# Steps of a sound simulation study

## Presentation on theme: "Steps of a sound simulation study"— Presentation transcript:

Steps of a sound simulation study
1. Formulate the problem and plan the study. Problem of interest is stated precisely Arrange a meeting of the team of simulation analysts and determine the objectives of the simulation study Specific questions to answer Performance measure used to evaluate the efficiency of different system configurations Scope of the model System configurations to be modeled Software to be used Time frame for the study

2. Collect data and define a model
Collect information on the system layout and operating procedures Collect data (if possible) to specify the model parameter and input probability distributions Collect data on the performance of the existing system (if possible) Build the model according to: Project objectives Performance measure Data availability Credibility concerns Computer constraints Opinions of subject matter experts Time and memory constraints

3. Validate the model: 4. Construct a computer program and Verify
perform a structural walk-through of the conceptual model, this Helps ensure that the model assumptions are correct and complete Promotes ownership of the model Takes place before programming begins 4. Construct a computer program and Verify Decide which computer program to use Construct the computer program and make sure it is verified.

5. Make pilot runs 6. Is the program model valid?
Use for validating the model Gives you how much more runs you need 6. Is the program model valid? Use the pilot runs to check whether the results obtained are consistent with the real world or not. 7. Design experiments. Decide how much simulation runs to be made for each alternative system

Analyze the output data.
Perform the productive simulation runs that are used to estimate the performance measure Analyze the output data. Use statistical techniques to analyze the output data obtained by the production runs (will discuss later) Document, present, and use results

Monte Carlo Simulation
Solving deterministic problems using stochastic models. Example: estimate It is efficient in solving multi dimensional integrals.

Monte Carlo Simulation
To illustrate, consider a known region R with area A and R1 subset of R whose area A1 in unknown. To estimate the area of R1 we can through random points in the region R. The ratio of points in the region R1 over the points in R approximately equals the ratio of A1/A. R R1

Monte Carlo Simulation
To estimate the integral I. one can estimate the area under the curve of g. Suppose that M = max {g(x) } on [a,b] 1. Select random numbers X1, X2, …,Xn in [a,b] And Y1, Y2, … ,Yn in [0,M] 2. Count how many points (Xi,Yi) in R1, say C1 3. The estimate of I is then C1M(b-a)/n M R R1 a b

Most complex, real-world systems with stochastic elements that cannot be described by mathematical models. Simulation is often the only investigation possible Simulation allow us to estimate the performance of an existing system under proposed operating conditions. Alternative proposed system designs can be compared with the existing system We can maintain much better control over the experiments than with the system itself Study the system with a long time frame