Simulation Modeling and Analysis

Size: px
Start display at page:

Download "Simulation Modeling and Analysis"

Transcription

1 Simulation Modeling and Analysis FOURTH EDITION Averill M. Law President Averill M. Law & Associates, Inc. Tucson, Arizona, USA www. averill-law. com Boston Burr Ridge, IL Dubuque, IA New York San Francisco St. Louis Bangkok Bogota Caracas Kuala Lumpur Lisbon London Madrid Mexico City Milan Montreal New Delhi Santiago Seoul Singapore Sydney Taipei Toronto

2 CONTENTS List of Symbols Preface xv xvii Chapter 1 Basic Simulation Modeling The Nature of Simulation Systems, Models, and Simulation Discrete-Event Simulation Time-Advance Mechanisms Components and Organization of a Discrete-Event Simulation Model Simulation of a Single-Server Queueing System Problem Statement Intuitive Explanation Program Organization and Logic C Program Simulation Output and Discussion Alternative Stopping Rules Determining the Events and Variables Simulation of an Inventory System Problem Statement Program Organization and Logic C Program Simulation Output and Discussion Parallel/Distributed Simulation and the High Level Architecture Parallel Simulation Distributed Simulation and the High Level Architecture Steps in a Sound Simulation Study Other Types of Simulation Continuous Simulation Combined Discrete-Continuous Simulation Monte Carlo Simulation Spreadsheet Simulation 74

3 Vlll CONTENTS 1.9 Advantages, Disadvantages, and Pitfalls of Simulation 76 Appendix 1 A: Fixed-Increment Time Advance 78 Appendix IB: A Primer on Queueing Systems 79 1B.1 Components of a Queueing System 80 1B.2 Notation for Queueing Systems 80 IB.3 Measures of Performance for Queueing Systems Chapter 2 Modeling Complex Systems Introduction List Processing in Simulation Approaches to Storing Lists in a Computer Linked Storage Allocation A Simple Simulation Language: simlib Single-Server Queueing Simulation with simlib Problem Statement simlib Program Simulation Output and Discussion Time-Shared Computer Model Problem Statement simlib Program Simulation Output and Discussion Multiteller Bank With Jockeying Problem Statement simlib Program Simulation Output and Discussion Job-Shop Model Problem Statement simlib Program Simulation Output and Discussion Efficient Event-List Manipulation 155 Appendix 2A: C Code for simlib Chapter 3 Simulation Software Introduction Comparison of Simulation Packages with Programming Languages Classification of Simulation Software General-Purpose vs. Application-Oriented Simulation Packages 189

4 CONTENTS IX Modeling Approaches Common Modeling Elements 3.4 Desirable Software Features General Capabilities Hardware and Software Requirements Animation and Dynamic Graphics Statistical Capabilities Customer Support and Documentation Output Reports and Graphics 3.5 General-Purpose Simulation Packages Arena Extend Other General-Purpose Simulation Packages 3.6 Object-Oriented Simulation 3.7 Examples of Application-Oriented Simulation Packages j)ter 4 Review of Basic Probability and Statistics 4.1 Introduction 4.2 Random Variables and Their Properties 4.3 Simulation Output Data and Stochastic Processes 4.4 Estimation of Means, Variances, and Correlations 4.5 Confidence Intervals and Hypothesis Tests for the Mean 4.6 The Strong Law of Large Numbers 4.7 The Danger of Replacing a Probability Distribution by its Mean Appendix 4A: Comments on Covariance-Stationary Processes er 5 Building Valid, Credible, and Appropriately Detailed Simulation Models 5.1 Introduction and Definitions 5.2 Guidelines for Determining the Level of Model Detail 5.3 Verification of Simulation Computer Programs 5.4 Techniques for Increasing Model Validity and Credibility Collect High-Quality Information and Data on the System Interact with the Manager on a Regular Basis Maintain a Written Assumptions Document and Perform a Structured Walk-Through Validate Components of the Model by Using Quantitative Techniques

