Manufacturing produces real wealth for our society, is a source of employment for the population, and constitutes the backbone of the service sector. Recent years have seen the emergence of automated manufacturing systems (AMSs), driven by demand for increased productivity, flexibility, and competitiveness. These systems are highly capital-intensive, so it is important to be able to plan, predict, and manage their performance. Performance evaluation offers methods and tools for the design and operation of high-performance AMSs.

Performance evaluation methods for AMSs fall into two classes: performance measurement of existing systems and performance modeling. The latter can be either simulation or analytical. Traditionally, discrete event simulation has been widely accepted and employed in factory environments for the study of issues in design and operation. Meanwhile, analytical modeling tools are becoming increasingly popular and have emerged as an alternative to simulation. Discrete event dynamic system models can be broadly classified as qualitative or quantitative. This book is mainly concerned with the quantitative analytical modeling of AMSs.

The book has an introduction, four major chapters, and an epilogue. Chapter 2 provides a logical overview of AMSs, covering the hardware, the software, and integration issues. It presents the evolution of manufacturing, the product cycle in manufacturing plants, different types of plant configurations, performance measures of manufacturing systems, the building blocks of AMSs, plant layouts, flexible manufacturing systems, and computer control and integration issues.

Chapter 3 gives a detailed presentation of Markov chain models. The chapter starts with preliminary material on memoryless random variables, stochastic processes, and the Poisson process. The authors then discuss discrete and continuous time Markov chain models, the Markov model of a transfer line, birth and death processes in manufacturing, time-reversible Markov chains, modeling of deadlocks, semi-Markov processes, and transient analysis of manufacturing systems. The chapter concludes with an overview of computational issues in Markov analysis.

Chapter 4, on queueing models, deals with queues and queueing networks. The topics covered under queues are Little’s law, M/M/1 queues, M/M/m queues, batch arrival queueing systems, queues with general distributions, and queues with breakdowns. The chapter includes a case study on analyzing the performance of a flexible machine center, using polling models. Under queueing networks, the authors cover Little’s law, open and closed queueing networks, product form networks, queueing networks with blocking, approximate analysis of queueing systems, and performability analysis.

Petri net models are the subject of chapter 5. The discussion starts with the classical Petri net models and proceeds to stochastic and generalized stochastic Petri nets, a case study of a Kanban production system, deadlock analysis using Petri nets, and extended classes of timed Petri nets. The chapter concludes with a discussion of integrated models that use both queueing networks and Petri nets.

The epilogue is dedicated to important issues in AMS modeling and design that fall outside the scope of this text.

The audience for this book comprises engineering students at the senior undergraduate level, first-year graduate students, research and development engineers, and factory managers. The authors have succeeded in making this work a textbook in the true sense. The book is interesting to read, well presented, and well illustrated, with a sufficient selection of exercises and references as well as an adequate index. Some exceptions to this general impression include the sometimes superfluous use of abbreviations in the text and an overloaded flowchart for the automated operation of flexible manufacturing systems in Figure 2.22. The book would also benefit from some general introductory references to database management systems, selected from the large number of textbooks available. In summary, this excellent textbook fulfills its basic goal of introducing analytical modeling tools and their use during the life cycle of an AMS.