learning outcomes
DESCRIPTION
Learning Outcomes. Mahasiswa akan dapat menjelaskan definisi, pengertian, klasifikasi, motivasi penggunaan simulasi,model simulasi dan langkah-langkah proses simulasi. Outline Materi:. Pengertian simulasi Klasifikasi model simulasi Motivasi menggunakan simulasi - PowerPoint PPT PresentationTRANSCRIPT
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Learning Outcomes
• Mahasiswa akan dapat menjelaskan definisi, pengertian, klasifikasi, motivasi penggunaan simulasi,model simulasi dan langkah-langkah proses simulasi.
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Outline Materi:
• Pengertian simulasi• Klasifikasi model simulasi• Motivasi menggunakan simulasi• Langkah-langkah proses simulasi
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Pengertian Simulasi (Simulation)
Simulation: a descriptive technique that enables a decision maker to evaluate the behavior of a model under various conditions.
•Simulation models complex situations
•Models are simple to use and understand
•Models can play “what if” experiments
•Extensive software packages available
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Simulation Process
1. Identify the problem
2. Develop the simulation model
3. Test the model
4. Develop the experiments
5. Run the simulation and evaluate results
6. Repeat 4 and 5 until results are satisfactory
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Monte Carlo Simulation
Monte Carlo method: Probabilistic simulation technique used when a process has a random component
• Identify a probability distribution
• Setup intervals of random numbers to match probability distribution
• Obtain the random numbers • Interpret the results
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Simulating Distributions• Poisson
– Mean of distribution is required
• Normal– Need to know the mean and standard deviation
Simulatedvalue
Mean Randomnumber
Standarddeviation
+ X=
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Uniform Distribution
a b0 x
F(x)
Simulatedvalue
a + (b - a)(Random number as a percentage)=
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Negative Exponential Distribution
F(t)
0 T t
P t T RN( ) .
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Computer Simulation• Simulation languages
– SIMSCRIPT II.5
– GPSS/H
– GPSS/PC
– RESQ
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Advantages of Simulation• Solves problems that are difficult or impossible to solve
mathematically
• Allows experimentation without risk to actual system
• Compresses time to show long-term effects
• Serves as training tool for decision makers
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Limitations of Simulation• Does not produce optimum solution
• Model development may be difficult
• Computer run time may be substantial
• Monte Carlo simulation only applicable to random systems
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