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1 McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. 2 Technical Note 17 Simulation McGraw-Hill/Irwin ©The McGraw-Hill Companies, Inc., 2006 3 OBJECTIVES 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 McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. 4 Simulation-Defined A simulation is a computer-based model used to run experiments on a real system – – – McGraw-Hill/Irwin Typically done on a computer Determines reactions to different operating rules or change in structure Can be used in conjunction with traditional statistical and management science techniques © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. 5 Major Phases in a Simulation Study Start Define Problem Construct Simulation Model Specify values of variables and parameters Lets look at each of these steps in turn… Run the simulation Evaluate results Validation Propose new experiment Stop McGraw-Hill/Irwin From Exhibit TN17.1 © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. Simulation Methodology:Problem Definition Specifying 6 the objectives Identifying the relevant controllable and uncontrollable variables of the system to be studied McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. 7 Constructing a Simulation Model Specification of Variables and Parameters Specification of Decision Rules Specification of Probability Distributions Specification of Time-Incrementing Procedure McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. Data Collection & Random No. Interval Example Suppose you timed 20 athletes running the 8 100-yard dash and tallied the information into the four time intervals below You then count the tallies and make a frequency distribution Then convert the frequencies into percentages You then can add the frequencies into a cumulative distribution Finally, use the percentages to develop the random number intervals Seconds Tallies 0-5.99 6-6.99 7-7.99 8 or more McGraw-Hill/Irwin Frequency 4 10 4 2 % 20 50 20 10 Accum. % 20 70 90 100 RN Intervals 00-19 20-69 70-89 90-99 © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. 9 Specify Values of Variables and Parameters Determination of starting conditions Determination McGraw-Hill/Irwin of run length © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. 10 Run the Simulation By computer Manually McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. 11 Evaluate Results Conclusions depend on – – the degree to which the model reflects the real system 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 McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. 12 Validation Refers to testing the computer program to ensure that the simulation is correct To insure that the model results are representative of the real world system they seek to model McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. 13 Proposing a New Experiment Consider changing many of the factors: – – – – – parameters variables decision rules starting conditions 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 McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. 14 Considerations When Using Computer Models Computer language selection Flowcharting Coding Data generation Output reports Validation McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. 15 Types of Simulation Models Continuous – Based on mathematical equations – Used for simulating continuous values for all points in time – Example: The amount of time a person spends in a queue Discrete – Used for simulating specific values or specific points – Example: Number of people in a queue McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. 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 McGraw-Hill/Irwin 16 © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. 17 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 McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. 18 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 McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. 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 McGraw-Hill/Irwin 19 © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. 20 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 McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. 21 End of Technical Note 17 McGraw-Hill/Irwin ©The McGraw-Hill Companies, Inc., 2006