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It is also necessary to consider that the queue management average time of service is uniform for all the employees. The values corresponding to the waiting times length of stay in the system and average waiting time in queue, analyzed in conditions of stability, are superior to those actually observed since simplifications have been made by eliminating servers that spent very little of their time-serving customers. This approach to reality does not consider the time allocated by other female employees to alleviate the queue with the consequent punctual increase in the service rate that allows customers to wait no longer than- minutes in any case. Since the number of servers is not fixed and WinQSB does not allow the introduction of decimals in the number of servers, it has been considered as the best approximation two servers and half working at the same service rate, the average between the results obtained from solving the system with two and with three servers. Which the calculations will be made on the variables of interest that approximate the behavior of the system with two and a half servers.
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These ures do adjust more loyally to the customer queue management system observed situation, with eventual queues of up to four customers and waiting time around twenty minutes. This approximation, therefore, gives us ures that correspond to reality in a more precise way than those obtained previously. With the new set of data taken many predictions can not be made since the sample is small and the results would not link reality with fidelity. Yes, we can carry out an analysis of the sensitivity on the variable arrival rate which would make us available to obtain data about the operation of the waiting line for the current con uration of the model in different ranges of arrival flows Of customers. Thanks to this analysis we are able to affirm that the system will be operating under stability conditions with two employees provided that the customer queue management system time between arrivals is not less than five minutes or the arrival rate is not greater than.
In addition, the waiting line is view here able to operate attending arrival rates of, customers per minute minutes and seconds between one client and the next without too negative consequences since the average number of customers in the system does not exceed five, which means that on average there are not more than three customers in tail. The average time of stay in the system would be a bit high for arrival rates of around. But without significant negative consequences since the average waiting time is minutes, ure around which is the tolerance Estimated waiting time for customers. In this End of Degree Work, the analysis of the different queuing systems has been carried out through which the different waiting lines that we find in reality can be analyzed. It has been found that queuing systems cover a wide spectrum of problems and each specific waiting line must be modeled according to its characteristics, so that there is no possibility of carrying out systematic analyzes valid for different cases.
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It has also revealed the complexity inherent in online queue management system modeling queue systems that do not respond to the simplest cases. Despite this, it demonstrates a great utility and possibility of analysis through specialized software such as WinQSB and other much more complete existing payment applications. With analysis of this type, decision-making can be assisted, supported by monetary estimates of the consequences with a significant level of confidence. Not only are higher revenues or lower costs achieved directly with this type online queue management system of research, but also increase the quality of customer service which influences the long-term image of the company and the level of sales.