Should I use the mean or median of my data for queueing models? I am working on a project with a call center. Long story short, I am analzying the data revolved around the incoming calls to this call center in order to eventually use a queueing model.
A queueing model is one which you provide with certain values such as the average service time of calls (average value of how long calls last) and some other inputs, and it would at the end of the day tell us how many agents need to be planned to answer incoming calls.
My question is: Should I insert the mean value or the median value of my service time data to this queueing model? (The data follows a lognormal distribution).
Note:
1- I cleaned my data from outliers so I suppose a mean and a median value are both usable.
2- The answer to my decision is probably going to be dependent on what my end-goal is, which is why I tried to explain my goal in the above paragraph.
Could someone shed some light on this? Thank you!

The standard results in queuing theory predict mean wait times, so you will find means easier to work with.
But let me ask something. You say "I cleaned my data from outliers". Do you mean that you dropped the records of really long waits? That sounds like a mistake - those are the most important records for this application, aren't they.
You wouldn't invest based on a model that ignores stock market crashes.
