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Introduction to Modeling and Simulation An essential introduction to engineering system modeling and simulation from a well-trusted source in engineering and education This new introductory-level textbook provides thirteen self-contained chapters, each covering an important topic in engineering systems modeling and simulation. The importance of such a topic cannot be overstated; modeling and simulation will only increase in importance in the future as computational resources improve and become more powerful and accessible, and as systems become more complex. This resource is a wonderful mix of practical examples, theoretical concepts, and experimental sessions that ensure a well-rounded education on the topic. The topics covered in Introduction to Modeling and Simulation are timeless fundamentals that provide the necessary background for further and more advanced study of one or more of the topics. The text includes topics such as linear and nonlinear dynamical systems, continuous-time and discrete-time systems, stability theory, numerical methods for solution of ODEs, PDE models, feedback systems, optimization, regression and more. Each chapter provides an introduction to the topic to familiarize students with the core ideas before delving deeper. The numerous tools and examples help ensure students engage in active learning, acquiring a range of tools for analyzing systems and gaining experience in numerical computation and simulation systems, from an author prized for both his writing and his teaching over the course of his over-40-year career. Introduction to Modeling and Simulation readers will also find: Numerous examples, tools, and programming tips to help clarify points made throughout the textbook, with end-of-chapter problems to further emphasize the material As systems become more complex, a chapter devoted to complex networks including small-world and scale-free networks - a unique advancement for textbooks within modeling and simulation A complementary website that hosts a complete set of lecture slides, a solution manual for end-of-chapter problems, MATLAB files, and case-study exercises Introduction to Modeling and Simulation is aimed at undergraduate and first-year graduate engineering students studying systems, in diverse avenues within the field: electrical, mechanical, mathematics, aerospace, bioengineering, physics, and civil and environmental engineering. It may also be of interest to those in mathematical modeling courses, as it provides in-depth material on MATLAB simulation and contains appendices with brief reviews of linear algebra, real analysis, and probability theory.

• 1. Nanostructural dynamics problems with complicating effects
• 2. Vibration of functionally graded piezoelectric material beams
• 3. Vibration of microstructural elements
• 4. Coupled shallow water wave equations
• 5. Natural convection in a nanofluid flow
• 6. Fractional fluid mechanics systems
• 7. Inverse problems in diffusion processes with uncertain parameters
• 8. Affine approach in solving linear structural dynamic problems with uncertain parameters
• 9. Numerical solution of Langevin stochastic differential equation with uncertain parameters
• 10. Fuzzy eigenvalue problems of structural dynamics using ANN
• 11. Artificial neural network approach for solving fractional order applied problems
• 12. Speech emotion recognition using deep learning
• 13. A user independent hand gesture recognition system using deep CNN feature fusion and machine learning technique
• 14. A survey on group modeling strategies for recommender systems
• 15. Extraction of glacial lakes in the Himalayan region using landsat imagery.
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In general, every problem of science and engineering is governed by mathematical models. There is often a need to model, solve and interpret the problems one encounters in the world of practical problems. Models of practical application problems usually need to be handled by efficient computational models. New Paradigms in Computational Modeling and Its Applications deals with recent developments in mathematical methods, including theoretical models as well as applied science and engineering. The book focuses on subjects that can benefit from mathematical methods with concepts of simulation, waves, dynamics, uncertainty, machine intelligence, and applied mathematics. The authors bring together leading-edge research on mathematics combining various fields of science and engineering. This perspective acknowledges the inherent characteristic of current research on mathematics operating in parallel over different subject fields. New Paradigms in Computational Modeling and Its Applications meets the present and future needs for the interaction between various science and technology/engineering areas on the one hand and different branches of mathematics on the other. As such, the book contains 13 chapters covering various aspects of computational modeling from theoretical to application problems. The first six chapters address various problems of structural and fluid dynamics. The next four chapters include solving problems where the governing parameters are uncertain regarding fuzzy, interval, and affine. The final three chapters will be devoted to the use of machine intelligence in artificial neural networks.
