An Introduction to Queueing Theory
Modeling and Analysis in Applications
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Beschreibung
This introductory textbook is designed for a one-semester course on queueing theory that does not require a course in stochastic processes as a prerequisite. By integrating the necessary background on stochastic processes with the analysis of models, the work provides a sound foundational introduction to the modeling and analysis of queueing systems for a broad interdisciplinary audience of students in mathematics, statistics, and applied disciplines such as computer science, operations research, and engineering.
Key features:
* An introductory chapter including a historical account of the growth of queueing theory in the last 100 years.
* A modeling-based approach with emphasis on identification of models using topics such as collection of data and tests for stationarity and independence of observations.
* Rigorous treatment of the foundations of basic models commonly used in applications with appropriate references for advanced topics.
* A chapter on modeling and analysis using computational tools.
* A comprehensive treatment of statistical inference for queueing systems.
* A discussion of operational and decision problems.
* Modeling exercises as a motivational tool, and review exercises covering background material on statistical distributions.
An Introduction to Queueing Theory may be used as a textbook by first-year graduate students in fields such as computer science, operations research, industrial and systems engineering, as well as related fields such as manufacturing and communications engineering. Upper-level undergraduate students in mathematics, statistics, and engineering may also use the book in an elective introductory course on queueing theory. With its rigorous coverage of basic material and extensive bibliography of the queueing literature, the work may also be useful to applied scientists and practitioners as a self-study reference for applications and further research.
This introductory textbook is designed for a one-semester course on queueing theory that does not require a course in stochastic processes as a prerequisite. The book provides a foundational introduction to the modeling and analysis of queueing systems.
There are several books on queueing theory available for students as well as - searchers. At the low end of mathematical sophistication, some provide usable f- mulas in a recipe fashion. At the high end there are research monographs on speci?c topics and books with an emphasis on theoretical analysis. In between there are a few textbooks with one common feature: all of them require an adequate background knowledge of probability and Markov processes that can be acquired normally with a semester-length graduate course. Consequently, most people who deal with the modeling and analysis of queueing systems either do not take a course on the subject because it would require an extra semester, or take a course on queueing systems without the necessary background and learn only how to use the results. This book is addressedtoremedythissituationbyprovidingaone-semesterfoundationalintrod- tion to the theory necessary for modeling and analysis of systems while developing the essential Markov process concepts and techniques using queueing processes as examples. Some of the key features of the book also distinguish it from others. Its introd- tory chapter includes a historical perspective on the growth of queueing theory in the last 100 years. With an emphasis on modeling and analysis it deals with topics such as identi?cation of models, collection of data, and tests for stationarity and indep- dence of observations. It provides a rigorous treatment of basic models commonly used in applications with references for advanced topics.
The first textbook on queueing theory that does not require a course in stochastic processes as a prerequisite; suitable for applied science students not needing a specialized course in queueing theory
For a broad interdisciplinary audience of advanced undergraduates, graduate students, researchers, and practitioners in applied mathematics, statistics, computer science, operations research, and industrial, systems, manufacturing, and communications engineering
Modeling exercises are included as a motivational tool; review exercises cover background material on statistical distributions
Includes a chapter on modeling using computational tools
Minimal prerequisites: calculus with some differential equations and an undergraduate course in probability and statistics
Chapter 10 material on statistical methods does not appear in any other introductory or even more advanced textbooks on queueing theory
Instructor's guide to solutions of exercises available upon request
This introductory textbook is designed for a one-semester course on queueing theory that does not require a course in stochastic processes as a prerequisite. By integrating the necessary background on stochastic processes with the analysis of models, the work provides a sound foundational introduction to the modeling and analysis of queueing systems for a broad interdisciplinary audience of students in mathematics, statistics, and applied disciplines such as computer science, operations research, and engineering.
Containing exercises and examples as motivational and background review tools, An Introduction to Queueing Theory may be used as a textbook by first-year graduate students in fields such as computer science, operations research, industrial and systems engineering, as well as related fields such as manufacturing and communications engineering. Upper-level undergraduate students in mathematics, statistics, and engineering may also use the book in an elective introductory course on queueing theory. With its rigorous coverage of basic material and extensive bibliography of the queueing literature, the work may also be useful to applied scientists and practitioners as a self-study reference for applications and further research.
Autor*in
U. Narayan Bhat
Themen in »An Introduction to Queueing Theory«
Markov Markov models Operations Research calculus communication data collection decision problems modeling queueing models queueing theory stationarity tests statistical distributions statistical inference statistics stochastic processes
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From the reviews:
"This is a new addition to the literature in Queueing Theory, which has been a subject area of intense interest because of its theoretical [richness] and wide applicability. This book has brought a freshness and novelty as it deals mainly with modeling and analysis in applications as well as with statistical inference for queueing problems. The book includes basics of stochastic processes and some mathematical topics, in addition to a chapter written by two computer scientists on modeling and analysis using computational tools, with useful simulation programs...With his 40 years of valuable experience in teaching and high level research in this subject area, Professor Bhat has been able to achieve what he aimed: to make [the work] somewhat different in content and approach from other books."—Assam Statistical Review
“The huge range of applications makes queueing theory an interesting object of study for students of mathematics, computer science, operations research and engineering. This book is an introduction to queueing theory. … The book also contains 3 appendices about Poisson and Markov processes and other background material … . the extensive bibliography of the queueing literature (202 references) which is given at the end of the book … help readers to further their research.” (Slobodanka S. Mitrović, Mathematical Reviews, Issue 2010 a)
“It is aimed at beginning graduate students and advanced undergraduate students in industrial engineering, electrical engineering, computer science, operations research, management science, mathematics, and statistics. … it covers a surprisingly large number of topics, including some that do not get much attention in other, much large books. … As far as writing style is concerned, Bhat tends to be precise and concise. … I like books that can be short yet make a major contribution.” (Myron Hlynka, Technometrics, Vol. 52 (1), February, 2010)
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