5 X CONTENTS Validate the Output from the Overall Simulation Model Animation 5.5 Management's Role in the Simulation Process 5.6 Statistical Procedures for Comparing Real-World Observations and Simulation Output Data Inspection Approach Confidence-Interval Approach Based on Independent Data Time-Series Approaches Other Approaches Chapter 6 Selecting Input Probability Distributions 6.1 Introduction 6.2 Useful Probability Distributions Parameterization of Continuous Distributions Continuous Distributions Discrete Distributions Empirical Distributions 6.3 Techniques for Assessing Sample Independence 6.4 Activity I: Hypothesizing Families of Distributions Summary Statistics Histograms Quantile Summaries and Box Plots 6.5 Activity II: Estimation of Parameters 6.6 Activity III: Determining How Representative the Fitted Distributions Are Heuristic Procedures Goodness-of-Fit Tests 6.7 The ExpertFit Software and an Extended Example 6.8 Shifted and Truncated Distributions 6.9 Bezier Distributions 6.10 Specifying Multivariate Distributions, Correlations, and Stochastic Processes Specifying Multivariate Distributions Specifying Arbitrary Marginal Distributions and Correlations Specifying Stochastic Processes 6.11 Selecting a Distribution in the Absence of Data 6.12 Models of Arrival Processes Poisson Processes Nonstationary Poisson Processes Batch Arrivals

6 CONTENTS XI 6.13 Assessing the Homogeneity of Different Data Sets Appendix 6A: Tables of MLEs for the Gamma and Beta Distributions Chapter 7 Random-Number Generators 7.1 Introduction 7.2 Linear Congruential Generators Mixed Generators Multiplicative Generators 7.3 Other Kinds of Generators More General Congruences Composite Generators Feedback Shift Register Generators 7.4 Testing Random-Number Generators Empirical Tests Theoretical Tests Some General Observations on Testing Appendix 7A: Appendix 7B: Portable C Code for a PMMLCG Portable C Code for a Combined MRG Chapter 8 Generating Random Variates 8.1 Introduction 8.2 General Approaches to Generating Random Variates Inverse Transform Composition Convolution Acceptance-Rejection Ratio of Uniforms Special Properties Generating Continuous Rando Uniform Exponential m-erlang Gamma Weibull Normal Lognormal Beta Pearson Type V Pearson Type VI Log-Logistic

7 Xll CONTENTS Johnson Bounded Johnson Unbounded Bezier Triangular Empirical Distributions Generating Discrete Random Variates Bernoulli Discrete Uniform Arbitrary Discrete Distribution Binomial Geometric Negative Binomial Poisson Generating Random Vectors, Correlated Random Variates, and Stochastic Processes Using Conditional Distributions Multivariate Normal and Multivariate Lognormal Correlated Gamma Random Variates Generating from Multivariate Families Generating Random Vectors with Arbitrarily Specified Marginal Distributions and Correlations Generating Stochastic Processes Generating Arrival Processes Poisson Processes Nonstationary Poisson Processes Batch Arrivals 477 Appendix 8A: Validity of the Acceptance-Rejection Method 477 Appendix 8B: Setup for the Alias Method Chapter 9 Output Data Analysis for a Single System Introduction Transient and Steady-State Behavior of a Stochastic Process Types of Simulations with Regard to Output Analysis Statistical Analysis for Terminating Simulations Estimating Means Estimating Other Measures of Performance Choosing Initial Conditions Statistical Analysis for Steady-State Parameters The Problem of the Initial Transient Replication/Deletion Approach for Means Other Approaches for Means Estimating Other Measures of Performance 533

Simulation Input Data Modeling

Simulation Input Data Modeling Introduction to Modeling and Simulation Simulation Input Data Modeling OSMAN BALCI Professor Department of Computer Science Virginia Polytechnic Institute and State University (Virginia Tech) Blacksburg,

More information

Simulation with Arena

Simulation with Arena Simulation with Arena Sixth Edition W. David Kelton Professor Department of Operations, Business Analytics, and Information Systems University of Cincinnati Randall P. Sadowski Retired Nancy B. Zupick

