Computer generates the length of queue service

The created client will be served if a server is free, otherwise it is assigned a bone position in the queue (the shortest in the case several files) which is decremented as a server becomes free (that of its queue in If multiple files). When the customer occupies position1 and a server becomes free (in the server), he joined the service, its Dd service start date is updated the current date and the computer generates the length of his service under distribution service lives.

Equilibrium probabilities of Queue

Then updates:, we used the approximations established in and simulated equilibrium probabilities. For type there is no theoretical models that support all aspects of this approach, the results were simulated under the same initial conditions. Data The statistical analysis of all the data collected from our case study resulting in a model and allowed us to conclude that the arrivals of customers are rate fishmongers per second and that the holding times are Gamma with mean1 seconds and variance.

HenceIt is found that the intensity queue system of the service is high because the servers spend more 93% of their time occupied the bank which ensures full utilization of its resources. We customer service (exactly 2.81). There is an average of 12 customers in the system of which just over9 customers on average in the waiting line. A customer stays queue management an average of 13 minutes 16 seconds in the bank which 10 minutes8 seconds in the queue. A new customer happens 80% chance to wait before being served.

We find that both approaches provide virtually the same results. We can therefore conclude that there are no significant differences between the two types of disposition of the queue if not the organizational perspective (Order comfort and spaces). Indeed, the single queue model has some advantages: (i) for the bank, the presence of zero or one queue ensures order and comfort customers, and rational use of space;

For the queue system customers, the system of numbering does not require the physical presence in the queue, so they can sit and expect or care while waiting. This article, we used the formula to propose formulas for obtaining approximations performance measures queuing queue management model queue system M/ G/ c. We implemented a simulator provides model performance measures A/ B/ C where A, B are distributions or Deterministic, Exponential, or Gamma.

Digital application of queue system

Digital application with data from a case study in a Cameroonian private bank we have verified the theory that in a banking system, the type of organization the queue (single file for all servers or multiple files, one per server) does not affect system performance measures. As seen in Figure, the macro-steps are performed sequentially, but loop backs or flashbacks are possible to correct or complete the development of the model based on objectives.

Each of these macro-steps will now be detailed in several stages and it will bring out more precisely the sequence of the project. IV. Problem analysis The problem analysis queue system is an indispensable preliminary and of great importance, since it is in this stage that we must define precisely what we want to Obviously with the simulation, and what precision is expected.

It also determines the performance indicators that will allow verifying queue system if we achieved the goals it has set. Finally, we need to provide digital data to the queue mangement system model.

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