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Abstract
Discrete-event simulation is a key technology for analyzing and improving organizational performance. Over time, various software packages have evolved that greatly facilitate the development and analysis of discrete-event simulation models. However, each of these applications uses very different approaches for representing the behavior of the systems being studied. As a result of this disparity in approaches and implementations, models developed in different applications cannot interact, thus creating "islands" of modeling and analysis tools. Based on the simulation modeling process, there are two basic opportunities for models to interact - at the model formulation stage (conceptual models) and at the model application stage (programmed models). In this paper, we describe the type of interactions that can exist at each stage and provide a framework for enabling disparate discrete-event simulation model interoperability. The framework defines basic simulation model elements and provides a structure, in the form of an entity-relationship diagram that defines the relationships among the elements. The neutral schema that results from the structure provides the basis for enhanced model understanding at the conceptual stage and enables model translation at the application stage.
Keywords
Simulation, Framework, Interoperability, Integration
1. Introduction
Discrete-event simulation is becoming an important aid to help decision makers at all levels in the organization deal with the complex, highly-interdependent, dynamic, and stochastic processes that exist in most organizations today. It is a highly effective tool for making both strategic decisions (e.g. capital equipment purchases, facilities design) and operational or tactical decision (e.g. resource allocation, processing rules and policies). In fact, according to the Oak Ridge Centers for Manufacturing Technologies [1], "modeling and simulation are emerging as key technologies to support manufacturing in the 21st century, and no other technology offers more than a fraction of the potential that modeling and simulation does for improving products, perfecting processes, reducing design-to-manufacturing cycle time, and reducing product realization costs."
Over time, various software packages have evolved that greatly facilitate the development and analysis of discreteevent simulation models. However, each of these applications uses very different approaches and terminologies for representing and describing the behavior of the systems being studied. For example, a machine is referred to as a "location" in ProModel®, "machine" in QUEST®, and "processor" in Flexsim®. As a result of this disparity...