How do you debug and troubleshoot an Arena model of a queueing system?

1 Check your assumptions

Before you start building your model, you should clearly define your assumptions and objectives. What are the inputs, outputs, and parameters of your queueing system? What are the key performance indicators (KPIs) and metrics that you want to measure and optimize? What are the assumptions and constraints that you make about the system behavior, dynamics, and environment? You should document and justify your assumptions and check if they are consistent, realistic, and relevant to your problem. If your assumptions are wrong or incomplete, your model will not reflect the real system and may produce inaccurate or misleading results.

2 Validate your data

Another important step is to validate your data sources and inputs. You should verify that your data is accurate, complete, and representative of the system you are modeling. You should also check if your data follows the appropriate probability distributions and statistical properties that you assume in your model. For example, you can use Arena's Input Analyzer tool to fit your data to different distributions and compare their goodness-of-fit tests. You should also check for outliers, missing values, or errors in your data and handle them appropriately. If your data is invalid or unreliable, your model will not capture the system variability and uncertainty and may produce biased or inconsistent results.

3 Test your logic

Once you have your data and assumptions ready, you can start building your model logic using Arena's modules, blocks, and elements. You should test your logic step by step, using small and simple models first, and then gradually adding more complexity and detail. You should use Arena's animation, tracing, and debugging features to monitor and inspect your model behavior and flow. You should also use Arena's reports, graphs, and statistics to check and verify your model outputs and KPIs. You should look for any errors, warnings, or anomalies in your logic, such as infinite loops, deadlock situations, resource conflicts, queue overflows, or incorrect calculations. If you find any problems, you should review and revise your logic accordingly.

4 Compare with benchmarks

Another way to troubleshoot your model is to compare it with existing benchmarks or reference models. You can use Arena's templates, examples, or tutorials to find similar or related models of queueing systems and see how they are built and configured. You can also use external sources, such as textbooks, papers, or case studies, to find theoretical or empirical models of queueing systems and compare their assumptions, parameters, and outputs with yours. You should look for any significant differences or discrepancies between your model and the benchmarks and try to explain or justify them. If you cannot, you may need to modify or improve your model.

5 Experiment with scenarios

Finally, you can troubleshoot your model by experimenting with different scenarios and sensitivity analyses. You can use Arena's Scenario Manager tool to create and run multiple scenarios with different values of your inputs, parameters, or variables. You can also use Arena's OptQuest tool to optimize your model based on your objectives and constraints. You should analyze and compare the results of your scenarios and see how they affect your model performance and validity. You should look for any unexpected or unrealistic results or trends and try to understand their causes and implications. If you find any issues, you may need to adjust or refine your model.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?