To conduct the experiment for the M/M/c/N queueing model, follow these detailed steps:

1. Set Simulation Parameters:

   • Set the Mean Arrival Rate: Input the average rate at which customers arrive at the system.
   • Set the Mean Service Rate: Input the average rate at which each server can process customers.
   • Set the Number of Servers: Define the number of servers available to process customers in the system.
   • Set the Maximum Number of Customers: Define the system's capacity, which is the maximum number of customers that can be present in the system (both in service and in the queue) at any given time.

2. Ensure Steady-State Conditions: Ensure that the mean arrival rate is less than the product of the mean service rate and the number of servers. This is crucial for the system to reach a steady state, where the queue does not grow indefinitely, and the system can operate in equilibrium over time.

3. Set the Simulation Speed:

   • Adjust Simulation Speed: Set the simulation speed according to your requirements. This could help in either speeding up the simulation process or making it slower for detailed observation.

4. Start the Experiment:

   • Initiate the Simulation: Click the 'Start' button to begin the simulation. The system will start processing customers based on the parameters you have set, simulating the arrivals, services, and departures.

5. Stop the Experiment and View Results:

   • Stop the Simulation: After allowing the simulation to run for a sufficient period to gather data, click the 'Stop' button.
   • View Results: Examine the steady-state results provided by the simulation. These results will be available both numerically and graphically, showing metrics such as the average number of customers in the system, blocking probability, average waiting time, and server utilization.

6. Compare Theoretical and Experimental Results:

   • Analyze the Data: Compare the numerical and graphical results obtained from the simulation with the theoretical predictions of the M/M/c/N model. This comparison helps validate the theoretical model and provides insights into the system's performance under different conditions.