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This book is written to introduce computer simulations to undergraduate college students, freshmen to seniors, in STEM fields. The book starts with concepts from Basic Mathematics: Geometry, Algebra and Calculus, Properties of Elementary Functions (Polynomials, Exponential, Hyperbolic and Trigonometric Functions) are studied and simple differential equations representing these functions are derived. Numerical approximations of first and second order differential equations are studied in terms of finite differences on uniform grids. Computer solutions are obtained via recursive relations or solutions of simultaneous algebraic equations. Comparisons with the exact solutions (known a priori) allow the calculations of the error due to discretization. After the students build confidence in this approach, more problems where the solutions are not known a priori are tackled with applications in many fields. Next, the book gradually addresses linear differential equations with variable coefficients and nonlinear differential equations, including problems of bifurcation and chaos.Applications in Dynamics, Solid Mechanics, Fluid Mechanics, Heat Transfer, Chemical Reactions, and Combustion are included. Biographies of 50 pioneering mathematicians and scientists who contributed to the materials of the book are briefly sketched, to shed light on the history of these STEM fields.Finally, the main concepts discussed in the book, are summarized to make sure that the students do not miss any of them. Also, references for further readings are given for interested readers.
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• 1. Introduction
• 2. Stage 1 : basis function
• 3. Stage 2 : geometry
• 4. Stage 3 : computer aided design
• 5. Stage 4 : introduction to numerical simulation
• 6. Stage 5 : plates and shells
• 7. Stage 6 : integral equation
• 8. Stage 7 : the boundary element method for plane problems
• 9. Stage 8 : the boundary element method for three-dimensional problems
• 10. Stage 9 : the boundary element method with volume effects
• 11. Stage 10 : the time domain

The 5th edition of Ross's Simulation continues to introduce aspiring and practicing actuaries, engineers, computer scientists and others to the practical aspects of constructing computerized simulation studies to analyze and interpret real phenomena. Readers learn to apply results of these analyses to problems in a wide variety of fields to obtain effective, accurate solutions and make predictions about future outcomes. This latest edition features all-new material on variance reduction, including control variables and their use in estimating the expected return at blackjack and their relation to regression analysis. Additionally, the 5th edition expands on Markov chain monte carlo methods, and offers unique information on the alias method for generating discrete random variables. By explaining how a computer can be used to generate random numbers and how to use these random numbers to generate the behavior of a stochastic model over time, Ross's Simulation, 5th edition presents the statistics needed to analyze simulated data as well as that needed for validating the simulation model. * Additional material on variance reduction, including control variables and their use in estimating the expected return at blackjack and their relation to regression analysis* Additional material and examples on Markov chain Monte Carlo methods* Unique material on the alias method for generating discrete random variables* Additional material on generating multivariate normal vectors.
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• Preface ix Acknowledgements xi Contributing authors xiii 1 Introduction 1 Mike Christie, Andrew Cliffe, Philip Dawid and Stephen Senn 1.1 The origins of the SCAM project 1 1.2 The scope of modelling in the modern world 2 1.3 The different professions and traditions engaged in modelling 3 1.4 Different types of models 3 1.5 Different purposes for modelling 5 1.6 The purpose of the book 6 1.7 Overview of the chapters 6 References 8 2 Statistical model selection 11 Philip Dawid and Stephen Senn 2.1 Introduction 11 2.2 Explanation or prediction? 12 2.3 Levels of uncertainty 12 2.4 Bias variance trade-off 13 2.5 Statistical models 15 2.5.1 Within-model inference 16 2.6 Model comparison 18 2.7 Bayesian model comparison 18 2.7.1 Model uncertainty 19 2.7.2 Laplace approximation 20 2.8 Penalized likelihood 20 2.8.1 Bayesian information criterion 21 2.9 The Akaike information criterion 21 2.9.1 Inconsistency of AIC 23 2.10 Significance testing 23 2.11 Many variables 27 2.12 Data-driven approaches 28 2.12.1 Cross-validation 29 2.12.2 Prequential analysis 29 2.13 Model selection or model averaging? 