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On the performance of inter-organizational design optimization systems

Published: 03 December 2006 Publication History

Abstract

Simulation-based design optimization is a key technology in many industrial sectors. Recent developments in software technology have opened a novel range of possibilities in this area. It has now become possible to involve multiple organizations in the simulation of a candidate design, by composing their respective simulation modules on the Internet. Thus, it is possible to deploy an inter-organizational design optimization system, which may be particularly appealing because modern engineering products are assembled out of smaller blocks developed by different organizations. In this paper we explore some of the fundamental performancerelated issues involved in such a novel scenario, by analyzing a variety of options: centralized control vs. distributed control; generation of new candidate designs one at a time or in batches; communication and computation performed serially or with time overlap. Our analysis provides useful insights into the numerous trade-offs involved in the implementation of inter-organizational design optimization.

References

[1]
Aalst, W. 1998. The application of Petri nets to workflow management. The Journal of Circuits, Systems and Computers 8 (1): 21--66.
[2]
Alonso, G., F. Casati, H. Kuno, and V. Machiraju. 2004. Web services: Concepts, architectures and applications. Springer Verlag.
[3]
April, J., F. Glover, J. P. Kelly, and M. Laguna. 2003. Simulation-based optimization: practical introduction to simulation optimization. In Proceedings of the Winter Simulation Conference, 71--78.
[4]
Banks, J., J. S. Carson, B. L. Nelson, and D. M. Nicol. 2005. Discrete---event system simulation. 4th ed. Upper Sadder River, New Jersey: Prentice-Hall.
[5]
Bose, R., and J. Frew. 2005. Lineage retrieval for scientific data processing: a survey. ACM Computing Surveys 37 (1): 1--28.
[6]
Botros, K., D. Sennhauser, K. Jungowski, G. Poissant, H. Golshan, and J. Stoffregen. 2004. Effects of dynamic penalty parameters on the convergence of moga in optimization of a large gas pipeline network. In 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference.
[7]
Boulougouris, E. K., A. D. Papanikolaou, and G. Zaraphonitis. 2004. Optimization of arrangements of ro-ro passenger ships with genetic algorithms. Ship Technology Research 51 (3): 99--105.
[8]
Carson, Y., and A. Maria. 1997. Simulation optimization: methods and applications. In Proceedings of the Winter Simulation Conference, 118--126.
[9]
DESMO-J. 2000. The desmo---j homepage. <http://www.desmoj.de>.
[10]
Fu, Y., B. Kachnowski, and E. Lee. 2004. Occupant model correlation using a multiobjective evolution strategy. In 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, New York, August 30--31.
[11]
Gaiddon, A., D. D. Knight, and C. Poloni. 2004. Multicriteria design optimization of a supersonic inlet based upon global missile performance. Journal of Propulsion and Power 20 (3): 542--558.
[12]
Giassi, A., F. Bennis, and J. J. Maisonneuve. 2004. Multidisciplinary design optimisation and robust design approaches applied to concurrent design. Structural and Multidisciplinary Optimization 28 (5): 356--371.
[13]
Gottschalk, K., S. Graham, H. Kreger, and J. Snell. 2002. Introduction to web services architecture. IBM Systems Journal 41 (2): 170--177.
[14]
Hepp, E., O. Lohne, and S. Sannes. 2003, November. Extended casting simulation for improved magnesium die casting. In DGM 6th International Conference on Magnesium Alloys and Their Applications, 669--674. Wolfsburg, Germany.
[15]
Johnston, W. E. 2004. Semantic services for grid-based, large-scale science. IEEE Intelligent Systems 19 (1): 34--39.
[16]
Ludäscher, B., I. Altintas, C. Berkley, D. Higgins, E. Jaeger, M. Jones, E. A. Lee, J. Tao, and Y. Zhao. 2005. Scientific workflow management and the kepler system. Concurrency & Computation: Practice & Experience. To appear.
[17]
Maisonneuve, J. J., S. Harries, J. Marzi, H. C. Raven, U. Viviani, and H. Piippo. 2003. Towards optimal design of ship hull shapes. In Proceedings of the 8th International Marine Design Conference, 31--42.
[18]
Miettinen, K. M. 1998. Nonlinear multiobjective optimization, Volume 12 of International Series in Operations Research & Management Science. Springer Verlag.
[19]
Papazoglou, M. P., and D. Georgakopoulos. 2003. Service---oriented computing: Introduction. Communications of the ACM 46 (10): 24--28.
[20]
Quagliarella, D., J. Périaux, C. Poloni, and G. Winter. (Eds.) 1997. Genetic algorithms and evolution strategies in engineering and computer science. West Sussex, England: John Wiley and Sons.
[21]
Stal, M. 2002. Web services: beyond component-based computing. Communications of the ACM 45 (10): 71--76.
[22]
Swisher, J., P. Hyden, S. Jacobson, and L. Scruben. 2000. A survey of simulation optimization techniques and procedures. In Proceedings of the 2000 Winter Simulation Conference.
[23]
Taga, I., A. Funakubo, and Y. Fukui. 2005. Design and development of an artificial implantable lung using multiobjective genetic algorithm: Evaluation of gas exchange performance. ASAIO Journal 51 (1): 92--102.
[24]
Vavak, F., and T. Fogarty. 1996. Comparison of steady state and generational genetic algorithms for use in nonstationary environments. In Proceedings of the 1996 IEEE Conference on Evolutionary Computation: IEEE Press.
[25]
Workflow Management Coalition 1999. Workflow management coalition terminology & glossary. Workflow Management Coalition. Document No. WFMC-TC-1011.3.
  1. On the performance of inter-organizational design optimization systems

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    cover image ACM Conferences
    WSC '06: Proceedings of the 38th conference on Winter simulation
    December 2006
    2429 pages
    ISBN:1424405017

    Sponsors

    • IIE: Institute of Industrial Engineers
    • ASA: American Statistical Association
    • IEICE ESS: Institute of Electronics, Information and Communication Engineers, Engineering Sciences Society
    • IEEE-CS\DATC: The IEEE Computer Society
    • SIGSIM: ACM Special Interest Group on Simulation and Modeling
    • NIST: National Institute of Standards and Technology
    • (SCS): The Society for Modeling and Simulation International
    • INFORMS-CS: Institute for Operations Research and the Management Sciences-College on Simulation

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    Published: 03 December 2006

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    • ASA
    • IEICE ESS
    • IEEE-CS\DATC
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    • NIST
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    • INFORMS-CS
    WSC06: Winter Simulation Conference 2006
    December 3 - 6, 2006
    California, Monterey

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    WSC '06 Paper Acceptance Rate 177 of 252 submissions, 70%;
    Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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