Discrete optimization
Systems Modeling and Computer Simulation
Systems Modeling and Computer Simulation
Computer Performance Modeling Handbook
Computer Performance Modeling Handbook
Enabling Technologies for Simulation Science VI
Enabling Technologies for Simulation Science VI
Supply chain applications II: development of a high-level supply chain simulation model
Proceedings of the 33nd conference on Winter simulation
SpringSim '09 Proceedings of the 2009 Spring Simulation Multiconference
Selecting simualtion abstraction levels in simulation models of complex manufacturing systems
Proceedings of the Winter Simulation Conference
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Today's industrial and defense communities are increasingly reliant on the use simulation to reduce cost. At times, due to their stove-piped nature, these simulations themselves have resulted in a waste of both time and money with regard to future simulation development. Current trends address this problem by promoting the development of simulation infrastructures that are scalable, portable, and interoperable over a variety of paradigms. These infrastructures, such as HLA and SPEEDES, address cost issues by providing simulation infrastructures that promote model re-use by managing model interactions across diverse paradigms, improving scenario development, and allowing for a scalable distributed simulation capability.While these modern simulation infrastructures address many cost-related issues, they do not fully address issues related to model re-use. Simulations that utilize model reuse may result in large complex system models comprised of a diverse set of subsystem component models covering varying amounts of detail and fidelity. Often, a complex simulation that re-uses high fidelity subcomponent models may result in a more detailed system model than the simulation objective requires. Simulating such a system model results in a waste of simulation time with respect to addressing the simulation goals. These simulation costs, however, can be reduced through the use of abstract modeling techniques. These techniques can reduce the subcomponent model complexity by eliminating, grouping, or estimating model parameters or variables at a less detailed level without grossly affecting the simulation results. Key issues in the abstraction process involve identifying the variables or parameters than can be abstracted away for a given simulation objective and applying the proper abstraction technique to replace those parameters. This paper presents approaches for both identifying and replacing these candidate variables.