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The arrival process in this article is also not a queue management process with train receipts As already mentioned, packages arrive independently of each other according to a Poisson distribution. Nevertheless, this study gives us a first impression of the system with strictly one reservation location and shows a way to determine the delay analytically, however, this way only applies to a system with the above mentioned specific parameters. In a final section of this article, charts are plotted comparing the characteristics of the QMA R system with the characteristics of an FCFS system and an absolute priority system for type packages. The values in the graph were obtained by applying the above formulas for the average delay. In this section, it appears online queue management system that a system with one reservation location certainly reduces the average delay for packets of stream compared to the FCFS system when the partial load of prioritized flow is not too high.

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How the system behaves when multiple reservation locations are included online queue management system in the queue is not yet analyzed in this paper. In order to avoid complex calculations. Here to a waiting line system is assumed with types of packages and reservation location. Type packets are defined as delay sensitive packets, type packets are previously delay insensitive. In this article, the operating time is always assumed to be exactly time slot, while the number of arrivals per time slot is independent and evenly distributed.

This article provides a distribution for the delays for customer queue management system the types of packages. From that derivative distribution a formula is then determined for the average, the variance and the distribution of the tail for the delays of packages. In an article by Layers and, further work is being done on the article by De Must, and Brunei, with this extension that the operating times are not fixed at a strict time slot, but are determined by a discrete distribution. The analysis made in this article is based on the use of probability generating functions and also leads to obtaining a distribution, an average and a variance for the delays for type and type packages. How the distribution of the delays for type and type packages is precisely determined is beyond the scope of this short literature review, but it may be interesting to see how this system describes the system and which numerical results were obtained. In order to be able to make the analysis, it is ensured that every state of the system can be described by a so called system state vector. On the basis of this system state vector all possible situations that the system can take are described. If a simulation program is to be designed in this master s thesis, it is important to correctly describe the situation in which the system is located. The three parameters that are included here in the system state vector customer queue management system will certainly be included in the simulation program. To find reflection.

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It will also have to be determined for every package arriving at the operating station and must be re evaluated per time slot. Also, the position of the reservation location, or even the reservation locations in the simulation program, will have to be re evaluated for each time slot in the simulation. The total system content defined as in this article will have to be tracked as a queue and the number of packages it contains. In this article a regime distribution is derived for the system state vector and on the basis of this distribution a distribution is derived for the package delays for type and type packages. Based on the obtained distributions, numerical results are further described that further look here describe the characteristics of the system. In this article, independent equally divided Bernoulli arrivals are assumed.