More information

Random Number Generation and Monte Carlo Methods

Random Number Generation and Monte Carlo Methods James E. Gentle Random Number Generation and Monte Carlo Methods With 30 Illustrations Springer Contents Preface vii 1 Simulating Random Numbers from a Uniform Distribution 1 1.1 Linear Congruential Generators

More information

Lecture: Simulation. of Manufacturing Systems. Sivakumar AI. Simulation. SMA6304 M2 ---Factory Planning and scheduling. Simulation - A Predictive Tool

Lecture: Simulation. of Manufacturing Systems. Sivakumar AI. Simulation. SMA6304 M2 ---Factory Planning and scheduling. Simulation - A Predictive Tool SMA6304 M2 ---Factory Planning and scheduling Lecture Discrete Event of Manufacturing Systems Simulation Sivakumar AI Lecture: 12 copyright 2002 Sivakumar 1 Simulation Simulation - A Predictive Tool Next

More information

BESTFIT, DISTRIBUTION FITTING SOFTWARE BY PALISADE CORPORATION

BESTFIT, DISTRIBUTION FITTING SOFTWARE BY PALISADE CORPORATION Proceedings of the 1996 Winter Simulation Conference ed. J. M. Charnes, D. J. Morrice, D. T. Brunner, and J. J. S\vain BESTFIT, DISTRIBUTION FITTING SOFTWARE BY PALISADE CORPORATION Linda lankauskas Sam

More information

I Communication Networks A First Course

I Communication Networks A First Course n =Q I Communication Networks A First Course Jean Walrand University of California at Berkeley Me Grain/ Hill WCB McGraw-Hill ULB Darmstadt iflllillll 16643424 Boston Burr Ridge, IL Dubuque, IA Madison,

More information

VHDL. Douglas L. Perry. Third Edition

VHDL. Douglas L. Perry. Third Edition VHDL Douglas L. Perry Third Edition McGraw-Hill New York San Francisco Washington, D.C. Auckland Bogota Caracas Lisbon London Madrid Mexico City Milan Montreal New Delhi San Juan Singapore Sydney Tokyo

More information

DATABASE SYSTEM CONCEPTS

DATABASE SYSTEM CONCEPTS DATABASE SYSTEM CONCEPTS HENRY F. KORTH ABRAHAM SILBERSCHATZ University of Texas at Austin McGraw-Hill, Inc. New York St. Louis San Francisco Auckland Bogota Caracas Lisbon London Madrid Mexico Milan Montreal

More information

Fuzzy Set Theory and Its Applications. Second, Revised Edition. H.-J. Zimmermann. Kluwer Academic Publishers Boston / Dordrecht/ London

Fuzzy Set Theory and Its Applications. Second, Revised Edition. H.-J. Zimmermann. Kluwer Academic Publishers Boston / Dordrecht/ London Fuzzy Set Theory and Its Applications Second, Revised Edition H.-J. Zimmermann KM ff Kluwer Academic Publishers Boston / Dordrecht/ London Contents List of Figures List of Tables Foreword Preface Preface

More information

Fast Automated Estimation of Variance in Discrete Quantitative Stochastic Simulation

Fast Automated Estimation of Variance in Discrete Quantitative Stochastic Simulation Fast Automated Estimation of Variance in Discrete Quantitative Stochastic Simulation November 2010 Nelson Shaw njd50@uclive.ac.nz Department of Computer Science and Software Engineering University of Canterbury,

More information

Programming. In Ada JOHN BARNES TT ADDISON-WESLEY

Programming. In Ada JOHN BARNES TT ADDISON-WESLEY Programming In Ada 2005 JOHN BARNES... TT ADDISON-WESLEY An imprint of Pearson Education Harlow, England London New York Boston San Francisco Toronto Sydney Tokyo Singapore Hong Kong Seoul Taipei New Delhi

More information

MECHATRONICS. William Bolton. Sixth Edition ELECTRONIC CONTROL SYSTEMS ENGINEERING IN MECHANICAL AND ELECTRICAL PEARSON

