How do you design and implement a queueing experiment in arena?

2 Define the input parameters

The next step is to define the input parameters that control the variability and uncertainty of your model. You can use the Input Analyzer feature of Arena to fit the empirical data to different probability distributions, such as exponential, normal, or Poisson. You can also use expressions, variables, and attributes to define the input parameters dynamically. For example, you can use an expression to change the arrival rate based on the time of day, or an attribute to assign different priorities to different entities.

3 Run the simulation

The third step is to run the simulation and observe how your model behaves over time. You can use the Run Controller feature of Arena to set the run length, replication number, warm-up period, and termination condition of your simulation. You can also use the animation and debugging tools to visualize and verify your model. For example, you can use the animation to see how the entities flow through the system, or the breakpoints to pause the simulation at certain events.

4 Collect the output statistics

The final step is to collect the output statistics and analyze the results of your simulation. You can use the Output Analyzer feature of Arena to display and compare the performance measures and indicators of your model, such as average queue length, average waiting time, utilization, throughput, or service level. You can also use the graphs, reports, and scenarios tools to visualize and summarize your output statistics. For example, you can use the graphs to plot the confidence intervals of your performance measures, or the scenarios to test different input parameters.

5 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?