Article

Symbiotic Simulation Systems: An Extended Definition Motivated by Symbiosis in Biology

Authors:

Stephen John Turner,

Malcolm Yoke Hean LowAuthors Info & Claims

PADS '08: Proceedings of the 22nd Workshop on Principles of Advanced and Distributed Simulation

Pages 109 - 116

https://doi.org/10.1109/PADS.2008.17

Published: 03 June 2008 Publication History

Abstract

Although various forms of symbiosis are known in biology, only mutualism has been considered in the context of symbiotic simulation systems. In this paper, we explain why the original definition of symbiotic simulation systems is narrow and why it is important to consider other forms of symbiosis as well. As a consequence we propose an extended definition of symbiotic simulation systems motivated by symbiosis in biology. By using this extended definition, we identify five different types of symbiotic simulation systems which can be applied in various applications. We describe how single systems can be combined and propose a hybrid symbiotic simulation system in the context of semiconductor manufacturing.

References

[1]

V. Akcelik, J. Bielak, G. Biros, I. Epanomeritakis, O. Ghattas, L. F. Kallivokas, and E. J. Kim. A framework for on-line inversion-based 3D site characterization. In Proceedings of the International Conference on Computational Science , pages 717-724, 2004.

[2]

S. Annan and J. Banks. Design of a knowledge-based on-line simulation system to control a manufacturing shop floor. IIE Transactions, 24(3):72-83, 1992.

[3]

H. Aydt, S. J. Turner, W. Cai, and M. Y. H. Low. An agent-based generic framework for symbiotic simulation systems. In A. M. Uhrmacher and D. Weyns, editors, Agents, Simulation and Applications (to appear). Taylor & Francis, 2008.

[4]

H. Aydt, S. J. Turner, W. Cai, M. Y. H. Low, P. Lendermann, and B. P. Gan. Symbiotic simulation control in semiconductor manufacturing. In Proceedings of the International Conference on Computational Science (to appear), 2008.

Digital Library

[5]

J. Cortial, C. Farhat, L. J. Guibas, and M. Rajashekhar. Compressed sensing and time-parallel reduced-order modeling for structural health monitoring using a DDDAS. In Proceedings of the International Conference on Computational Science, pages 1171-1179, 2007.

Digital Library

[6]

W. Davis. Handbook of simulation. In J. Banks, editor, On-Line Simulation: Need and Evolving Research Requirements , pages 465-516. Wiley-Interscience, 1998.

[7]

A. de Bary. Die Erscheinung der Symbiose. Verlag von Karl J. Trübner, Strasbourg, 1879.

[8]

A. Douglas. Symbiotic Interactions. Oxford University Press, 1994.

[9]

C. C. Douglas, J. D. Beezley, J. L. Coen, D. Li, W. Li, A. K. Mandel, J. Mandel, G. Qin, and A. Vodacek. Demonstrating the validity of a wildfire DDDAS. In Proceedings of the International Conference on Computational Science, pages 522-529, 2006.

Digital Library

[10]

G. R. Drake and J. S. Smith. Simulation system for real-time planning, scheduling, and control. In Proceedings of the Winter Simulation Conference, pages 1083-1090, 1996.

Digital Library

[11]

H. ElMaraghy, I. Abdallah, and W. ElMaraghy. On-line simulation and control in manufacturing systems. CIRP Annals-Manufacturing Technology, 47(1):401-404, 1998.

[12]

R. Fujimoto, D. Lunceford, E. Page, and A. M. U. (editors). Grand challenges for modeling and simulation: Dagstuhl report. Technical Report 350, Schloss Dagstuhl. Seminar No 02351, August 2002.

[13]

R. M. Fujimoto. Parallel and Distributed Simulation Systems . Wiley Series on Parallel and Distributed Computing. John Wiley & Sons, Inc., New York, NY, USA, 2000.

Digital Library

[14]

R. M. Fujimoto, R. Guensler, M. Hunter, H. K. Kim, J. Lee, J. Leonard, M. Palekar, K. Schwan, and B. Seshasayee. Dynamic data driven application simulation of surface transportation systems. In Proceedings of the International Conference on Computational Science, pages 425-432, 2006.

Digital Library

[15]

F. Kamrani. Using on-line simulation in UAV path planning. Licentiate Thesis in Electronics and Computer Systems, KTH, Stockholm, Sweden, 2007.

[16]

F. Kamrani and R. Ayani. Using on-line simulation for adaptive path planning of UAVs. In Proceedings of the 11th IEEE International Symposium on Distributed Simulation and Real-time Applications, pages 167-174, Chania, Greece, October 2007.

Digital Library

[17]

D. Katz and S. Manivannan. Exception management on a shop floor using online simulation. In Proceedings of the Winter Simulation Conference, pages 888-896, 1993.

Digital Library

[18]

C. Kennedy, G. K. Theodoropoulos, V. Sorge, E. Ferrari, P. Lee, and C. Skelcher. AIMSS: An architecture for data driven simulations in the social sciences. In Proceedings of the International Conference on Computational Science, pages 1098-1105, 2007.

