Qualitative Simulation Modeling And Analysis

Recently there has been considerable interest in qualitative methods in simulation and mathematical model- ing. Qualitative Simulation Modeling and Analysis is the first book to thoroughly review fundamental concepts in the field of qualitative simulation. The book will appeal to readers in a variety of disciplines including researchers in simulation methodology, artificial intelligence and engineering. This book boldly attempts to bring together, for the first time, the qualitative techniques previously found only in hard-to-find journals dedicated to single disciplines. The book is written for scientists and engineers interested in improving their knowledge of simulation modeling. The "qualitative" nature of the book stresses concepts of invariance, uncertainty and graph-theoretic bases for modeling and analysis.

Designed for courses at advanced undergraduate or graduate level in industrial engineering and business, this text provides a review of various aspects of simulation study, including modelling, simulation software, validation, and output data analysis.

Comprehensive, state-of-the-art coverage of every important simulation technique This fully-revised book has the most comprehensive and up-to-date coverage of all aspects of a simulation study. Equally well suited for use in university courses, simulation practice, and self-study, the book offers clear and intuitive explanations as well as 300 figures, 218 examples, and 217 problems. You will get detailed discussions on modeling and simulation, simulation software, model verification and validation, input modeling, random-number and variate generation, statistical design and analysis of simulation experiments, experimental design, simulation optimization, agent-based simulation, machine learning, and much more. Authored by an operations research analyst and industrial engineer with more than 40 years of experience, Simulation Modeling and Analysis is widely regarded as the "bible" of simulation and now has more than 178,000 copies in print and 23,700 citations. This sixth edition has been streamlined, with several chapters downsized to eliminate outdated simulation programs or statistical techniques that are rarely used in practice and are unnecessarily complicated. Most analyses of simulation output data can now be done using three simple and familiar statistical formulas or expressions. A new chapter covers AI and machine learning and their application to simulation. Covers what are arguably the three most-innovative and popular simulation-software packages: AnyLogic, FlexSim, and Simio Includes a set of instructor's resources Has been used at universities such as University of California-Berkeley, Stanford, Georgia Tech, Michigan, Cornell, Purdue, Virginia Tech, Penn State, Wisconsin, Columbia, Texas A&M, Washington, and Johns Hopkins Written by a world-class expert in the field and an experienced educator who has presented more than 550 simulation and statistics short courses in 20 countries

Since the publication of the first edition in 1982, the goal of Simulation Modeling and Analysis has always been to provide a comprehensive, state-of-the-art, and technically correct treatment of all important aspects of a simulation study. The book strives to make this material understandable by the use of intuition and numerous figures, examples, and problems. It is equally well suited for use in university courses, simulation practice, and self study. The book is widely regarded as the “bible” of simulation and now has more than 100,000 copies in print. The book can serve as the primary text for a variety of courses; for example: • A first course in simulation at the junior, senior, or beginning-graduate-student level in engineering, manufacturing, business, or computer science (Chaps. 1 through 4, and parts of Chaps. 5 through 9). At the end of such a course, the students will be prepared to carry out complete and effective simulation studies, and to take advanced simulation courses. • A second course in simulation for graduate students in any of the above disciplines (most of Chaps. 5 through 12). After completing this course, the student should be familiar with the more advanced methodological issues involved in a simulation study, and should be prepared to understand and conduct simulation research. • An introduction to simulation as part of a general course in operations research or management science (part of Chaps. 1, 3, 5, 6, and 9).

Modeling and Simulation have become endeavors central to all disciplines of science and engineering. They are used in the analysis of physical systems where they help us gain a better understanding of the functioning of our physical world. They are also important to the design of new engineering systems where they enable us to predict the behavior of a system before it is ever actually built. Modeling and simulation are the only techniques available that allow us to analyze arbitrarily non-linear systems accurately and under varying experimental conditions. Continuous System Modeling introduces the student to an important subclass of these techniques. They deal with the analysis of systems described through a set of ordinary or partial differential equations or through a set of difference equations. This volume introduces concepts of modeling physical systems through a set of differential and/or difference equations. The purpose is twofold: it enhances the scientific understanding of our physical world by codifying (organizing) knowledge about this world, and it supports engineering design by allowing us to assess the consequences of a particular design alternative before it is actually built. This text has a flavor of the mathematical discipline of dynamical systems, and is strongly oriented towards Newtonian physical science.

one-of-a-kind introduction to the theory and application of modeling and simulation techniques in the realm of international studies Modeling and Simulation for Analyzing Global Events provides an orientation to the theory and application of modeling and simulation techniques in social science disciplines. This book guides readers in developing quantitative and numeric representations of real-world events based on qualitative analysis. With an emphasis on gathering and mapping empirical data, the authors detail the steps needed for accurately analyzing global events and outline the selection and construction of the best model for understanding the event¿s data. Providing a theoretical foundation while also illustrating modern examples, the book contains three parts: Advancing Global Studies—introduces the what, when, and why of modeling and simulation and also explores its brief history, various uses, and some of the advantages and disadvantages of modeling and simulation in problem solving. In addition, the differences in qualitative and quantitative research methods, mapping data, and conducting model validation are also discussed. Modeling Paradigms—examines various methods of modeling including system dynamics, agent-based modeling, social network modeling, and game theory. This section also explores the theory and construction of these modeling paradigms, the fundamentals for their application, and various contexts for their use. Modeling Global Events—applies the modeling paradigms to four real-world events that are representative of several fundamental areas of social science studies: internal commotion within an anarchic state, a multi-layered study of the Solidarity movement in Poland, uni-lateral military intervention, and the issue of compellence and deterrence during a national security crisis. Modeling and Simulation for Analyzing Global Events is an excellent book for statistics, engineering, computer science, economics, and social sciences courses on modeling and simulation at the upper-undergraduate and graduate levels. It is also an insightful reference for professionals who would like to develop modeling and simulation skills for analyzing and communicating human behavior observed in real-world events and complex global case studies.

"This is an excellent and well-written text on discrete event simulation with a focus on applications in Operations Research. There is substantial attention to programming, output analysis, pseudo-random number generation and modelling and these sections are quite thorough. Methods are provided for generating pseudo-random numbers (including combining such streams) and for generating random numbers from most standard statistical distributions." --ISI Short Book Reviews, 22:2, August 2002

This book offers a comprehensive and readable introduction to modern business and data analytics. It is based on the use of Excel, a tool that virtually all students and professionals have access to. The explanations are focused on understanding the techniques and their proper application, and are supplemented by a wealth of in-chapter and end-of-chapter exercises. In addition to the general statistical methods, the book also includes Monte Carlo simulation and optimization. The second edition has been thoroughly revised: new topics, exercises and examples have been added, and the readability has been further improved. The book is primarily intended for students in business, economics and government, as well as professionals, who need a more rigorous introduction to business and data analytics – yet also need to learn the topic quickly and without overly academic explanations.