30 References 31 3 Modelling in drug development 35 Stephen Senn 3.1 Introduction 35 3.2 The nature of drug development and scope for statistical modelling 36 3.3 Simplicity versus complexity in phase III trials 36 3.3.1 The nature of phase III trials 36 3.3.2 The case for simplicity in analysing phase III trials 37 3.3.3 The case for complexity in modelling clinical trials 38 3.4 Some technical issues 39 3.4.1 The effect of covariate adjustment in linear models 40 3.4.2 The effect of covariate adjustment in non-linear models 42 3.4.3 Random effects in multi-centre trials 44 3.4.4 Subgroups and interactions 45 3.4.5 Bayesian approaches 46 3.5 Conclusion 46 3.6 Appendix: The effect of covariate adjustment on the variance multiplier in least squares 47 References 48 4 Modelling with deterministic computer models 51 Jeremy E. Oakley 4.1 Introduction 51 4.2 Metamodels and emulators for computationally expensive simulators 52 4.2.1 Gaussian processes emulators 53 4.2.2 Multivariate outputs 56 4.3 Uncertainty analysis 57 4.4 Sensitivity analysis 58 4.4.1 Variance-based sensitivity analysis 58 4.4.2 Value of information 61 4.5 Calibration and discrepancy 63 4.6 Discussion 64 References 65 5 Modelling future climates 69 Peter Challenor and Robin Tokmakian 5.1 Introduction 69 5.2 What is the risk from climate change? 70 5.3 Climate models 70 5.4 An anatomy of uncertainty 72 5.4.1 Aleatoric uncertainty 72 5.4.2 Epistemic uncertainty 73 5.5 Simplicity and complexity 75 5.6 An example: The collapse of the thermohaline circulation 77 5.7 Conclusions 79 References 79 6 Modelling climate change impacts for adaptation assessments 83 Suraje Dessai and Jeroen van der Sluijs 6.1 Introduction 83 6.1.1 Climate impact assessment 84 6.2 Modelling climate change impacts: From world development paths to localized impacts 87 6.2.1 Greenhouse gas emissions 87 6.2.2 Climate models 90 6.2.3 Downscaling 93 6.2.4 Regional/local climate change impacts 94 6.3 Discussion 95 6.3.1 Multiple routes of uncertainty assessment 96 6.3.2 What is the appropriate balance between simplicity and complexity? 96 References 98 7 Modelling in water distribution systems 103 Zoran Kapelan 7.1 Introduction 103 7.2 Water distribution system models 104 7.2.1 Water distribution systems 104 7.2.2 WDS hydraulic models 104 7.2.3 Uncertainty in WDS hydraulic modelling 107 7.3 Calibration of WDS hydraulic models 108 7.3.1 Calibration problem 108 7.3.2 Existing approaches 109 7.3.3 Case study 113 7.4 Sampling design for calibration 116 7.4.1 Sampling design problem 116 7.4.2 Existing approaches 116 7.4.3 Case study 120 7.5 Summary and conclusions 120 References 122 8 Modelling for flood risk management 125 Jim Hall 8.1 Introduction 125 8.2 Flood risk management 126 8.2.1 Long-term change 130 8.2.2 Uncertainty 131 8.3 Multi-purpose management 131 8.4 Modelling for flood risk management 132 8.4.1 Source 132 8.4.2 Pathway 132 8.4.3 Receptors 135 8.4.4 An example of a system model: Towyn 135 8.5 Model choice 137 8.6 Conclusions 143 References 144 9 Uncertainty quantification and oil reservoir modelling 147 Mike Christie 9.1 Introduction 147 9.2 Bayesian framework 148 9.2.1 Solution errors 149 9.3 Quantifying uncertainty in prediction of oil recovery 150 9.3.1 Stochastic sampling algorithms 151 9.3.2 Computing uncertainties from multiple history matched models 153 9.4 Inverse problems and reservoir model history matching 155 9.4.1 Synthetic problems 155 9.4.2 Imperial college fault model 157 9.4.3 Comparison of algorithms on a real field example 158 9.5 Selecting appropriate detail in models 162 9.5.1 Adaptive multiscale estimation 162 9.5.2 Bayes factors 165 9.5.3 Application of solution error modelling 167 9.6 Summary 170 References 171 10 Modelling in radioactive waste disposal 173 Andrew Cliffe 10.1 Introduction 173 10.2 The radioactive waste problem 174 10.2.1 What is radioactive waste? 174 10.2.2 How much radioactive waste is there? 175 10.2.3 What are the options for long-term management of radioactive waste? 175 10.3 The treatment of uncertainty in radioactive waste disposal 177 10.3.1 Deep geological disposal 177 10.3.2 Repository performance assessment 177 10.3.3 Modelling 179 10.3.4 Model verification and validation 180 10.3.5 Strategies for dealing with uncertainty 182 10.4 Summary and conclusions 184 References 184 11 Issues for modellers 187 Mike Christie, Andrew Cliffe, Philip Dawid and Stephen Senn 11.1 What are models and what are they useful for? 187 11.2 Appropriate levels of complexity 189 11.3 Uncertainty 190 11.3.1 Model inputs and parameter uncertainty 190 11.3.2 Model uncertainty 191 References 192 Glossary 193 Index 201.
