Final answer:
Modeling the barbershop scenario with a continuous-time Markov chain allows for the calculation of the apprentice's busy time by finding the steady-state probabilities of the system's states.
Step-by-step explanation:
The student is asking about the long-run proportion of time the apprentice in a barbershop is busy cutting hair when customers arrive at a given rate and when there are two barbers with different service rates. To solve this, one can model the situation using a continuous-time Markov chain. The owner cuts at a rate of 4 customers per hour, the apprentice at 2 customers per hour, and customers walk in at a rate of 6 customers per hour. Since the apprentice is only engaged when the owner is busy, and there's a capacity for one waiting customer, we need to calculate the probability of the states where the apprentice is actively cutting hair.
When modeling the states, state 0 represents both barbers being free, state 1 represents only the owner being busy, and state 2 represents both the owner and the apprentice being busy. Using the arrival rates and service rates, the transition rates between these states can be determined. The steady-state probabilities are then calculated, providing the long-term proportion of time each state occurs. Finally, the apprentice's busy proportion is the steady-state probability of state 2, as this is the only state where the apprentice works.