How do you incorporate random arrivals and service times in an Arena model?

1 Random arrivals

One of the most common ways to model random arrivals of entities in Arena is to use the Create module. The Create module allows you to specify the interarrival time of entities, which can be constant, expression, or based on a probability distribution. You can choose from a variety of built-in distributions, such as exponential, normal, uniform, or Poisson, or define your own custom distribution using the Input Analyzer tool. You can also set the maximum number of entities to be created, the first creation time, and the batch size.

2 Random service times

Another important aspect of queueing systems is the service time of entities, which is usually dependent on the type and availability of resources. In Arena, you can use the Process module to define the service time of entities, which can also be constant, expression, or based on a probability distribution. You can also use the Seize, Delay, and Release modules to model the resource allocation, service delay, and resource release of entities. You can assign different service times and resources to different entity types using the Assign and Decide modules.

3 Queueing systems in Arena

There are many examples of queueing systems that can be modeled in Arena, such as bank tellers, call centers, airport security checkpoints, or manufacturing lines. You can use the Basic Process panel to create a simple queueing system with one or more servers and one or more queues. You can also use the Advanced Process panel to create more complex queueing systems with multiple servers, queues, priorities, schedules, failures, and routings. You can customize your model with attributes, variables, and expressions to capture the specific features and behaviors of your system.

4 Model analysis

Once you have created your queueing system model in Arena, you can run it and analyze its performance using various tools and outputs. You can use the Run Setup dialog box to set the replication parameters, such as the run length, warm-up period, number of replications, and confidence interval. You can also use the Output Analyzer tool to view and compare the statistics and graphs of your model outputs, such as the number of entities in system, the average waiting time, the resource utilization, or the queue length. You can also use the Animation and Debug modules to visualize and verify your model logic and results.

5 Model improvement

After you have analyzed your queueing system model in Arena, you may want to improve its performance by changing some of the model parameters or logic. You can use the OptQuest tool to perform optimization and sensitivity analysis on your model. OptQuest allows you to define the objective function, the decision variables, and the constraints of your optimization problem. It then uses a combination of metaheuristic algorithms to search for the optimal or near-optimal solution. You can also use the Scenario Manager tool to create and compare different scenarios of your model with different parameter values or logic.

6 Model documentation

Finally, you may want to document your queueing system model in Arena for future reference or communication. You can use the Report module to generate a comprehensive report of your model, which includes the model overview, the module details, the input data, the output data, and the graphs. You can also use the Note module to add comments or explanations to your model. You can also use the Template module to create reusable templates of your model or parts of your model that you can share or apply to other models.

7 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?