1. ModelLab6 -1: Travelers arrive at the main entrance door of an airline terminal according to an exponential interarrival-time distribution with mean 1.6 minutes, with the first arrival at timemniformly between 2 and 3 minutes. At the check-in counter, travelers wait in a single line untm available to serve them. The check-in time (in minutes) follows a Weibull distribution with parameters 3: 7.75 and a=3.91 . Upon completion of their check-in, they are free to travel to their gates. Create a simulation model of the system with animation (including the travel time from entrance to check-in). Run the simulation for a single replication of 16 hours to determine the average time in system, number of passengers completing checkin, and the average length of the check-in queue. 2. (5 pts.) ModelLab6-2: Modify ModelLab6-1 above by adding agent breaks. The 16 hours are divided into two 8-hour shifts. Agent breaks are staggered, starting at 90 minutes after the beginning of each shift. Each agent is given one 15-minute break.Agent lunch breaks {30 minutes) are also staggered, starting 3.5 hours after the beginning of each shift. The agents are rude and, if they are busy when break time comes around, they just leave anyway and make the passenger wait until break time is over before finishing up that passenger (since all agents are identical, it's not necessary for the same agent to finish up that passenger). For Schedule Rules, see Figure 4-6 from the Arena textbook on the next page. Compare the results of this model to those of the model without agent breaks. 3. (5 pts.) ModelLab6-3: During the verification process of the airline check-in system from ModelLab6-2 above, it was discovered that there were really two types of passengers. The first passenger type arrives according to an exponential interarrival distribution with mean 2.4 minutes and has a service time (in minutes) following a gamma distribution with param ete rs B = 0.42 and [I = 14.4 . The second type of passenger arrives according to an exponential distribution with mean 4.4 minutes and has a service time {in minutes) following 3 plus an Erlang distribution with parameters ExpMean = 0.54 and k: 15 (i.e., the Expression for the service time is 3+ERLA(0.54, 15)). A passenger of each type arrives at time 0. Modify the model from ModelLab6-2 above to include this new information. Com pare the results. Provide the summary of your results from the three models in the table below. How are the results different for the three models and why