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Computer Science - Theoretical Computer Science | Introduction to Discrete Event Systems

Introduction to Discrete Event Systems

Cassandras, Christos, Lafortune, St├ęphane

1999, XLIV, 823 p.

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A substantial portion of this book is a revised version of Discrete Event Systems: Modeling and Performance Analysis (1993), which was written by the first author and received the 1999 Harold Chestnut Prize, awarded by the International Federation of Automatic Control (IFAC) for best control engineering textbook. This new expanded book is a comprehensive introduction to the field of discrete event systems, emphasizing breadth of coverage and accessibility of the material to readers with different backgrounds. Its key feature is the emphasis placed on a unified modeling framework that transcends specific application areas and allows linking of the following topics in a coherent manner: language and automata theory, supervisory control, Petri net theory, (max,+) algebra, Markov chains and queueing theory, discrete-event simulation, perturbation analysis, and concurrent estimation techniques.

Introduction to Discrete Event Systems will be of interest to advanced-level students in a variety of disciplines where the study of discrete event systems is relevant: control, communications, computer engineering, computer science, manufacturing engineering, operations research, and industrial engineering.

Content Level » Research

Keywords » Algebra - Counting - Finite - System - System Dynamics - calculus - communication - control engineering - function - systems theory

Related subjects » Applications - Mathematics - Operations Research & Decision Theory - Production & Process Engineering - Theoretical Computer Science

Table of contents 

Preface. 1: Systems and Models. Introduction. System and Control Basics. Discrete Event Systems. Summary of System Classifications. The Goals of System Theory. Summary & Problems. Selected References. 2: Languages and Automata. Introduction. The Concepts of Languages and Automata. Operations on Automata. Finite-State Automata. Analysis of Discrete-Event Systems. Summary & Problems. Selected References. 3: Supervisory Control. Introduction. Feedback Control with Supervisors. Specifications on Controlled System. Dealing with Uncontrollability. Dealing with Blocking. Modular Control. Dealing with Unobservability. Decentralized Control. Summary & Problems. Selected References. 4: Petri Nets. Introduction. Petri Net Basics. Comparison of Petri Nets and Automata. Analysis of Petri Nets. Summary & Problems. Selected References. 5: Timed Models. Introduction. Timed Automata. Timed Petri Nets. Dioid Algebras. Concluding Comments. Summary & Problems. Selected References. 6: Stochastic Times Automata. Introduction. Stochastic Process Basics. Stochastic Clock Structures. Stochastic Timed Automata. The Generalized Semi-Markov Process. The Poisson Counting Process. Properties of the Poisson Process. Automata with Poisson Clock Structure. Extensions of the GSMP. Summary & Problems. Selected References. 7: Markov Chains. Introduction. Discrete-Time Markov Chains. Continuous-Time Markov Chains. Birth-Death Chains. Uniformization of Markov Chains. Summary & Problems. Selected References. 8: Introduction to Queueing Theory. Introduction. Specification of Queueing Models. Performance of a Queueing System. Queueing System Dynamics. Little's Law. Simple Markovian Queueing Systems. Markovian Queueing Networks. Non-Markovian Queueing Systems. Summary & Problems. Selected References. 9: Controlled Markov Chains. Introduction. Applying 'Control' in Markov Chains. Markov Decision Processes. Solving Markov Decision Problems. Control of Queueing Systems. Summary & Problems. Selected References. 10: Introduction to Discrete-Event Simulation. Introduction. The Event Scheduling Scheme. The Process-Oriented Simulation Scheme. Discrete-Event Simulation Languages. Random Number Generation. Random Variate Generation. Output Analysis. Summary & Problems. Selected References. 11: Sensitivity Analysis and Concurrent Estimation.Introduction. Sample Functions and Their Derivatives. Perturbation Analysis: Some Key Ideas. PA of GI/G/ 1 Queueing Systems. IPA for Stochastic Timed Automata. Sensitivity Estimation Revisited. Extensions of IPA. Smoothed Perturbation Analysis (SPA). PA for Finite Parameter Changes. Concurrent Estimation. Summary & Problems. Selected References. I. Review of Probability Theory. II. IPA Estimator. Index. About the Authors.

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