input_modeling

input_modeling - SYSC 4005-5001, Winter 2010 Input Modeling...

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Professor John Lambadaris 1 SYSC 4005-5001, Winter 2010 Input Modeling Winter 2010 Slides are based on the texts: -Discrete Event System Simulation, by Banks et al -Discrete Event Simulation: A first Course, by Leemis and Park
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Professor John Lambadaris 2 SYSC 4005-5001, Winter 2010 Purpose & Overview ± Input models provide the driving force for a simulation model. ± The quality of the output is no better than the quality of inputs. ± We need 4 steps for input model development: ² Collect data from the real system ( will not cover field data collection in this class!—see next transparency for more info ) ² Identify a probability distribution to represent the input process ² Choose ( i.e. estimate ) parameters for the distribution ² Evaluate the chosen distribution and parameters for goodness of fit.
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Professor John Lambadaris 3 SYSC 4005-5001, Winter 2010 Data Collection ± One of the biggest tasks in solving a real problem. ± Suggestions that may enhance and facilitate data collection: ² Plan ahead: begin by a practice or pre-observing session, watch for unusual circumstances ² Analyze the data as it is being collected: check adequacy ² Combine homogeneous data sets, e.g. successive time periods, during the same time period on successive days ² Be aware of data censoring: the quantity is not observed in its entirety, danger of leaving out long process times for example! ² Check for autocorrelation (section 9.7) ² Collect input data, not performance data
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Professor John Lambadaris 4 SYSC 4005-5001, Winter 2010 Identifying the Distribution ± Histograms ± Selecting families of distribution ± Parameter estimation ± Goodness-of-fit tests ± Fitting a non-stationary process
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Professor John Lambadaris 5 SYSC 4005-5001, Winter 2010 Histograms ± A frequency distribution or histogram is useful in determining the shape of a distribution ± The number of class intervals (bins) depends on: ² The number of observations ² The dispersion of the data ² Suggested number of bins: the square root of the sample size ± For continuous data: ² Corresponds to the probability density function of a theoretical distribution ± For discrete data: ² Corresponds to the probability mass function ± If few data points are available: combine adjacent cells to eliminate the ragged appearance of the histogram
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Professor John Lambadaris 6 SYSC 4005-5001, Winter 2010 Histograms
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Professor John Lambadaris 7 SYSC 4005-5001, Winter 2010 Histograms b
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Professor John Lambadaris 8 SYSC 4005-5001, Winter 2010 Histograms
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Professor John Lambadaris 9 SYSC 4005-5001, Winter 2010 Ragged, coarse and appropriate histograms Same data with different interval sizes
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Professor John Lambadaris 10 SYSC 4005-5001, Winter 2010 Histograms ± Vehicle Arrival Example: # of vehicles arriving at an intersection between 7 am and 7:05 am was monitored for 100 random workdays.
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This note was uploaded on 04/16/2010 for the course SCE sysc5001 taught by Professor Lambadaris during the Spring '10 term at Carleton CA.

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input_modeling - SYSC 4005-5001, Winter 2010 Input Modeling...

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