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Several points of disagreement exist between different modelling traditions as to whether complex models are always better than simpler models, as to how to combine results from different models and how to propagate model uncertainty into forecasts. This book represents the result of collaboration between scientists from many disciplines to show how these conflicts can be resolved. Key Features: * Introduces important concepts in modelling, outlining different traditions in the use of simple and complex modelling in statistics. * Provides numerous case studies on complex modelling, such as climate change, flood risk and new drug development. * Concentrates on varying models, including flood risk analysis models, the petrol industry forecasts and summarizes the evolution of water distribution systems. * Written by experienced statisticians and engineers in order to facilitate communication between modellers in different disciplines. * Provides a glossary giving terms commonly used in different modelling traditions. This book provides a much-needed reference guide to approaching statistical modelling. Scientists involved with modelling complex systems in areas such as climate change, flood prediction and prevention, financial market modelling and systems engineering will benefit from this book. It will also be a useful source of modelling case histories.
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Includes issues related to simulation, such as: Web-based simulation, virtual reality, augmented reality, and artificial intelligence.
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This book constitutes the thoroughly refereed post-proceedings of the 11th International Conference on Computer Aided Systems Theory, EUROCAST 2007. Coverage in the 144 revised full papers presented includes formal approaches, computation and simulation in modeling biological systems, intelligent information processing, heuristic problem solving, signal processing architectures, robotics and robotic soccer, cybercars and intelligent vehicles and artificial intelligence components.
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• Keynotes.- Modeling Workflow Patterns from First Principles.- Heart Modeling, Computational Physiology and the IUPS Physiome Project.- Conceptual Schemas and Ontologies for Database Access: Myths and Challenges.- Data Warehousing and Data Mining.- Multidimensional Data Modeling for Business Process Analysis.- Mining Hesitation Information by Vague Association Rules.- A Model Driven Modernization Approach for Automatically Deriving Multidimensional Models in Data Warehouses.- Design Methodologies and Tools.- Cost-Based Fragmentation for Distributed Complex Value Databases.- From Business Models to Service-Oriented Design: A Reference Catalog Approach.- Teaching a Schema Translator to Produce O/R Views.- Building a Tool for Cost-Based Design of Object-Oriented Database Schemas.- Information and Database Integration.- Generic Schema Mappings.- Relational Data Tailoring Through View Composition.- On the Discovery of Preferred Work Practice Through Business Process Variants.- Information Modelling Concepts and Ontologies.- Towards Automated Reasoning on ORM Schemes.- From Declarative to Imperative UML/OCL Operation Specifications.- An Ontological Metamodel of Classifiers and Its Application to Conceptual Modelling and Database Design.- Integrity Constraints.- Handling Inconsistency of Vague Relations with Functional Dependencies.- Querying Incomplete Data with Logic Programs: ER Strikes Back.- Prioritized Preferences and Choice Constraints.- Logical Foundations of Conceptual Modelling.- Reasoning over Extended ER Models.- On Order Dependencies for the Semantic Web.- Collection Type Constructors in Entity-Relationship Modeling.- Patterns and Conceptual Meta-modelling.- Schema Exchange: A Template-Based Approach to Data and Metadata Translation.- A Conceptual Modeling Methodology Based on Niches and Granularity.- As We May Link: A General Metamodel for Hypermedia Systems.- Requirements Elicitation.- A Goal Oriented Approach for Modeling and Analyzing Security Trade-Offs.- Rapid Business Process Discovery (R-BPD).- Ontology-Driven Business Modelling: Improving the Conceptual Representation of the REA Ontology.- A Comparison of Two Approaches to Safety Analysis Based on Use Cases.- Using Unified Modeling Language for Conceptual Modelling of Knowledge-Based Systems.- Tracing the Rationale Behind UML Model Change Through Argumentation.- Reuse and Reengineering.- Exploring Alternatives for Representing and Accessing Design Knowledge About Enterprise Integration.- Mining and Re-engineering Transactional Workflows for Reliable Executions.- Cross: An OWL Wrapper for Reasoning on Relational Databases.- Semi-structured Data and XML.- Augmenting Traditional Conceptual Models to Accommodate XML Structural Constructs.- VERT: A Semantic Approach for Content Search and Content Extraction in XML Query Processing.- A Conceptual Model for Multidimensional Analysis of Documents.- Web Information Systems and XML.- Automatic Hidden-Web Table Interpretation by Sibling Page Comparison.- A Fine-Grained XML Structural Comparison Approach.- Fine-Grained Compatibility and Replaceability Analysis of Timed Web Service Protocols.
