Three types of customers arrive at a small airport: check baggage (30%, that is, for each arriving customer there is a 0.30 probability that this is a “check-baggage” customer), purchase tickets (15%), and carry-on (55%). The interarrival-time distribution for all customers combined is EXPO(1.3); all times are in minutes and the f rst arrival is at time 0. The bag checkers go directly to the check-bag counter to check their bags— the time for which is distributed TRIA(2, 4, 5)—proceed to X-ray, and then go to the gate. The ticket buyers travel directly to the ticket counter to purchase their tickets—the time for which is distributed EXPO(7)—proceed to X-ray, and then go to the gate. The carry-ons travel directly to the X-ray, then to the gate counter to get a boarding pass— the time for which is distributed TRIA(1, 1.6, 3). All three counters are staffed all the time with one agent each. The X-ray time is EXPO(1). All travel times are EXPO(2), except for the carry-on time to the X-ray, which is EXPO(3). Run your model for a single replication of length 920 minutes, and collect statistics on resource utilization, queues, and system time from entrance to gate for all customers combined. For the output statistics requested, put a text box inside your Arena f le, or paste in a partial screenshot from Arena or another application that provides the requested results. For “queues” and “system time” report both the average and maximum.