Whats the best approach too?????? (1 Viewer)

jdraw said:

Just noticed this thread and admit to being a little confused with the requirement.
If you have Visitors entering and on average they leave(are processed) in 45 minutes, then it seems you may have the concept of a queue. You haven't stated clearly what happens in those nominal 45 minutes, and that may be the key issue causing confusion.

For example if you are admitting people to an office to be served by 1 technician, then it's possible for the visitor waiting time to vary depending on the time for the technician to resolve the problem.

Here' a link to some queue based application info by PhilS/codekabinett

As always clarity is critical to getting focused responses.


I agree that approach in post#7 should identify the visitors on site.
ONSite Visitors = Number on site at start + Number of new visitors - Number of Visitors who have left

Click to expand...

Thanks its not quite correct.

This is as simple as visitors in a waiting room. The average time is 45 minutes therefore how many chairs are required?

We have no other data than time in and the 45 minutes average wait time.

That's really all there is too it, and the boss wants this in 15 minute slices, 30 minute slices and 60 minute slices. Don't ask me...I work there :banghead:

Back to the queue idea for an example from Google search that sounds similar to your issue.

This is as simple as visitors in a waiting room. The average time is 45 minutes therefore how many chairs are required?

Click to expand...

Little’s Law is a theorem that determines the average number of items in a stationary queuing system based on the average waiting time of an item within a system and the average number of items arriving at the system per unit of time.
Mathematically, Little’s Law is expressed through the following equation:

Little’s Law - Formula
L = λ * W

Where:

L – the average number of items in a queuing system
λ – the average number of items arriving at the system per unit of time
W – the average waiting time an item spends in a queuing system

Example of Little’s Law

John owns a small coffee shop. He wants to know the average number of customers queuing in his coffee shop to decide whether he needs to add more space to accommodate more clients. Currently, his queuing area can accommodate no more than eight customers.

John measured that on average, 40 customers arrive at his coffee shop every hour. He also determined that on average, a customer spends around 6 minutes in a store (or 0.1 hours). Given the inputs, John can find the average number of the customers queuing in his coffee shop by applying the Little’s Law:

L = 40 x 0.1 = 4 customers

The Little’s Law shows that on average, there are only four customers queuing in John’s coffee shop. Therefore, he does not require to create more space in the store to accommodate more queuing customers.

Sounds similar to your set up, so may be applicable.

Good luck with your project.

@isladogs

Ockham's razor in action.

I think you are right, it's just another example of a clockin/clockout application.

I agree that clockin/clockout is the way to approach the problem, as Colin said in post #7 and I concurred in post 18.. However, the OP says his data for analysis does not have a clockout value.
If he takes the incoming data and analyzes it, he could determine
- the average number of clients entering the system in each 15 min period A15
- the average number of clients entering the system in each 30 min period A30
- the average number of clients entering the system in each 60 min period A60

He knows the 45 min waiting time.

He could use Little's Law to get an approximation.

I do note that GLB hasn't been back?