Transcript model simulasi - Binus Repository

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:
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Pengertian simulasi
Klasifikasi model simulasi
Motivasi menggunakan simulasi
Langkah-langkah proses simulasi
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
Simulated = Mean
value
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+
Random X Standard
number
deviation
Uniform Distribution
F(x)
0
a
b
x
Simulated
a + (b - a)(Random number as a percentage)
=
value
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Negative Exponential Distribution
F(t)
P ( t  T ) . RN
0
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T
t
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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