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d. Trust Us Bank has two types of customers: merchant customers and regular customers. The mean arrival rate for each type of customer is 15 per hour. Both types of customers currently wait in the same line and are served by the same tellers with the same average service time. However, Suzanne is considering changing this. The new system she is considering would have two linesone for merchant customers and one for regular customers. There would be a single teller serving each line. What would be the average waiting time for each type of customer before reaching a teller? On average, how many total customers would be in the bank, including those currently being served? How do these results compare to those from part a.

e. Suzanne feels that if the tellers are specialized into merchant tellers and regular tellers, they would be more efficient and could serve customers in an average of 2.5 minutes instead of 3 minutes. Answer the questions for part d again with this new average service time.

2 1 Template for the M/M/s Queueing Model 2 3 Data 2. = (mean arrival rate) ul= (mean service rate) (# servers) 4 LO 5 6 S = 6 7 8 9 Pr(W>t) = #DIV/0! when t = 0.0833333 9 10 11 #DIV/0! #/0! Prob(W>t) = #REF! when t = 0.0833333 Probability 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 NWA.co IN 1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Number of Customers in System Answer Question 1d. Avg Waiting time in the System (in Minutes) Avg Waiting Time in the Que? (in minutes) > 11.1a 11.1b 11.10 11.1d 11.1e + A E B D 1 Template for the M/M/s Queueing Model 2 3 4 3 Data st II LO 5 u= (mean arrival rate) (mean service rate) (# servers) co S = 7 8 Pr(W>t) = #DIV/0! when t = 0.0833333 9 1 9 10 11 #DIV/0! #AI/0! Prob(W,>t) = #REF! when t = 0.0833333 12 13 14 15 16 17 18 19 20 21 22 Probability 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 ooooooooo OOONOOMNO T 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Number of Customers in System 23 24 8898%E8888 25 Answer Question 1e. 26 27 28 29 Avg Waiting time in the System (in Minutes) Avg Waiting Time in the Que? (in minutes) 11.1a 11.1b 11.10 11.10 11.1e + 2 1 Template for the M/M/s Queueing Model 2 3 Data 2. = (mean arrival rate) ul= (mean service rate) (# servers) 4 LO 5 6 S = 6 7 8 9 Pr(W>t) = #DIV/0! when t = 0.0833333 9 10 11 #DIV/0! #/0! Prob(W>t) = #REF! when t = 0.0833333 Probability 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 NWA.co IN 1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Number of Customers in System Answer Question 1d. Avg Waiting time in the System (in Minutes) Avg Waiting Time in the Que? (in minutes) > 11.1a 11.1b 11.10 11.1d 11.1e + A E B D 1 Template for the M/M/s Queueing Model 2 3 4 3 Data st II LO 5 u= (mean arrival rate) (mean service rate) (# servers) co S = 7 8 Pr(W>t) = #DIV/0! when t = 0.0833333 9 1 9 10 11 #DIV/0! #AI/0! Prob(W,>t) = #REF! when t = 0.0833333 12 13 14 15 16 17 18 19 20 21 22 Probability 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 ooooooooo OOONOOMNO T 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Number of Customers in System 23 24 8898%E8888 25 Answer Question 1e. 26 27 28 29 Avg Waiting time in the System (in Minutes) Avg Waiting Time in the Que? (in minutes) 11.1a 11.1b 11.10 11.10 11.1e +