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Introduction to Matrix-Analytic Methods in Queues 2
No full textMatrix-analytic methods (MAM) were introduced by Professor Marcel Neuts and have been applied to a variety of stochastic models since.In order to provide a clear and deep understanding of MAM while showing their power, this book presents MAM concepts and explains the results using a number of worked-out examples. This book’s approach will inform and kindle the interest of researchers attracted to this fertile field.To allow readers to practice and gain experience in the algorithmic and computational procedures of MAM, Introduction to Matrix?Analytic Methods in Queues 2 provides a number of computational exercises. It also incorporates simulation as another tool for studying complex stochastic models, especially when the state space of the underlying stochastic models under analytic study grows exponentially.This book’s detailed approach will make it more accessible for readers interested in learning about MAM in stochastic models
Analysis of a Queueing Model with MAP Arrivals and Heterogeneous Phase-Type Group Services
Get PDFQueueing models have proven to be very useful in real-life applications to enable the practitioners to optimize the limited resources to conduct their businesses as well as offer services efficiently. In general, we can group such applications into two sectors: manufacturing and service. These two sectors cover everything we deal with on a day-to-day basis. Queues in which the services are offered in blocks (or groups or batches) are well established in the literature and have a wide variety of applications in practice. In this paper, we look at one such queueing model in which the arrivals occur according to a Markovian arrival process and the services are offered in batches of varying sizes from 1 to a finite pre-determined constant, say, b. The service times are assumed to be of phase type with representation depending on the size of the group. Thus, the distributions considered are heterogeneous from both the representation and rate points of view. The model can be studied as a G I/M/1-type queue or as a QBD-model. The model is analyzed in steady state by establishing results including on the rate matrix and the waiting time distribution and providing a number of illustrative examples
Compatibility of Crude Oil Blends─Processing Issues Related to Asphaltene Precipitation, Methods of Instability Prediction─A Review
Get PDFProcessing crude oil of variable composition, especially due to the need to process crude oil blends obtained from various sources, presents a tremendous process challenge. This is mainly due to the destabilization of the colloidal system manifested mostly by the precipitation of the asphaltene fraction. The precipitation of asphaltenes from crude oil is a serious problem during extraction, transport, and processing. In the latter case, engineers and scientists have spent a lot of time determining what mechanisms are conducive to the occurrence of this phenomenon. On the one hand, there was a scientific curiosity about the principles of the nanoworld (nanoscale) of asphaltene molecules that determine their stability, and on the other hand, the willingness of process engineers in refineries to maintain the equipment in the best condition and maximize plant profits. Over the years, many methods have been developed to assess the stability of asphaltenes in crude oils and their blends, starting with methodologies based on the separation of a complex mixture into basic groups of compounds with similar properties (SARA) to sophisticated numerical models on an increasingly better understanding of interactions between molecules under changing conditions. In the former case, the basic instruments available in every laboratory are used whereas in the latter case technically advanced measurement systems capable of reproducing the real conditions of crude oil processing are employed. This paper reviews the methods of determining the stability of crude oils and their blends along with a critical assessment of their effectiveness
