows the researchers to experiment and extend the ATC methods under different system structures and/or different ATC strategies.

Keywords

Analytical target cascading Target optimization Web portal Web services XML Hierarchical system modeling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

The authors are most grateful to the Hong Kong University Committee on Research and Conference Grants, William Mong Engineering Fund and NSFC (Grant No: 70371023) for financial support that made this research possible.

References

  1. 1.
    Choudhary R, Malkawi A, Papalambros PY (2003) A hierarchical design optimization framework for building performance analysis. Proceedings of the 8th international building performance simulation association (IBPSA) conference, Eindhoven, Netherlands, 11–14 August 2003Google Scholar
  2. 2.
    Etman LFP, Kokkolaras M, Papalambros PY, Hofkamp AT, Rooda JE (2002) Coordination specification for analytical target cascading using the Chi language. Proceedings of the 9th AIAA/ISSMO symposium on multidisciplinary analysis and optimization, work in progress, AIAA–2002–5637, Atlanta, Georgia, USA, 4–6 September 2002Google Scholar
  3. 3.
    Fellini R, Kim HM, Kokkolaras M, Michelena N, Papalambros PY (2001) Target cascading for design of product families. Proceedings of the 4th congress on structural and multidisciplinary optimization, Dalian, China, 4–8 June 2001Google Scholar
  4. 4.
    Ge P, Lu S C-Y, Suh NP (2002) An axiomatic approach for target cascading of parametric design of engineering systems. Ann CIRP 51(1):111–114CrossRefGoogle Scholar
  5. 5.
    Kim HM, Kokkolaras M, Louca L, Delagrammatikas G, Michelena N, Filipi Z, Papalambros P, Assanis D (2002) Target cascading in vehicle redesign: a class VI truck study. Int J Veh Des 29(3):199–225CrossRefGoogle Scholar
  6. 6.
    Kim HM, Michelena NF, Papalambros PY, Jiang T (2000) Target cascading in optimal system design. Proceedings of the 2000 ASME design automation conference, DAC paper 14253, Baltimore, 10–13 September 2000Google Scholar
  7. 7.
    Kim HM, Rideout DG, Papalambros PY, Stein JL (2001) Analytical target cascading in automotive vehicle design. Proceedings of the 2001 ASME design automation conference, DAC–21079, Pittsburgh, Pennsylvania, USA, 9–12 September 2001Google Scholar
  8. 8.
    Kim HM (2001) Target cascading in optimal system design. PhD Dissertation, Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, USAGoogle Scholar
  9. 9.
    Louca LS, Kokkolaras M, Delagrammatikas GJ, Michelena NF, Filipi ZS, Papalambros PY, Assanis DN (2002) Analytical target cascading for the design of an advanced technology heavy truck. Proceedings of the ASME international mechanical engineering congress and exposition, New Orleans, Louisiana, 17–22 November 2002Google Scholar
  10. 10.
    Mahmoud HA, Kabamba PT, Ulsoy AG, Brusher GA (2002) Target reduction and balancing using system norms. Proceedings of the American control conference, Anchorage, AK, May 2002Google Scholar
  11. 11.
    Mahmoud HA, Kabamba PT, Ulsoy AG, Brusher GA (2003) Ranking subsystem targets according to their influence on system performance. Proceedings of the American control conference, Denver, CO, June 2003Google Scholar
  12. 12.
    Michelena N, Park H, Papalambros P (2003) Convergence properties of analytical target cascading. AIAA J 41:897–905Google Scholar
  13. 13.
    Nyström M, Larsson T, Karlsson L, Kokkolaras M, Papalambros PY (2003) Linking analytical target cascading to engineering information systems for simulation-based optimal vehicle design. ICED 03, 14th international conference on engineering design, research for practice-innovative products, processes and organisations, 19–21 August 2003, Stockholm, SwedenGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2005

Authors and Affiliations

  • George Q. Huang
    • 1
    Email author
  • T. Qu
    • 1
  • David W. L. Cheung
    • 2
  • L. Liang
    • 3
  1. 1.Department of Industrial and Manufacturing Systems EngineeringThe University of Hong KongHong KongPeople's Republic of China
  2. 2.Department of Computer ScienceThe University of Hong KongHong KongPeople's Republic of China
  3. 3.Business SchoolUniversity of Science and Technology of ChinaHong KongPeople's Republic of China

Personalised recommendations