Three types of customers arrive at a small airport:check baggage (30%, that is, for each arriving customer there is a0.30 probability that this is a “check-baggage” customer), purchasetickets (15%), and carry-on (55%). The interarrival-time data forall customers combined is as attached; all times are in minutes andthe first arrival is at time 0. The bag checkers go directly to thecheck-bag counter to check their bags the time as attached—proceedto X-ray, and then go to the gate. The ticket buyers traveldirectly to the ticket counter to purchase their tickets—the timefor which is as attached proceed to X-ray, and then go to the gate.The carry-ons travel directly to the X-ray, then to the gatecounter to get a boarding pass the time for which is as attached.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 forthe carry-on time to the X-ray, which is EXPO(3). Run your modelfor a single replication of length 920 minutes, and collectstatistics on resource utilization, queues, and system time fromentrance to gate for all customers combined. For the outputstatistics requested, put a text box inside your Arena file, orpaste in a partial screenshot from Arena or another applicationthat provides the requested results. For “queues” and “system time”report both the average and maximum. Suppose that you had theoption of adding one agent to the system and could add that agentto any one of the check-bag counter, the gate, the ticket-buyingcounter, or the X-ray station. What’s your recommendation? Set upand run a Process Analyzer (PAN) experiment (and also include thebase case of no extra agent anywhere, so five scenarios in all),using 100 replications of each scenario. As the overall outputperformance metric, use average total time of passengers in thesystem. Write a brief discussion of your findings, includingstatistical justification of your recommendation.