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SIMULATION

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Simulation Definition of Simulation Simulation Methodology Proposing a New Experiment Considerations When Using Computer Models Types of Simulations Desirable Features of Simulation Software Advantages & Disadvantages of Simulation

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Simulation Defined A computer-based model used to run experiments on a real system. ä Typically done on a computer. ä Determines reactions to different operating rules or change in structure. ä Can be used in conjunction with traditional statistical and management science techniques.

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Major Phases in a Simulation Study Start Define Problem Construct Simulation Model Specify values of variables and parameters Run the simulation Evaluate results Validation Propose new experiment Stop

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Simulation Methodology: Problem Definition Specifying the objectives Identifying the relevant controllable and uncontrollable variables of the system to be studied

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Constructing a Simulation Model Specification of Variables and Parameters Specification of Decision Rules Specification of Probability Distributions Specification of Time-Incrementing Procedure

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Data Collection and Random Number Interval Example Suppose you timed 20 athletes running the 50-yard dash and tallied the information into the four time intervals below. Seconds 5-5.99 6-6.99 7-7.99 8 or more TalliesFrequency 4 10 4 2 You then count the tallies and make a frequency distribution. % 20 50 20 10 Then convert the frequencies into percentages. Finally, use the percentages to develop the random number intervals. RN Intervals 00-19 20-69 70-89 90-99 Accum. % 20 70 90 100 You then can add the frequencies into a cumulative distribution.

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Evaluating Results Conclusions depend on ä the degree to which the model reflects the real system ä design of the simulation (in a statistical sense) The only true test of a simulation is how well the real system performs after the results of the study have been implemented.

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Proposing a New Experiment Might want to change many of the factors: ä parameters ä variables ä decision rules ä starting conditions ä run length If the initial rules led to poor results or if these runs yielded new insights into the problem, then a new decision rule may be worth trying.

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Considerations When Using Computer Models Computer language selection Flowcharting Coding Data generation Output reports Validation

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Types of Simulation Models Continuous äBased on mathematical equations. äUsed for simulating continuous values for all points in time. äExample: The amount of time a person spends in a queue. Discrete äUsed for simulating specific values or specific points. äExample: Number of people in a queue.

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Desirable Features of Simulation Software Be capable of being used interactively as well as allowing complete runs. Be user-friendly and easy to understand. Allow modules to be built and then connected. Allow users to write and incorporate their own routines. Have building blocks that contain built-in commands. Have macro capability, such as the ability to develop machining cells.

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Desirable Features of Simulation Software Have material-flow capability. Output standard statistics such as cycle times, utilization, and wait times. Allow a variety of data analysis alternatives for both input and output data. Have animation capabilities to display graphically the product flow through the system. Permit interactive debugging.

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Advantages of Simulation Often leads to a better understanding of the real system. Years of experience in the real system can be compressed into seconds or minutes. Simulation does not disrupt ongoing activities of the real system. Simulation is far more general than mathematical models. Simulation can be used as a game for training experience.

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Advantages of Simulation (Continued) Simulation provides a more realistic replication of a system than mathematical analysis. Simulation can be used to analyze transient conditions, whereas mathematical techniques usually cannot. Many standard packaged models, covering a wide range of topics, are available commercially. Simulation answers what-if questions.

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Disadvantages of Simulation There is no guarantee that the model will, in fact, provide good answers. There is no way to prove reliability. Building a simulation model can take a great deal of time. Simulation may be less accurate than mathematical analysis because it is randomly based. A significant amount of computer time may be needed to run complex models. The technique of simulation still lacks a standardized approach.

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