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This graduate-level textbook covers modelling, programming and
analysis of stochastic computer simulation experiments, including
the mathematical and statistical foundations of simulation and why
it works. The book is rigorous and complete, but concise and
accessible, providing all necessary background material.
Object-oriented programming of simulations is illustrated in
Python, while the majority of the book is programming language
independent. In addition to covering the foundations of simulation
and simulation programming for applications, the text prepares
readers to use simulation in their research. A solutions manual for
end-of-chapter exercises is available for instructors.
This graduate-level textbook covers modelling, programming and
analysis of stochastic computer simulation experiments, including
the mathematical and statistical foundations of simulation and why
it works. The book is rigorous and complete, but concise and
accessible, providing all necessary background material.
Object-oriented programming of simulations is illustrated in
Python, while the majority of the book is programming language
independent. In addition to covering the foundations of simulation
and simulation programming for applications, the text prepares
readers to use simulation in their research. A solutions manual for
end-of-chapter exercises is available for instructors.
A coherent introduction to the techniques for modeling dynamic
stochastic systems, this volume also offers a guide to the
mathematical, numerical, and simulation tools of systems analysis.
Suitable for advanced undergraduates and graduate-level industrial
engineers and management science majors, it proposes modeling
systems in terms of their simulation, regardless of whether
simulation is employed for analysis.
Beginning with a view of the conditions that permit a
mathematical-numerical analysis, the text explores Poisson and
renewal processes, Markov chains in discrete and continuous time,
semi-Markov processes, and queuing processes. Each chapter opens
with an illustrative case study, and comprehensive presentations
include formulation of models, determination of parameters,
analysis, and interpretation of results. Programming
language-independent algorithms appear for all simulation and
numerical procedures. Solutions to the exercises are available upon
request from the publisher at [email protected].
This Handbook is a collection of chapters on key issues in the
design and analysis of computer simulation experiments on models of
stochastic systems. The chapters are tightly focused and written by
experts in each area. For the purpose of this volume "simulation"
refers to the analysis of stochastic processes through the
generation of sample paths (realization) of the processes.
Attention focuses on design and analysis issues and the goal of
this volume is to survey the concepts, principles, tools and
techniques that underlie the theory and practice of stochastic
simulation design and analysis. Emphasis is placed on the ideas and
methods that are likely to remain an intrinsic part of the
foundation of the field for the foreseeable future. The chapters
provide up-to-date references for both the simulation researcher
and the advanced simulation user, but they do not constitute an
introductory level 'how to' guide.
Computer scientists, financial analysts, industrial engineers,
management scientists, operations researchers and many other
professionals use stochastic simulation to design, understand and
improve communications, financial, manufacturing, logistics, and
service systems. A theme that runs throughout these diverse
applications is the need to evaluate system performance in the face
of uncertainty, including uncertainty in user load, interest rates,
demand for product, availability of goods, cost of transportation
and equipment failures.
* Tightly focused chapters written by experts
* Surveys concepts, principles, tools, and techniques that underlie
the theory and practice of stochastic simulation design and
analysis
* Provides an up-to-date reference for bothsimulation researchers
and advanced simulation users
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