Digital Library

[19]

D. Knight, Q. Ma, T. Rossman, and Y. Jaluria. Evaluation of fluid-thermal systems by dynamic data driven application systems - part ii. In Proceedings of the International Conference on Computational Science, pages 1189-1196, 2007.

Digital Library

[20]

M. Y. H. Low, K. W. Lye, P. Lendermann, S. J. Turner, R. T. W. Chim, and S. H. Leo. An agent-based approach for managing symbiotic simulation of semiconductor assembly and test operation. In Proceedings of the 4th International Joint Conference on Autonomous Agents and Multi-agent Systems, pages 85-92, New York, NY, USA, 2005. ACM Press.

Digital Library

[21]

G. R. Madey, A.-L. Barabási, N. V. Chawla, M. Gonzalez, D. Hachen, B. Lantz, A. Pawling, T. Schoenharl, G. Szabó, P. Wang, and P. Yan. Enhanced situational awareness: Application of DDDAS concepts to emergency and disaster management. In Proceedings of the International Conference on Computational Science, pages 1090-1097, 2007.

Digital Library

[22]

K. Mahinthakumar, G. von Laszewski, S. R. Ranjithan, D. Brill, J. Uber, K. Harrison, S. Sreepathi, and E. M. Zechman. An adaptive cyberinfrastructure for threat management in urban water distribution systems. In Proceedings of the International Conference on Computational Science, pages 401-408, 2006.

Digital Library

[23]

A. Majumdar, A. Birnbaum, D. J. Choi, A. Trivedi, S. K. Warfield, K. Baldridge, and P. Krysl. A dynamic data driven grid system for intra-operative image guided neurosurgery. In Proceedings of the International Conference on Computational Science, pages 672-679, 2005.

Digital Library

[24]

J. Mandel, J. D. Beezley, L. S. Bennethum, S. Chakraborty, J. L. Coen, C. C. Douglas, J. Hatcher, M. Kim, and A. Vodacek. A dynamic data driven wildland fire model. In Proceedings of the International Conference on Computational Science, pages 1042-1049, 2007.

Digital Library

[25]

J. Mandel, M. Chen, L. P. Franca, C. J. Johns, A. Puhalskii, J. L. Coen, C. C. Douglas, R. Kremens, A. Vodacek, and W. Zhao. A note on dynamic data driven wildfire modeling. In Proceedings of the International Conference on Computational Science, pages 725-731, 2004.

[26]

J. D. McCalley, V. Honavar, S. M. Ryan, W. Q. Meeker, D. Qiao, R. A. Roberts, Y. Li, J. Pathak, M. Ye, and Y. Hong. Integrated decision algorithms for autosteered electric transmission system asset management. In Proceedings of the International Conference on Computational Science , pages 1066-1073, 2007.

Digital Library

[27]

J. D. McCalley, V. Honavar, S. M. Ryan, W. Q. Meeker, R. A. Roberts, D. Qiao, and Y. Li. Auto-steered information-decision processes for electric system asset management. In Proceedings of the International Conference on Computational Science, pages 440-447, 2006.

Digital Library

[28]

J. Michopoulos, P. Tsompanopoulou, E. N. Houstis, and A. Joshi. Agent-based simulation of data-driven fire propagation dynamics. In Proceedings of the International Conference on Computational Science, pages 732-739, 2004.

[29]

J. Michopoulos, P. Tsompanopoulou, E. N. Houstis, J. R. Rice, C. Farhat, M. Lesoinne, and F. Lechenault. DDEMA: A data driven environment for multiphysics applications. In Proceedings of the International Conference on Computational Science, pages 309-318, 2003.

Digital Library

[30]

National Science Foundation. DDDAS: Dynamic data driven applications systems. Program Solicitation 05-570, http://www.nsf.gov/pubs/2005/nsf05570/ nsf05570.htm, 2005.

[31]

S. Paracer and V. Ahmadjian. Symbiosis: An Introduction to Biological Associations. Oxford University Press US, 2000.

[32]

K. Perumalla, R. Fujimoto, T. McLean, and G. Riley. Experiences applying parallel and interoperable network simulation techniques in on-line simulations of military networks. In Proceedings of the 16th Workshop on Parallel and Distributed Simulation, pages 88-95, 2002.

Digital Library

[33]

J. Potoradi, O. Boon, J. Fowler, M. Pfund, and S. Mason. Using simulation-based scheduling to maximize demand fulfillment in a semiconductor assembly facility. In Proceedings of the Winter Simulation Conference, pages 1857-1861, 2002.

Digital Library

[34]

W. Scholl and J. Domaschke. Implementation of modeling and simulation in semiconductor wafer fabrication with time constraints between wet etch and furnace operations. IEEE Transactions on Semiconductor Manufacturing, 13(3):273- 277, August 2000.

[35]

D. Wilkinson. At cross purposes. Nature, 412(6846):485, 2001.

[36]

L. Xue, Z. Hao, D. Li, and X. Liu. Dongting lake floodwater diversion and storage modeling and control architecture based on the next generation network. In Proceedings of the International Conference on Computational Science, pages 841-848, 2007.

