Building Simulation Model

1. Building Simulation Model • In this lecture, we are interested in whether a simulation model is accurate representation of the real system. • We are interested in building a valid,credible,and verified simulation model. • Verification: is to determine the conceptual simulation model has been correctly translated into a computer program. • Validation: is the process of determining whether a simulation model is an accurate representation of the system.

2. Validation Versus Verification Real world (News) • Verification is to check whether the translation is correct • Validation to check whether the news is correct or not English Report Arabic translator Arabic News

3. Credibility • A simulation model has a credibility if the manager and other key project personnel accept them as correct.

4. Guidelines for determining the level of model detail • Define the specific issues to be investigated and the performance measures that will be used for evaluation. • The entity moving through the simulation model need not be the entity moving in the actual system. • Example: it might be better to model a box of parts in a manufacturing model as an entity rather than modeling each part as an entity.

5. Guidelines cont. • Use Subject Mater Experts (SMEs) and sensitivity analysis to help determine the level of model detail. • SMEs are People who are familiar with the system are asked what components are likely to be added to the model • Sensitivity analysis is used to determine what factors that have a great impact on the performance measure. • Do not include excessive amount of model detail. Start by a moderate detail and add more details as needed.

6. Do not have more detail in the model more than is necessary to address the issues of interest. • The model detail should be consistent with the type of data available. • Time and Money constraints are a major factor. • If the number of factors is large. Perform analytical analysis to determine the important factors. • There are some commercial software packages available for this purpose.

7. Verification of Simulation Computer Program Some of these techniques are valid for any computing program. Technique1: Divide the program into subprograms and then debug these subprograms individually. • Sometimes computer simulation programs may go to 10,000 statements • Start by a moderate simulation model then gradually increase complexity as needed. Technique 2: Make a structured walk-through of the program by other persons as one may not able to criticize himself.

8. Verification cont. • Technique 3: Run the simulation model under several settings of the input parameters. Try it first for known system performance. • Technique 4: One of the powerful techniques is to trace the state of the simulated system and compare with a hand simulation. • Technique5: Run the model under simplified assumptions for which its true characteristics are known. • Example: replace the M/E2/1 queuing model by M/M/1 … etc.

9. Verification cont • Technique 6: If it is possible, use animation to trace entities in the model. • Technique 7: Compute a sample mean and sample variance for each simulation input probability and compare with the desired known mean and variance. • Technique 8: Use commercial simulation packages carefully to reduce the amount of programming time.

10. Techniques to increase model validity and credibility Technique 1:Collect high quality information and data on the system.Make use of the existing information including: • Conversation with Subject Mater Experts (SME’s) • Observations of the system: • Existing Theory • Use previous data that have been introduced from similar simulation studies. • Use the experience and intuition of the modelers to hypothesize how certain components of a complex system operation.

11. A. Conversation with Subject Mater Experts (SME’s) Example: for a communication network, relevant people may include: • End users • Network designers • Technology experts • System administrators • Application architects • Maintenance personnel • Managers

12. B. Observations of the system: If the system exists or a similar system exists then collect data from this system. It is important to follow these two principles. • The modelers need to make sure that the data requirements are specified precisely to the people who provide the data. • The modeler should understand the process of produced data.

13. The following errors may occur during the collection of data: • Data are not representatives of what we need • Example: the data collected in a military field test may not represent the actual combat field. • Data are not of the appropriate type or format • Example: the machine downtime in a manufacture should read only the time when the work is on not the wall clock. • Data may contain measurements, recording or rounding errors. • Data may be biased because of self interest • Data may have inconsistent units. Km, miles, …etc.

14. Technique 2:Interact with the manager on a regular basis. • Technique 3:Maintain an assumptions of document and perform a structural walk-through: • Record all assumptions that have been taken in the simulation model including • Project goals • Detailed description of each subsystem • What simplifying assumptions were made and why • Summary of data such as mean, histogram, … etc. • Sources of important and controversial information.

15. Technique 4:Validate components of the model by using quantitative techniques. • Use sensitivity analysis to determine model factors that have a significant impact on the desired measures of performance. • Examples include • The value of a parameter • The choice of a distribution that the entity moving in the system • The level of detail of subsystems • What data are the most crucial to collect

16. Technique 5:Validate output from the overall simulation model. • Compare the output with a similar existing system to check whether the output of the simulation model reflects the real life performance or not.