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A multiresolution simulation system and simulation development processes

Author

Listed:
  • Young-Jun Jee
  • Tae-Gyung Lee
  • Sang-Ho Park
  • Jun-Ho Cho
  • Hee-Soo Kim
  • Tae-Eog Lee

Abstract

Defense modeling and simulation often should run multiple simulation models together to share information on their combat entity states and battlefields and to interact with each other. However, most simulation models were developed by various parties with different purposes, modeling views, and model resolutions. Therefore, it has been challenging to identify differences between the models, transform one’s information for the other models, and manage interoperation between the models. There have been numerous works on multiresolution modeling (MRM) and simulation. However, we yet should have more systematic and integrated methods for designing, managing, and executing the interoperation processes of different simulation models. In this paper, we propose an integrated multiresolution simulation system for designing, developing, simulating, and managing multiple simulation models with different modeling views and resolutions. We propose an architecture, component functions, and processes, including model resolution conversion and management processes, and their Unified Modeling Language models. We also extend the Distributed Simulation Engineering and Execution Processes standard to incorporate MRM design and development processes. Finally, we present a case of MRM design and development of war game models by using the proposed system.

Suggested Citation

  • Young-Jun Jee & Tae-Gyung Lee & Sang-Ho Park & Jun-Ho Cho & Hee-Soo Kim & Tae-Eog Lee, 2022. "A multiresolution simulation system and simulation development processes," The Journal of Defense Modeling and Simulation, , vol. 19(3), pages 325-338, July.
  • Handle: RePEc:sae:joudef:v:19:y:2022:i:3:p:325-338
    DOI: 10.1177/1548512920966107
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    References listed on IDEAS

    as
    1. Woo-Seop Yun & Il-Chul Moon & Tae-Eog Lee, 2015. "Agent-Based Simulation of Time to Decide: Military Commands and Time Delays," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(4), pages 1-10.
    2. Donghyun Kim & Hyungil Moon & Donghyun Park & Hayong Shin, 2017. "An efficient approximate solution for stochastic Lanchester models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(11), pages 1470-1481, November.
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