Various Types of Simulations

Discrete Event Simulations

... are computational models of real-world systems conceived as discrete dynamic systems by representing their state with the help of state variables (in the form of attributes of object types), and capturing their dynamics by modeling the events that are responsible for their state changes. Typically, they use next-event time progression (as opposed to fixed-increment time progression).

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Processing Network Models

... are an important class of Discrete Event Simulations, where objects enter a system via arrival events at an entry node and then flow through one or more processing nodes (representing, e.g., sevice desks or manufacturing machines) where they are subject to processing activities before they leave the system at an exit node via departure events. These models are the focus of most commercial simulation tools, such as Arena, Simio and AnyLogic.

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Grid Space Models

... are widely used in the social sciences, and are often implemented with the simulation programming framework NetLogo. They use the two-dimensional discrete Euclidean space, called grid space, for visualizing simulation runs. Typically, they use discrete simulation time and fixed-increment time progression (as opposed to next-event time progression). A grid space model that does neither define objects nor events, but only grid cell states and grid cell state changes based on the states of neighbor cells, may be considered a Cellular Automata model.

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Agent-Based Modeling and Simulation

The term "agent-based modeling" is an umbrella term that subsumes many different approaches to simulation, typically focused on modeling (collections of) entities/objects/individuals and their interactions with each other and with their environment.

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Continuous Simulations

... are mainly used in science and enginering. They model the spatial structure and continuous state changes of objects and matter. They use continuous simulation time with fixed-increment time progression.

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Hybrid Simulations

... model dynamic systems the state of which is subject to both discrete and continuous changes. They use a combination of next-event and fixed-increment time progression.

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