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Micro Saint Sharp
discrete-event network simulation software

Micro Saint Sharp is a discrete-event network simulation software package for building models that simulate real-life processes. With Micro Saint Sharp models, you can gain useful information about processes that might be too expensive or time-consuming to test in the real world.Micro Saint Sharp does not use the terminology or graphic representations of a specific industry. You can model any process as a network of tasks that can be represented in a flow chart diagram. Its intuitive graphical user interface and flow chart approach to modeling make it easy to use by generalists as well as simulation experts. Micro Saint Sharp is a powerful discrete event simulation tool that allows you to quickly build models.Micro Saint Sharp's power, flexibility and tools for optimization make it the simulation tool of choice for any organization.
Applications
Human Factors and Ergonomics, Manufacturing Control, Management, Military, Psychology Research, etc.
Features
• A palette of tools for drawing your task network on the screen display.
• Task description windows that you can open from the network diagram or Tree View.
• Drop-down menus for defining characteristics such as task time distributions, routing decision types, and queue order.
• Built-in system variables and the ability to add custom variables.
• Built-in functions that you can call from expressions in your model!and the ability to add custom functions.
 
• Data collection snapshots that you can define to be triggered by clock times or model events.
• Scenario events that you can schedule to occur at specific clock times to change the values of variables or trigger tasks.
• Symbolic animation of the network diagram during model execution.
• 2D image animation of the model using your own custom background and images of your choice.
• 3D animation of the model using 3D objects, 2D images, billboards, 3D lines, comments, and different camera views.
• Automatic creation of static and dynamic charts from variable data collected during model runs.
• Automatic generation of certain performance measures regarding queues and tasks. Examples are minimum and maximum task times, average wait times and queue length over time.
• Syntax checker that searches the model for errors in syntax and displays the errors.
• Inter operability so you can exchange data with other software applications.
 
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