HAMBURGER: PERFORMANCE MEASURES

Hello everyone!

For this week, we have to compute the performance measures of the system, without failures and setups and including them.

For this calculation, we use the following relations according to Little’s Law:

ORDERING STATION

Including Failures

As we mentioned previously, the station number two has a failure which is the result of sporadic accidents because of misuse of knifes, shake milk machines or stoves. To calculate the average time between failures is necessary to take into account the rate, 0.00472 accidents/day which means 3.278E-06 accidents per minute, so mf is 305084,75 minutes. Additionally, the average time of reparation is 30 minutes. In order to compute the performance measures of this station, it is important to calculate the percentage of time in steady state that the station is available to proceed, that we are going to represent with the letter A.

COOKING AND DELIVERING STATION

Model G/G/m including Failures

Now we develop the model with non-planned failures for accidents handling tools, knifes or machines.

The failures occurs with a rate of 0.00472 accidents/day, that means and accident occurs in average every 211.86 days or minutes

We observed a mean time of 5 minutes each time for any kind of accident.

As A tends to 1 the performance measures will be the same for the model without preempt and not preempt stops. The following table resumes the performance measures for this station.

SAUCE STATION

As we said before this station presents a setup that is when the employees have to refill the sauce bottles, that takes 3 minutes and is every 100 clients, according to this, we proceeded to calculate how long a client is in the row and how many customers are going to be on the station at any time. For the computation of the performance measure, we said that the station has 3 servers.

First we proceeded to calculate the arrival coefficient of variance, the effective time and the variance of the effective time having clear that the refilling time is a setup and not a failure.

Secondly, modeling the station as a G/G/M we computed the time that a client is in the row with the utilization of the station,

Finally, the same variables were calculated without the refilling time to look how this setup affected the quantity and time that customers were in the station.