MECHATRONICS. William Bolton. Sixth Edition ELECTRONIC CONTROL SYSTEMS ENGINEERING IN MECHANICAL AND ELECTRICAL PEARSON MECHATRONICS ELECTRONIC CONTROL SYSTEMS IN MECHANICAL AND ELECTRICAL ENGINEERING Sixth Edition William Bolton PEARSON Harlow, England London New York Boston San Francisco Toronto Sydney Auckland Singapore

More information

LabVIEW Graphical Programming

LabVIEW Graphical Programming LabVIEW Graphical Programming Fourth Edition Gary W. Johnson Richard Jennings McGraw-Hill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney

More information

The Unified Modeling Language User Guide

The Unified Modeling Language User Guide The Unified Modeling Language User Guide Grady Booch James Rumbaugh Ivar Jacobson Rational Software Corporation TT ADDISON-WESLEY Boston San Francisco New York Toronto Montreal London Munich Paris Madrid

More information

StatsMate. User Guide

StatsMate. User Guide StatsMate User Guide Overview StatsMate is an easy-to-use powerful statistical calculator. It has been featured by Apple on Apps For Learning Math in the App Stores around the world. StatsMate comes with

More information

THE AVR MICROCONTROLLER AND EMBEDDED SYSTEMS. Using Assembly and С

THE AVR MICROCONTROLLER AND EMBEDDED SYSTEMS. Using Assembly and С THE AVR MICROCONTROLLER AND EMBEDDED SYSTEMS Using Assembly and С Muhammad AH Mazidi Sarmad Naimi Sepehr Naimi Prentice Hall Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam

More information

PROBLEM SOLVING USING JAVA WITH DATA STRUCTURES. A Multimedia Approach. Mark Guzdial and Barbara Ericson PEARSON. College of Computing

PROBLEM SOLVING USING JAVA WITH DATA STRUCTURES. A Multimedia Approach. Mark Guzdial and Barbara Ericson PEARSON. College of Computing PROBLEM SOLVING WITH DATA STRUCTURES USING JAVA A Multimedia Approach Mark Guzdial and Barbara Ericson College of Computing Georgia Institute of Technology PEARSON Boston Columbus Indianapolis New York

More information

What We ll Do... Random

What We ll Do... Random What We ll Do... Random- number generation Random Number Generation Generating random variates Nonstationary Poisson processes Variance reduction Sequential sampling Designing and executing simulation

More information

Modelling and Quantitative Methods in Fisheries

Modelling and Quantitative Methods in Fisheries SUB Hamburg A/553843 Modelling and Quantitative Methods in Fisheries Second Edition Malcolm Haddon ( r oc) CRC Press \ y* J Taylor & Francis Croup Boca Raton London New York CRC Press is an imprint of

More information

World Wide Web PROGRAMMING THE PEARSON EIGHTH EDITION. University of Colorado at Colorado Springs

World Wide Web PROGRAMMING THE PEARSON EIGHTH EDITION. University of Colorado at Colorado Springs PROGRAMMING THE World Wide Web EIGHTH EDITION ROBERT W. SEBESTA University of Colorado at Colorado Springs PEARSON Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam Cape

More information

Database Concepts. David M. Kroenke UNIVERSITATSBIBLIOTHEK HANNOVER

Database Concepts. David M. Kroenke UNIVERSITATSBIBLIOTHEK HANNOVER Database Concepts Fifth Edition David M. Kroenke David J. Auer ^111 I ii i.111 111 n.n jiiim^ TECHNISCHE INFORMATIOMSBiBLIOTHEK UNIVERSITATSBIBLIOTHEK HANNOVER j TIB/UB Hannover Prentice Hall Boston Columbus

More information

and Networks Data Communications Second Edition Tata McGraw Hill Education Private Limited Managing Director SoftExcel Services Limited, Mumbai

and Networks Data Communications Second Edition Tata McGraw Hill Education Private Limited Managing Director SoftExcel Services Limited, Mumbai Data Communications and Networks Second Edition ACHYUT S GODBOLE Managing Director SoftExcel Services Limited, Mumbai ATUL KAHATE Senior Consultant Oracle Financial Services Software Limited, Pune Tata

