An airline is analyzing its customer service phone lines, to determine how many people (operators) to have available for answering calls at different times of day. At busy times (about 10% of the times), the arrival rate is 10 calls/minute. At other times, the arrival rate is 2 calls/minute. Once an operator answers a call (at any time), it takes an average of 5 minutes to complete the call. [NOTE: This is a simplified version of the call center system. If you have deeper knowledge of how call centers work, please do not use it for this question; you would end up making the question more complex than it is designed to be.] a. The first model the airline tries is a queuing model with 100 operators always available. We would expect the queuing model to show that wait times are [ Select ] b. The second model the airline tries is a queuing model with 25 operators available during busy times and 5 operators available during non-busy times. We would expect the queuing model to show that wait times are [ Select ] The airline now has decided that, when there are 31 calls waiting, the airline will turn on an Al operator that can handle 20 calls simultaneously. The Al operator then stays on until no more calls are waiting. The airline would like to model this new process with a Markov chain, where each state is the number of calls waiting (e.g., 0 calls waiting, 1 call waiting, etc.). Notice that now, the transition probabilities from a state like "3 calls waiting" depend on whether the Al operator is currently on, and therefore depend on whether the system was more recently in the state "31 calls waiting" or "O calls waiting". C. The process is memoryless and the Markov chain is an appropriate model ONLY if the arrivals follow the Poisson distribution and the call durations follow the Exponential distribution. . Answer 1: low at both busy and non-busy times Answer 2: high at both busy and non-busy times Answer 3: is memoryless and the Markov chain is an appropriate model ONLY if the arrivals follow the Poisson distribution and the call durations follow the Exponential distribution. is not memoryless, so the Markov chain model would not be well-defined