Lab 6
Lab 6 Report
Part A
1. Run your model. Compare the queue statistics of the 3 processes with those obtained for Part C in the previous Lab. How have they changed and what conclusions can you draw? (Note the sums of all capacities for both cases are equivalent – 12 in each).
The queue time and number statistics for the Air Jordan Logo operators and the Air Max Logo Operators were relatively close. The average queue length for the Air Jordan Logo operators was 0.03769112 in the previous lab and 0.03767047 in this lab. The average queue length for the Air Max Logo Operator was 0.01767759 in the previous lab and 0.02381649 in this lab. The queue that had the largest change was that of the eyelet operator. The average queue length changed decreased from 5.9165 in the previous lab to 0.1918 in this lab. Furthermore, the average queue time for the eyelet operator decreased from 3.5395 in the previous lab to 0.1149 in this lab. The addition of sets or cross trained employees significantly decreased the queue for the eyelet operators without creating much change in the queue times and lengths of the Air Max and Air Jordan Logo Operator’s queues.
2. How do the utilizations of the various workers relate to the queue statistics of the stations where they work?
Two of the three highest utilized workers, Ron and Sylvia, worked as eyelet operators which was the busiest station. Paris, the second most utilized worker, worked for the Air Jordan and Air Max stations but was listed first, meaning she would be utilized before the other workers. Accordingly, longer queue lengths and times typically corresponded to a greater utilization.
Part B
1. Run the model and check the report. What is the average daily revenue? Note that this is NOT the “average” value for your recorded expression as this will give you the average value for all times of day (e.g. low revenues early in the day averaged with high revenues at the end of the day). Rather, you must check the
Part A
1. Run your model. Compare the queue statistics of the 3 processes with those obtained for Part C in the previous Lab. How have they changed and what conclusions can you draw? (Note the sums of all capacities for both cases are equivalent – 12 in each).
The queue time and number statistics for the Air Jordan Logo operators and the Air Max Logo Operators were relatively close. The average queue length for the Air Jordan Logo operators was 0.03769112 in the previous lab and 0.03767047 in this lab. The average queue length for the Air Max Logo Operator was 0.01767759 in the previous lab and 0.02381649 in this lab. The queue that had the largest change was that of the eyelet operator. The average queue length changed decreased from 5.9165 in the previous lab to 0.1918 in this lab. Furthermore, the average queue time for the eyelet operator decreased from 3.5395 in the previous lab to 0.1149 in this lab. The addition of sets or cross trained employees significantly decreased the queue for the eyelet operators without creating much change in the queue times and lengths of the Air Max and Air Jordan Logo Operator’s queues.
2. How do the utilizations of the various workers relate to the queue statistics of the stations where they work?
Two of the three highest utilized workers, Ron and Sylvia, worked as eyelet operators which was the busiest station. Paris, the second most utilized worker, worked for the Air Jordan and Air Max stations but was listed first, meaning she would be utilized before the other workers. Accordingly, longer queue lengths and times typically corresponded to a greater utilization.
Part B
1. Run the model and check the report. What is the average daily revenue? Note that this is NOT the “average” value for your recorded expression as this will give you the average value for all times of day (e.g. low revenues early in the day averaged with high revenues at the end of the day). Rather, you must check the