More information

Real-Time Systems and Programming Languages

Real-Time Systems and Programming Languages Real-Time Systems and Programming Languages Ada, Real-Time Java and C/Real-Time POSIX Fourth Edition Alan Burns and Andy Wellings University of York * ADDISON-WESLEY An imprint of Pearson Education Harlow,

More information

Essentials of Database Management

Essentials of Database Management Essentials of Database Management Jeffrey A. Hoffer University of Dayton Heikki Topi Bentley University V. Ramesh Indiana University PEARSON Boston Columbus Indianapolis New York San Francisco Upper Saddle

More information

Anany Levitin 3RD EDITION. Arup Kumar Bhattacharjee. mmmmm Analysis of Algorithms. Soumen Mukherjee. Introduction to TllG DCSISFI &

Anany Levitin 3RD EDITION. Arup Kumar Bhattacharjee. mmmmm Analysis of Algorithms. Soumen Mukherjee. Introduction to TllG DCSISFI & Introduction to TllG DCSISFI & mmmmm Analysis of Algorithms 3RD EDITION Anany Levitin Villa nova University International Edition contributions by Soumen Mukherjee RCC Institute of Information Technology

More information

Complete. The. Reference. Christopher Adamson. Mc Grauu. LlLIJBB. New York Chicago. San Francisco Lisbon London Madrid Mexico City

Complete. The. Reference. Christopher Adamson. Mc Grauu. LlLIJBB. New York Chicago. San Francisco Lisbon London Madrid Mexico City The Complete Reference Christopher Adamson Mc Grauu LlLIJBB New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto Contents Acknowledgments

More information

Student Version 8 AVERILL M. LAW & ASSOCIATES

Student Version 8 AVERILL M. LAW & ASSOCIATES ExpertFit Student Version 8 AVERILL M. LAW & ASSOCIATES 4729 East Sunrise Drive, # 462 Tucson, AZ 85718 Phone: 520-795-6265 E-mail: averill@simulation.ws Website: www.averill-law.com 1. Introduction ExpertFit

More information

Design of Experiments

Design of Experiments Seite 1 von 1 Design of Experiments Module Overview In this module, you learn how to create design matrices, screen factors, and perform regression analysis and Monte Carlo simulation using Mathcad. Objectives

More information

Computer Systems Performance Analysis and Benchmarking (37-235)

Computer Systems Performance Analysis and Benchmarking (37-235) Computer Systems Performance Analysis and Benchmarking (37-235) Analytic Modeling Simulation Measurements / Benchmarking Lecture by: Prof. Thomas Stricker Assignments/Projects: Christian Kurmann Textbook:

More information

rtrng: Advanced Parallel Random Number Generation in R

rtrng: Advanced Parallel Random Number Generation in R rtrng: Advanced Parallel Random Number Generation in R Riccardo Porreca Mirai Solutions GmbH Tödistrasse 48 CH-8002 Zurich Switzerland user!2017 Brussels info@mirai-solutions.com www.mirai-solutions.com

More information

CLASSIC DATA STRUCTURES IN JAVA

CLASSIC DATA STRUCTURES IN JAVA CLASSIC DATA STRUCTURES IN JAVA Timothy Budd Oregon State University Boston San Francisco New York London Toronto Sydney Tokyo Singapore Madrid Mexico City Munich Paris Cape Town Hong Kong Montreal CONTENTS

More information

Introductory logic and sets for Computer scientists

Introductory logic and sets for Computer scientists Introductory logic and sets for Computer scientists Nimal Nissanke University of Reading ADDISON WESLEY LONGMAN Harlow, England II Reading, Massachusetts Menlo Park, California New York Don Mills, Ontario

More information

ony Gaddis Haywood Community College STARTING OUT WITH PEARSON Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto

ony Gaddis Haywood Community College STARTING OUT WITH PEARSON Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto STARTING OUT WITH J^"* 1 Ti * ony Gaddis Haywood Community College PEARSON Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam Cape Town Dubai London Madrid Milan Munich Paris

