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Stochastic Processes and Models in Operations Research

Stochastic Processes and Models in Operations Research
Author: Anbazhagan, Neelamegam
Publisher: IGI Global
Total Pages: 359
Release: 2016-03-24
Genre: Business & Economics
ISBN: 1522500456

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Decision-making is an important task no matter the industry. Operations research, as a discipline, helps alleviate decision-making problems through the extraction of reliable information related to the task at hand in order to come to a viable solution. Integrating stochastic processes into operations research and management can further aid in the decision-making process for industrial and management problems. Stochastic Processes and Models in Operations Research emphasizes mathematical tools and equations relevant for solving complex problems within business and industrial settings. This research-based publication aims to assist scholars, researchers, operations managers, and graduate-level students by providing comprehensive exposure to the concepts, trends, and technologies relevant to stochastic process modeling to solve operations research problems.


Stochastic Models in Operations Research

Stochastic Models in Operations Research
Author: Daniel P. Heyman
Publisher: Courier Corporation
Total Pages: 564
Release: 2004-01-01
Genre: Mathematics
ISBN: 9780486432595

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This volume of a 2-volume set explores the central facts and ideas of stochastic processes, illustrating their use in models based on applied and theoretical investigations. Explores stochastic processes, operating characteristics of stochastic systems, and stochastic optimization. Comprehensive in its scope, this graduate-level text emphasizes the practical importance, intellectual stimulation, and mathematical elegance of stochastic models.


Operations Research Models and Methods

Operations Research Models and Methods
Author: Paul A. Jensen
Publisher: John Wiley & Sons
Total Pages: 708
Release: 2002-10-08
Genre: Technology & Engineering
ISBN: 0471380040

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In a rapidly developing field like Operations Research, its easy to get overwhelmed by the variety of topics and analytic techniques. Paul Jensen and Jonathan Bard help you master the expensive field by focusing on the fundamental models and methodologies underlying the practice of Operations Research. Bridging the gap between theory and practice, the author presents the quantitative tools and models most important to understanding modern operations research. You'll come to appreciate the power of OR techniques in solving real-world problems and applications in your own field. You'll learn how to translate complex situations into mathematical models, solve models and turn models into solutions. This text is designed to bridge the gap between theory and practice by presenting the quantitative tools and models most suited for modern operations research. The principal goal is to give analysts, engineers, and decision makers a larger appreciation of their roles by defining a common terminology and by explaining the interfaces between the underlying methodologies. Features Divides each subject into methods and models, giving you greater flexibility in how you approach the material. Concise and focused presentation highlights central ideas. Many examples throughout the text will help you better understand mathematical material.


Stochastic Processes

Stochastic Processes
Author: M. Girault
Publisher: Springer Science & Business Media
Total Pages: 136
Release: 2012-12-06
Genre: Business & Economics
ISBN: 3642882692

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Existing works on stochastic processes belong to a field of abstract mathematics which puts them beyond the scope of the non-specialist. The preoccupations of research mathematicians being more often than not distant from the practical problems of experimental methodology, the needs of practical workers, though real, are not met by the majority of works that. deal with processes. By "practical workers", we mean research scientists in all the different disciplines: Physics, Chemistry, Biology, Medicine, Population, Economics, Organisation, Operational Research etc. Indeed, all scientific research today touches upon complex fields in which deterministic models can be useful for no more than an element ary and simple approximation. The Calculus of Probability although offering some interesting models is still inadequate in many instances, particularly in the study of evolving systems. The practical worker must therefore have at his disposal a set of original and varied stochastic models. These models must not be too general, for in that case not only would their theoretical study prove difficult, but above all the adaptation of such models to an observed system would lead to an estimation of a great number of parameters on the basis of a necessarily restricted sample. This would constitute an insuperable difficulty for the practical scientist. It is therefore essential for him to have at his disposal a varied range of very characteristic models.


Stochastic Processes and Models

Stochastic Processes and Models
Author: David Stirzaker
Publisher: OUP Oxford
Total Pages: 342
Release: 2005-07-21
Genre: Mathematics
ISBN: 0191582999

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Stochastic Processes and Models provides a concise and lucid introduction to simple stochastic processes and models. Including numerous exercises, problems and solutions, it covers the key concepts and tools, in particular: random walks, renewals, Markov chains, martingales, the Wiener process model for Brownian motion, and diffusion processes, concluding with a brief account of the stochastic integral and stochastic differential equations as they arise in option-pricing. The text has been thoroughly class-tested and is ideal for an undergraduate second course in probability.


Introduction to Modeling and Analysis of Stochastic Systems

Introduction to Modeling and Analysis of Stochastic Systems
Author: V. G. Kulkarni
Publisher: Springer
Total Pages: 323
Release: 2010-11-03
Genre: Mathematics
ISBN: 1441917721

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This book provides a self-contained review of all the relevant topics in probability theory. A software package called MAXIM, which runs on MATLAB, is made available for downloading. Vidyadhar G. Kulkarni is Professor of Operations Research at the University of North Carolina at Chapel Hill.


Constructive Computation in Stochastic Models with Applications

Constructive Computation in Stochastic Models with Applications
Author: Quan-Lin Li
Publisher: Springer Science & Business Media
Total Pages: 693
Release: 2011-02-02
Genre: Mathematics
ISBN: 364211492X

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"Constructive Computation in Stochastic Models with Applications: The RG-Factorizations" provides a unified, constructive and algorithmic framework for numerical computation of many practical stochastic systems. It summarizes recent important advances in computational study of stochastic models from several crucial directions, such as stationary computation, transient solution, asymptotic analysis, reward processes, decision processes, sensitivity analysis as well as game theory. Graduate students, researchers and practicing engineers in the field of operations research, management sciences, applied probability, computer networks, manufacturing systems, transportation systems, insurance and finance, risk management and biological sciences will find this book valuable. Dr. Quan-Lin Li is an Associate Professor at the Department of Industrial Engineering of Tsinghua University, China.


Bayesian Analysis of Stochastic Process Models

Bayesian Analysis of Stochastic Process Models
Author: David Insua
Publisher: John Wiley & Sons
Total Pages: 315
Release: 2012-04-02
Genre: Mathematics
ISBN: 1118304039

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Bayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified treatment of Bayesian analysis of models based on stochastic processes, covering the main classes of stochastic processing including modeling, computational, inference, forecasting, decision making and important applied models. Key features: Explores Bayesian analysis of models based on stochastic processes, providing a unified treatment. Provides a thorough introduction for research students. Computational tools to deal with complex problems are illustrated along with real life case studies Looks at inference, prediction and decision making. Researchers, graduate and advanced undergraduate students interested in stochastic processes in fields such as statistics, operations research (OR), engineering, finance, economics, computer science and Bayesian analysis will benefit from reading this book. With numerous applications included, practitioners of OR, stochastic modelling and applied statistics will also find this book useful.