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This book constitutes the refereed proceedings of the 26th International Conference on Conceptual Modeling, ER 2007. Coverage in the papers includes data warehousing and data mining, design methodologies and tools, information and database integration, information modeling concepts and ontologies, integrity constraints, logical foundations of conceptual modeling, patterns and conceptual meta-modeling, semi-structured data and XML, as well as Web information systems and XML.
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• 1: Introduction
• 2: Simulation Examples
• 3: General Principles
• 4: Simulation Software
• 5: Statistical Models in Simulation
• 6: Queueing Models
• 7: Random-number Generation
• 8: Random-variate Generation
• 9: Input Modeling
• 10: V&V
• 11: Output Analysis for a Single Model
• 12: Comparing Multiple Designs
• 13: Simulation of Mfg. & MH Systems
• 14: Simulation of Computer Systems
• 15: Simulation of Communication Systems.
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For Junior & Senior level simulation courses in engineering, business, or computer science. This text provides a basic treatment of discrete-event simulation, including the proper collection and analysis of data, the use of analytic techniques, verification and validation of models, and designing simulation experiments. It offers an up-to-date treatment of simulation of manufacturing and material handling systems, computer systems, and computer networks. Students and instructors will find a variety of resources at the associated website, www.bcnn.net, including simulation source code for download, additional exercises and solutions, web links and errata.
(source: Nielsen Book Data)

• 1: Introduction to Applied System Simulation
• 2: Fundamentals of System Simulation
• 3: Simulation of Computer System Architectures
• 4: Simulation of Parallel and Distributed Systems Scheduling
• 5: Modeling and Simulation of ATM Systems and Networks
• 6: Simulation of Wireless Networks
• 7: Satellite System Simulation
• 8: Simulation in Web Data Management
• 9: Modeling and Simulation of Semiconductor Transceivers
• 10: Agent-Oriented Simulation
• 11: A Distributed Intelligent Discrete-Event Environment for Autonomous Agents Simulation
• 12: Simulation in the Health Services and Biomedicine
• 13: Simulation in Environmental and Ecological Systems
• 14: Simulation in City Planning and Engineering
• 15: Simulation of Manufacturing Systems
• 16: Aerospace Vehicle and Air Traffic Simulation
• 17: Simulation in Business Administration and Management
• 18: Military Applications of Simulation
• 19: Simulation in Education and Training
• 20: Parallel and Distributed Simulation
• 21: Verification, Validation, and Accreditation of Simulation Models.

This book provides state-of-the-art treatment of methodologies and applications of system simulation related to all disciplines that range from toll-plaza systems for highways and bridges, traffic light control in city planning and engineering, design of transistors, VLSI chips, and computer systems and networks to the design and optimization of the operation of aerodynamic, air traffic control, and space shuttle systems. In a series of structured and focused chapters written by leading international experts from academia, industry and government, this volume presents the fundamentals of system simulation along with the latest research results and applications.

• (NOTE: Each chapter concludes with Summary, References, and Exercises.) I. INTRODUCTION TO DISCRETE-EVENT SYSTEM SIMULATION.
