Using Little's Law, L = \lambda / \mu, where \lambda is the arrival rate and \mu is the service rate, we can calculate the average number of customers in the system as 10 / 15 = 5.

Using the M/M/c model, the average waiting time for a customer can be calculated as W = \lambda / (\mu * c - \lambda), where \lambda is the arrival rate, \mu is the service rate per server, and c is the number of servers. Plugging in the values, we get W = 12 / (5 * 3 - 12) = 2 hours.

The exponential distribution is widely used to model the inter-arrival times of customers in a queueing system due to its memoryless property, which means that the time until the next arrival is independent of the time since the last arrival.

Using Little's Law, \lambda = L / W, where \lambda is the arrival rate, L is the average number of customers in the system, and W is the average waiting time. Plugging in the values, we get \lambda = 10 / 2 = 10 per minute.

First-Come-First-Served (FCFS) is a queueing discipline where customers are served in the order in which they arrive, regardless of their job size or priority.

Round-Robin is a load balancing algorithm that distributes customers evenly among the servers by assigning each customer to the next available server in a circular fashion.

The M/M/1 model is used to analyze a queueing system with a single server (M), Poisson arrivals (M), and exponential service times (M).

The probability that a customer will have to wait for service is denoted by P(W > 0), where W is the waiting time.

The M/M/c model is used to analyze a queueing system with multiple servers (c), Poisson arrivals (M), and exponential service times (M).

The utilization factor \rho is defined as the ratio of the arrival rate \lambda to the service rate \mu.

The G/G/1 model is used to analyze a queueing system with general arrival and service time distributions.

The average time a customer spends in the system (T) is equal to the sum of the average waiting time (W) and the average service time (1 / \mu).

Shortest-Job-First (SJF) is a queueing discipline where customers with shorter job sizes are given priority over those with longer job sizes.

The probability that the system is empty is denoted by P(N = 0), where N is the number of customers in the system.

The M/G/c model is used to analyze a queueing system with multiple servers (c), Poisson arrivals (M), and a general service time distribution (G).