lunes, 19 de septiembre de 2011

Model Suggestions Based on Other Blogs


For this week, we have to review other similar projects and find possible improvements for our project. Consulting three different blogs, we have the following conclusions:

Our system could have one extra station, corresponding to the trash. It means that the client, after eating his meal, goes to the trash and leaves the table clean, before getting away HAMBURGER. The routing probability of this station is one when the client comes from the "Eating Area" station. There are not arrivals from other stations to the "Trash" station.
Blog: 
http://hamburgueserias.blogspot.com/

The possible failures for our system are greater than we mentioned before. At the "Ordering" station, an additional set-up is that there is no more billing paper, and the cashier has to put it in the register.  Another possible failure is that there is no change for the client and the cashier has to ask to the other server for exchange some money. Finally, it can appear a failure when the product that the client wants to order is not available in the system. Thus, the client has to think about another product in the menu.
Blog:
http://e9-restaurantes.blogspot.com/

For our system, it is possible that a client arrives to the system and decide not to get in because the place is crowded. The decision is based on the number of people in queue. If the queue is apparently long, then the client prefers going somewhere else.
Blog:
http://fastproyectb3.blogspot.com/

The mentioned blogs were chosen according to the following reasons:
·         The systems are based on a fast-food restaurant, where the speed of service is mainly important to improve customer service. That’s why restaurants avoid making their clients to wait for so long in line. Thus, it is expected that the number of clients in queue is minimal.
·         The products that are offered are: hamburgers, salads, bakery products, French fries, and so forth.
·         There is optional for the client to go to a particular station, because it depends on his willingness. For example, a costumer who does not want to get sauces, sugar, salt, etc. Or there is the possibility that he wants his meal to-go.
·         Service takes place in two ways. First, there are people who take and deliver the order. Second, there is self-service for each client.
We hope you enjoyed our entry.
Thank you for reading us,
Nataly Patacón
Camila Fonseca
Fernand Malagón
Alejandro Moreno

lunes, 12 de septiembre de 2011

PERFORMANCE MEASURES

This week we have to calculate performance measures for the restaurant HAMBURGER, but assuming that the distribution of the inter-arrival times and service times is exponential. This assumption is very important to apply Jackson’s theorem and modeling the system as a Jackson network.

Thus, by Jackson’s theorem, the arrival rates are calculated with the following expression:


The system of equations of the total arrival rates of HAMBURGER is:


 Looking at the graph schema, in “System Data” publication, the system of equations is simplified as it’s shown. 



We assume that our system satisfy Jackson’s suppositions (exponential inter-arrival times, exponential service times, infinite capacity for all stations and reached stable state λi<sii for all i stations). Then, the arrival rates found correspond to inter-arrival times distributed exponential. In order to calculate performance measures, we use network formulas, shown in the next table.



We hope you enjoyed our entry.
Thank you for reading us,

Nataly Patacón
Camila Fonseca
Fernand Malagón
Alejandro Moreno

viernes, 2 de septiembre de 2011

SYSTEM DATA - TIMES DISTRIBUTION

Hello everyone!
This week we had to find the distribution of the inter arrival time and service time of each station. For this task, we applied goodness-of-fit test for each station service time and for the inter arrival time, by using an statistical software (Crystal Ball). The results are presented in the next charts.

The following graphs show the closest distribution found by the software. However, none distribution fitted perfectly to our taken times.







In order to find distributions, the following tables show how to calculate the mean and the variance for  each one. The results for the distribution of the service times, and the inter-arrival time, are shown in the second table.


Time Units in minutes.


Thank you for reading us!
Regards,

Nataly Patacón
Camila Fonseca
Fernand Malagón
Alejandro Moreno