learning outcomes

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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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