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IE 372 Simulation - Handout 7
SIMULATION OUTPUT ANALYSIS (Law & Kelton) When some simulation input parameters are random variables, simulation outputs are also random variables. Therefore, we need to statistically analyze the simulation results. It is n
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IE 372 Simulation - Handout 6
RANDOM NUMBER AND RANDOM VARIATE GENERATION (Law & Kelton) General Idea Let X be a continuous random variable with probability density function f ( x) and cumulative distribution function F ( x) , i.e. P ( X x) = F ( x) .
F
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IE 372 Simulation - Handout 5
ADDITIONAL SIMAN CAPABILITIES Blocks and elements discussed so far constitute about 20% of SIMAN constructs, but over 50% of modeling requirements can be met with these. For the rest, we need to learn additional constructs.
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IE 372 Simulation - Handout 4
SELECTING INPUT PROBABILITY DISTRIBUTIONS (Law & Kelton) For simulating a real life system, we need to estimate the probability distribution of an input random variable using past data, if any. We have two options: 1. Fit a
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IE 372 Simulation - Handout 3
VERIFICATION AND VALIDATION (Law & Kelton)
Establish credibility
Validation Establish credibility
Verification
Validation
System Analysis and data collection
Conceptual model
Coding
Simulation program
Making runs
Correct re
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IE 372 Simulation - Handout 2
INTRODUCTION TO ARENA AND SIMAN About Simulation Languages Common features of simulation languages Random number (RN) and random variate (RV) generation Managing event list, calling necessary routines depending on the type
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IE 372 Simulation - Handout 1
INTRODUCTION TO SIMULATION Simulation Simulation refers to a broad collection of methods and applications to mimic the behavior of real systems usually on a computer with appropriate software. Simulation is the process of F