Digital Library

[37]

T. Ye, D. Harrison, B. Mo, B. Sikdar, H. Kaur, S. Kalyanaraman, B. Szymanski, and K. Vastola. Traffic management and network control using collaborative on-line simulation. In Proceedings of the IEEE International Conference on Communications , pages 204-209, 2001.

Cited By

Matta ALugaresi GRossetti MHunter S(2024)An Introduction to Digital TwinsProceedings of the Winter Simulation Conference10.5555/3712729.3712836(1281-1295)Online publication date: 15-Dec-2024
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Cao YCurrie COnggo BHiggins M(2021)Simulation optimization for a digital twin using a multi-fidelity frameworkProceedings of the Winter Simulation Conference10.5555/3522802.3523002(1-12)Online publication date: 13-Dec-2021
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Clemen TAhmady-Moghaddam NLenfers UOcker FOsterholz DStröbele JGlake DDiallo STolk AGiabbanelli P(2021)Multi-Agent Systems and Digital Twins for Smarter CitiesProceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/3437959.3459254(45-55)Online publication date: 21-May-2021
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Index Terms

Symbiotic Simulation Systems: An Extended Definition Motivated by Symbiosis in Biology
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies
    2. Simulation types and techniques
      1. Distributed simulation

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Published In

cover image ACM Conferences

PADS '08: Proceedings of the 22nd Workshop on Principles of Advanced and Distributed Simulation

June 2008

196 pages

ISBN:9780769531595

Sponsors

SIGSIM: ACM Special Interest Group on Simulation and Modeling

Publisher

IEEE Computer Society

United States

Publication History

Published: 03 June 2008

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Symbiotic Simulation

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PADS08

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SIGSIM

PADS08: PADS '08 - Principles of Advanced and Distributed Simulation

June 3 - 6, 2008

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PADS '08 Paper Acceptance Rate 21 of 52 submissions, 40%;

Overall Acceptance Rate 398 of 779 submissions, 51%

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Cited By

Matta ALugaresi GRossetti MHunter S(2024)An Introduction to Digital TwinsProceedings of the Winter Simulation Conference10.5555/3712729.3712836(1281-1295)Online publication date: 15-Dec-2024
https://dl.acm.org/doi/10.5555/3712729.3712836
Cao YCurrie COnggo BHiggins M(2021)Simulation optimization for a digital twin using a multi-fidelity frameworkProceedings of the Winter Simulation Conference10.5555/3522802.3523002(1-12)Online publication date: 13-Dec-2021
https://dl.acm.org/doi/10.5555/3522802.3523002
Clemen TAhmady-Moghaddam NLenfers UOcker FOsterholz DStröbele JGlake DDiallo STolk AGiabbanelli P(2021)Multi-Agent Systems and Digital Twins for Smarter CitiesProceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/3437959.3459254(45-55)Online publication date: 21-May-2021
https://dl.acm.org/doi/10.1145/3437959.3459254
Onggo BMustafee NSmart AJuan AMolloy OJohansson BJain S(2018)Symbiotic simulation systemProceedings of the 2018 Winter Simulation Conference10.5555/3320516.3320685(1358-1369)Online publication date: 9-Dec-2018
https://dl.acm.org/doi/10.5555/3320516.3320685
Klinger VBohlmann SSzczerbicka H(2017)Identification of motion-based action potentials in neural bundles using a continuous symbiotic systemProceedings of the Symposium on Modeling and Simulation in Medicine10.5555/3108760.3108761(1-12)Online publication date: 23-Apr-2017
https://dl.acm.org/doi/10.5555/3108760.3108761
Bohlmann SKlinger VSzczerbicka H(2017)Integration of a physical system, machine learning, simulation, validation and control systems towards symbiotic model engineeringProceedings of the Symposium on Modeling and Simulation of Complexity in Intelligent, Adaptive and Autonomous Systems10.5555/3108414.3108416(1-12)Online publication date: 23-Apr-2017
https://dl.acm.org/doi/10.5555/3108414.3108416
Yang CChi PSong XLin TLi BChai X(2016)An efficient approach to collaborative simulation of variable structure systems on multi-core machinesCluster Computing10.1007/s10586-015-0498-919:1(29-46)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1007/s10586-015-0498-9
Tjahjono BJiang X(2015)Linking symbiotic simulation to enterprise systemsProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2888714(823-834)Online publication date: 6-Dec-2015
https://dl.acm.org/doi/10.5555/2888619.2888714
Stelzer SRose OUhrmacher A(2012)Generation of alternatives for model predictive control in manufacturing systemsProceedings of the Winter Simulation Conference10.5555/2429759.2430224(1-2)Online publication date: 9-Dec-2012
https://dl.acm.org/doi/10.5555/2429759.2430224
Stelzer SRose OUhrmacher A(2012)Generation of alternatives for model predictive control in manufacturing systemsProceedings of the Winter Simulation Conference10.5555/2429759.2430213(1-2)Online publication date: 9-Dec-2012
https://dl.acm.org/doi/10.5555/2429759.2430213
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