More information

Business Driven Data Communications

Business Driven Data Communications Business Driven Data Communications Michael S. Gendron PEARSON Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal

More information

Stochastic Simulation: Algorithms and Analysis

Stochastic Simulation: Algorithms and Analysis Soren Asmussen Peter W. Glynn Stochastic Simulation: Algorithms and Analysis et Springer Contents Preface Notation v xii I What This Book Is About 1 1 An Illustrative Example: The Single-Server Queue 1

More information

Markov Chain Monte Carlo (part 1)

Markov Chain Monte Carlo (part 1) Markov Chain Monte Carlo (part 1) Edps 590BAY Carolyn J. Anderson Department of Educational Psychology c Board of Trustees, University of Illinois Spring 2018 Depending on the book that you select for

More information

ISO INTERNATIONAL STANDARD. Random variate generation methods. Méthodes de génération de nombres pseudo-aléatoires. First edition

ISO INTERNATIONAL STANDARD. Random variate generation methods. Méthodes de génération de nombres pseudo-aléatoires. First edition INTERNATIONAL STANDARD ISO 28640 First edition 2010-03-15 Random variate generation methods Méthodes de génération de nombres pseudo-aléatoires Reference number ISO 28640:2010(E) ISO 2010 PDF disclaimer

More information

Visual C# Tony Gaddis. Haywood Community College STARTING OUT WITH. Piyali Sengupta. Third Edition. Global Edition contributions by.

Visual C# Tony Gaddis. Haywood Community College STARTING OUT WITH. Piyali Sengupta. Third Edition. Global Edition contributions by. STARTING OUT WITH Visual C# 2012 Third Edition Global Edition Tony Gaddis Haywood Community College Global Edition contributions by Piyali Sengupta PEARSON Boston Columbus Indianapolis New York San Francisco

More information

Acknowledgments. Acronyms

Acknowledgments. Acronyms Acknowledgments Preface Acronyms xi xiii xv 1 Basic Tools 1 1.1 Goals of inference 1 1.1.1 Population or process? 1 1.1.2 Probability samples 2 1.1.3 Sampling weights 3 1.1.4 Design effects. 5 1.2 An introduction

More information

QstatLab: software for statistical process control and robust engineering

QstatLab: software for statistical process control and robust engineering QstatLab: software for statistical process control and robust engineering I.N.Vuchkov Iniversity of Chemical Technology and Metallurgy 1756 Sofia, Bulgaria qstat@dir.bg Abstract A software for quality

More information

STATISTICS (STAT) Statistics (STAT) 1

STATISTICS (STAT) Statistics (STAT) 1 Statistics (STAT) 1 STATISTICS (STAT) STAT 2013 Elementary Statistics (A) Prerequisites: MATH 1483 or MATH 1513, each with a grade of "C" or better; or an acceptable placement score (see placement.okstate.edu).

More information

Ludwig Fahrmeir Gerhard Tute. Statistical odelling Based on Generalized Linear Model. íecond Edition. . Springer

Ludwig Fahrmeir Gerhard Tute. Statistical odelling Based on Generalized Linear Model. íecond Edition. . Springer Ludwig Fahrmeir Gerhard Tute Statistical odelling Based on Generalized Linear Model íecond Edition. Springer Preface to the Second Edition Preface to the First Edition List of Examples List of Figures

More information

Systems:;-'./'--'.; r. Ramez Elmasri Department of Computer Science and Engineering The University of Texas at Arlington

Systems:;-'./'--'.; r. Ramez Elmasri Department of Computer Science and Engineering The University of Texas at Arlington Data base 7\,T"] Systems:;-'./'--'.; r Modelsj Languages, Design, and Application Programming Ramez Elmasri Department of Computer Science and Engineering The University of Texas at Arlington Shamkant

More information

Fathom Dynamic Data TM Version 2 Specifications

Fathom Dynamic Data TM Version 2 Specifications Data Sources Fathom Dynamic Data TM Version 2 Specifications Use data from one of the many sample documents that come with Fathom. Enter your own data by typing into a case table. Paste data from other

