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Modeling and Simulation By Lecturer: Nada Ahmed

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Introduction to simulation and Modeling

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What is Modeling Modeling: is the process of producing a model A model mean some thing which represents a system in a form which allow us to make predictions about the behavior of the system. A model is similar to but simpler than the system it represents.

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Purpose of modeling One purpose of a model is to enable the analyst to predict the effect of changes to the system. a model intended for a simulation study is a mathematical model developed with the help of simulation software. Mathematical model classifications include deterministic (input and output variables are fixed values) or stochastic (at least one of the input or output variables is probabilistic)

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Properties of a model a model should be a close approximation to the real system and incorporate most of its salient features. it should not be so complex that it is impossible to understand and experiment with it. A good model is a judicious tradeoff between realism and simplicity.

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WHAT IS SIMULATION? A simulation of a system is the operation of a model of the system. simulation is a tool to evaluate the performance of a system, existing or proposed, under different configurations of interest and over long periods of real time.

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Why and when to use simulation Simulation is used before an existing system is altered or a new system built, to reduce the chances of failure to meet specifications, to eliminate unforeseen bottlenecks, to prevent under or over-utilization of resources, and to optimize system performance.

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Benefits of modeling and simulation Obtain a better understanding of the system by developing a mathematical model of a system of interest, and observing the system's operation in detail over long periods of time. Test hypotheses about the system for feasibility. Compress time to observe certain phenomena over long periods or expand time to observe a complex phenomenon in detail.

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Benefits of modeling and simulation Study the effects of certain informational, organizational, environmental and policy changes on the operation of a system by altering the system's model Experiment with new or unknown situations about which only weak information is available

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A system model is deterministic or stochastic : A deterministic system model has no stochastic (random) components. A system model is static or dynamic: A static system model is one in which time is not a significant variable. A dynamic system model is continuous or discrete. MODEL CHARACTERIZATION

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Tree diagram of model characterization System model deterministicstaticdynamicdiscrete stochasticstaticdynamiccontinuousdiscrete

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A discrete-event simulation model A discrete-event simulation model is defined by three attributes: stochastic | at least some of the system state variables are random. dynamic | the time evolution of the system state variables is important. discrete-event | significant changes in the system state variables are associated with events that occur at discrete time instances only.

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Model Development discrete-event simulation model will be developed consistent of six steps. Steps (2) through (6) are typically iterated, perhaps many times, until a (hopefully) valid computational model, a computer program, has been developed.

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Steps of model development 1.Determine the goals and objectives of the analysis once a system of interest has been identified. These goals and objectives are often phrased as simple Boolean decisions or numeric decisions. 2.Build a conceptual model of the system based on (1). What are the state variables, how are they interrelated and to what extent are they dynamic

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3- Convert the conceptual model into a specification model. If this step is done well, the remaining steps are made much easier. If instead this step is done poorly the remaining steps are probably a waste of time. 4- Turn the specification model into a computational model, a computer program. At this point, a fundamental choice must be made | to use a general-purpose programming language or a special-purpose simulation language. Steps of model development

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5- Verify. As with all computer programs, the computational model should be consistent with the specification model | did we implement the computational model correctly? 6- Validate. Is the computational model consistent with the system being analyzed. did we build the right model? Steps of model development

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