The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.Cover design: eStudio Calamar S.L.Printed on acid-free paper PrefaceThis textbook was developed to fill the need for an accessible but comprehensive presentation of the analytical approaches for modeling and analyzing models of manufacturing and production systems. It is an out growth of the efforts within the Industrial and Systems Engineering Department at Texas A&M to develop and teach an analytically based undergraduate course on probabilistic modeling of manufacturing type systems. The level of this textbook is directed at undergraduate and masters students in engineering and mathematical sciences. The only prerequisite for students using this textbook is a previous course covering calculus-based probability and statistics. The underlying methodology is queueing theory, and we shall develop the basic concepts in queueing theory in sufficient detail that the reader need not have previously covered it. Queueing theory is a well-established discipline dating back to the early 1900's work of A. K. Erlang, a Danish mathematician, on telephone traffic congestion. Although there are many textbooks on queueing theory, these texts are generally oriented to the methodological development of the field and exact results and not to the practical application of using approximations in realistic modeling situations. The application of queueing theory to manufacturing type systems started with the approximation based work of Ward Whitt in the 1980's. His paper on QNA (a queueing network analyzer) in 1983 is the base from which most applied modeling efforts have evolved.There are several textbooks with titles similar to this book. This text is about the development of analytical approximation models and their use in evaluating factory performance. The tools needed for the analytical approach are fully developed. One useful non-analytical tool that is not fully developed in this textbook is simulation modeling. In practice as well as in the development of the models in this text, simulation is extensively used as a verification tool. Even though the development of simulation models is only modestly addressed, we would encourage instructors who use this book in their curriculum after a simulation course to ask students to simulate some of the homework problems so that a comparison can be made of the analysis using the models presented here with simulation models. By developing simulation models students will have a better understanding of the modeling assumptions and the accuracy of the analytical approximations. In addition several chapters include an appendix that contains instructions in the use of Microsoft Excel as an aid in modeling or in building simple simulation models.For this second edition, suggestions from various instructors who have used the textboo...
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