Modern Simulation and Modeling
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More About This Title Modern Simulation and Modeling
English
A step-by-step guide for today's modeling and simulation practices
This new guide for modeling and simulation of discrete-event systems (DES) demonstrates why simulation is fast becoming the method of choice for the evaluation of system performance in science, engineering, and management. The book begins with the basics of conventional simulation, then proceeds to modern simulation-treating sensitivity analysis and optimization in a wide range of systems that exhibit complex interaction of discrete events. These include communications networks, flexible manufacturing systems, PERT (project evaluation and review techniques) networks, queueing systems, and more.
Less focused on theory than on presenting a clear approach to practical applications, Modern Simulation and Modeling:
* Emphasizes concepts rather than mathematical completeness
* Integrates references and explanations of complex topics into the body of the text
* Provides an innovative chapter on rare-event probability estimation
* Describes the implementation of the score function (SF) method using the NSO simulation package
* Features 40 illustrations and numerous algorithms
* Offers extensive, end-of-chapter exercise sets
* Includes chapter bibliographies for further reading
Modern Simulation and Modeling is an essential text for graduate students of DES and stochastic processes and for undergraduate students in simulation. It is also an excellent reference for professionals in statistics and probability, mathematics, and management science.
This new guide for modeling and simulation of discrete-event systems (DES) demonstrates why simulation is fast becoming the method of choice for the evaluation of system performance in science, engineering, and management. The book begins with the basics of conventional simulation, then proceeds to modern simulation-treating sensitivity analysis and optimization in a wide range of systems that exhibit complex interaction of discrete events. These include communications networks, flexible manufacturing systems, PERT (project evaluation and review techniques) networks, queueing systems, and more.
Less focused on theory than on presenting a clear approach to practical applications, Modern Simulation and Modeling:
* Emphasizes concepts rather than mathematical completeness
* Integrates references and explanations of complex topics into the body of the text
* Provides an innovative chapter on rare-event probability estimation
* Describes the implementation of the score function (SF) method using the NSO simulation package
* Features 40 illustrations and numerous algorithms
* Offers extensive, end-of-chapter exercise sets
* Includes chapter bibliographies for further reading
Modern Simulation and Modeling is an essential text for graduate students of DES and stochastic processes and for undergraduate students in simulation. It is also an excellent reference for professionals in statistics and probability, mathematics, and management science.
English
REUVEN Y. RUBINSTEIN has written extensively on simulation while teaching in the Industrial Engineering and Management Department of the Technion-Israel Institute of Technology. BENJAMIN MELAMED is a member of the Rutgers University, Faculty of Management.
English
CONVENTIONAL SIMULATION.
Systems, Models, and Simulation.
Random Numbers, Variates, and Stochastic Process Generation.
Output Analysis of Discrete-Event Systems via Simulation.
Variance Reduction Techniques.
MODERN SIMULATION.
Sensitivity Analysis and Optimization of Discrete-Event Static Systems (DESS).
Sensitivity Analysis and Optimization of Discrete-Event Dynamic Systems: Distributed Parameters.
Sensitivity of Analysis of Discrete-Event Dynamic Systems: Structural Parameters.
Response Surface Methodology via the Score Function Method.
Estimating Rare-Event Probabilities and Related Optimization Issues.
Index.
Systems, Models, and Simulation.
Random Numbers, Variates, and Stochastic Process Generation.
Output Analysis of Discrete-Event Systems via Simulation.
Variance Reduction Techniques.
MODERN SIMULATION.
Sensitivity Analysis and Optimization of Discrete-Event Static Systems (DESS).
Sensitivity Analysis and Optimization of Discrete-Event Dynamic Systems: Distributed Parameters.
Sensitivity of Analysis of Discrete-Event Dynamic Systems: Structural Parameters.
Response Surface Methodology via the Score Function Method.
Estimating Rare-Event Probabilities and Related Optimization Issues.
Index.