• 1. Introduction to Simulation. When Simulation Is the Appropriate Tool. When Simulation Is Not Appropriate. Advantages and Disadvantages of Simulation. Areas of Application. Systems and System Environment. Components of a System. Discrete and Continuous Systems. Model of a System. Types of Models. Discrete-Event System Simulation. Steps in a Simulation Study.
• 2. Simulation Examples. Simulation of Queueing Systems. Simulation of Inventory Systems. Other Examples of Simulation.
• 3. General Principles. Concepts in Discrete-Event Simulation. List Processing.
• 4. Simulation Software. History of Simulation Software. Selection of Simulation Software. An Example Simulation. Simulation in C++. Simulation in GPSS. Simulation in CSIM. Simulation Packages. Experimentation and Statistical Analysis Tools. Trends in Simulation Software. II. MATHEMATICAL AND STATISTICAL MODELS.
• 5. Statistical Models in Simulation. Review of Terminology and Concepts. Useful Statistical Models. Discrete Distributions. Continuous Distributions. Poisson Process. Empirical Distributions.
• 6. Queueing Models. Characteristics of Queueing Systems. Queueing Notation. Long-Run Measures of Performance of Queueing Systems. Steady-State Behavior of Infinite-Population Markovian Models. Steady-State Behavior of Finite-Population Models. Networks of Queues. III. RANDOM NUMBERS.
• 7. Random-Number Generation. Properties of Random Numbers. Generation of Pseudo-Random Numbers. Techniques for Generating Random Numbers. Tests for Random Numbers.
• 8. Random-Variate Generation. Inverse Transform Technique. Direct Transformation for the Normal and Lognormal Distributions. Convolution Method. Acceptance-Rejection Technique. IV. ANALYSIS OF SIMULATION DATA.
• 9. Input Modeling. Data Collection. Identifying the Distribution with Data. Parameter Estimation. Goodness-of-Fit Tests. Selecting Input Models without Data. Multivariate and Time-Series Input Models.
• 10. Verification and Validation of Simulation Models. Model Building, Verification, and Validation. Verification of Simulation Models. Calibration and Validation of Models.
• 11. Output Analysis for a Single Model. Types of Simulations with Respect to Output Analysis. Stochastic Nature of Output Data. Measures of Performance and Their Estimation. Output Analysis for Terminating Simulations. Output Analysis for Steady-State Simulations.
• 12. Comparison and Evaluation of Alternative System Designs. Comparison of Two System Designs. Comparison of Several System Designs. Metamodeling. Optimization via Simulation.
• 13. Simulation of Manufacturing and Material Handling Systems. Manufacturing and Material Handling Simulations. Goals and Performance Measures. Issues in Manufacturing and Material Handling Simulations. Case Studies of the Simulation of Manufacturing and Material Handling Systems.
• 14. Simulation of Computer Systems. Introduction. Simulation Tools. Model Input. High-Level Computer-System Simulation. CPU Simulation. Memory Simulation. Appendix Tables. Random Digits. Random Normal Numbers. Cumulative Normal Distribution. Cumulative Poisson Distribution. Percentage Points of the Students t Distribution with v Degrees of Freedom. Percentage Points of the Chi-Square Distribution with v Degrees of Freedom. Percentage Points of the F Distribution with ...a = 0
• .05. Kolmogorov-Smirnov Critical Values. Maximum-Likelihood Estimates of the Gamma Distribution. Operating-Characteristic Curves for the Two-Sided t-Test for Different Values of Sample Size n. Operating-Characteristic Curves for the One-Sided t-Test for Different Values of Sample Size n. Index.
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Appropriate for junior- senior-level Simulation courses in departments of Engineering, Management and Computer Science; or a second course in Simulation. This text provides a basic treatment of discrete-event simulation, one of the most widely used operations research and management science tools for dealing with system design in the presence of uncertainty. Proper collection and analysis of data, use of analytic techniques, verification and validation of models and the appropriate design of simulation experiments are treated extensively. Readily understandable to those having a basic familiarity with differential and integral calculus, probability theory and elementary statistics. The Third Edition reorganizes, updates and expands coverage to reflect the most recent developments in software and methodology, and adds a chapter on the simulation of computer systems.
(source: Nielsen Book Data)