Handout 3_2010 - 1 IE 372 Simulation Handout 3 VERIFICATION...

Info iconThis preview shows pages 1–3. Sign up to view the full content.

View Full Document Right Arrow Icon
IE 372 Simulation - Handout 3 System Results used in decision making “Correct” results available Simulation program Conceptual model Analysis and data collection Selling results to mgt. Making runs Coding Validation Establish credibility Verification Validation Establish credibility Verification Verification is concerned with determining whether the conceptual simulation model (with its assumptions) has been correctly translated into a computer program, i.e. debugging the simulation code. Verification is about answering the question “Did we do the things right?” or “Does the model run as intended?” Always assume that it does not, and it must have some bugs. Try to find them. Eight Techniques to Debug the Model Code 1. Write and debug the program in modules or subprograms instead of writing one big program. Start with a simple model, add details incrementally. 2. A structured walk-through of the program by more than one person is advisable, especially for large programs. 3. Use different sets of input parameters and check to see that the output changes as expected. For example, create test cases with extreme conditions. Create congestion in the model (increase arrival rate) Force starvation (limit the number of entities to just a few) Reduce queue capacities to force balking/blocking 1 1
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Increase rate of occurrence of infrequent events, e.g. machine breakdowns Run the model with constant input parameters instead of random variables 4. Trace the model’s operation. Contents of event list and queues, values of state variables and statistical counters can be traced. Most modern simulation languages have interactive debuggers for this purpose. ARENA has the Run Controller and the TRACE element. 5. If possible, run the model under simplifying assumptions for which its true characteristics are known or can easily be computed. For example, use M/M/s or M/G/s queues from queueing theory to find steady-state values analytically, e.g. expected waiting time in system. Then, compare simulation results with these analytical results. 6. Animation can be used to detect unexpected occurrence of events and works as a debugger. 7. Check to see if input distributions’ parameters are entered as they are defined in the simulation language. For example, parameters of gamma and weibull distributions are defined differently in different sources. 8. Use a simulation language/package to reduce the programming effort, but be careful about possible errors in the language, especially in a recently released version. Tracing the Model’s Operation TRACE, Beginning Time { 0.0 }, Ending Time { Infinite }, Condition { Trace all }, Expressions { No values },…; Function: Traces the model’s operation between “Beginning Time” and “Ending Time” whenever the “Condition” holds. Displays the values of “Expressions” during the trace. Useful for off-line tracing but not interactive. 2
Background image of page 2
Image of page 3
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

Page1 / 20

Handout 3_2010 - 1 IE 372 Simulation Handout 3 VERIFICATION...

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online