More information

COMPUTER AND ROBOT VISION

COMPUTER AND ROBOT VISION VOLUME COMPUTER AND ROBOT VISION Robert M. Haralick University of Washington Linda G. Shapiro University of Washington A^ ADDISON-WESLEY PUBLISHING COMPANY Reading, Massachusetts Menlo Park, California

More information

MACHINES AND MECHANISMS

MACHINES AND MECHANISMS MACHINES AND MECHANISMS APPLIED KINEMATIC ANALYSIS Fourth Edition David H. Myszka University of Dayton PEARSON ж rentice Hall Pearson Education International Boston Columbus Indianapolis New York San Francisco

More information

Simulation: Solving Dynamic Models ABE 5646 Week 12, Spring 2009

Simulation: Solving Dynamic Models ABE 5646 Week 12, Spring 2009 Simulation: Solving Dynamic Models ABE 5646 Week 12, Spring 2009 Week Description Reading Material 12 Mar 23- Mar 27 Uncertainty and Sensitivity Analysis Two forms of crop models Random sampling for stochastic

More information

Multivariate Capability Analysis

Multivariate Capability Analysis Multivariate Capability Analysis Summary... 1 Data Input... 3 Analysis Summary... 4 Capability Plot... 5 Capability Indices... 6 Capability Ellipse... 7 Correlation Matrix... 8 Tests for Normality... 8

More information

Analysis of Simulation Results

Analysis of Simulation Results Analysis of Simulation Results Raj Jain Washington University Saint Louis, MO 63130 Jain@cse.wustl.edu Audio/Video recordings of this lecture are available at: http://www.cse.wustl.edu/~jain/cse574-08/

More information

Algorithmic Graph Theory and Perfect Graphs

Algorithmic Graph Theory and Perfect Graphs Algorithmic Graph Theory and Perfect Graphs Second Edition Martin Charles Golumbic Caesarea Rothschild Institute University of Haifa Haifa, Israel 2004 ELSEVIER.. Amsterdam - Boston - Heidelberg - London

More information

Access ComprehGnsiwG. Shelley Gaskin, Carolyn McLellan, and. Nancy Graviett. with Microsoft

Access ComprehGnsiwG. Shelley Gaskin, Carolyn McLellan, and. Nancy Graviett. with Microsoft with Microsoft Access 2010 ComprehGnsiwG Shelley Gaskin, Carolyn McLellan, and Nancy Graviett Prentice Hall Boston Columbus Indianapolis New York San Francisco Upper Saddle River Imsterdam Cape Town Dubai

More information

SigmaXL Feature List Summary, What s New in Versions 6.0, 6.1 & 6.2, Installation Notes, System Requirements and Getting Help

SigmaXL Feature List Summary, What s New in Versions 6.0, 6.1 & 6.2, Installation Notes, System Requirements and Getting Help SigmaXL Feature List Summary, What s New in Versions 6.0, 6.1 & 6.2, Installation Notes, System Requirements and Getting Help Copyright 2004-2013, SigmaXL Inc. SigmaXL Version 6.2 Feature List Summary

More information

Digital System Design with SystemVerilog

Digital System Design with SystemVerilog Digital System Design with SystemVerilog Mark Zwolinski AAddison-Wesley Upper Saddle River, NJ Boston Indianapolis San Francisco New York Toronto Montreal London Munich Paris Madrid Capetown Sydney Tokyo

More information

Introduction to the course

Introduction to the course Introduction to the course Lecturer: Dmitri A. Moltchanov E-mail: moltchan@cs.tut.fi http://www.cs.tut.fi/ moltchan/modsim/ http://www.cs.tut.fi/kurssit/tlt-2706/ 1. What is the teletraffic theory? Multidisciplinary

More information

book 2014/5/6 15:21 page v #3 List of figures List of tables Preface to the second edition Preface to the first edition

book 2014/5/6 15:21 page v #3 List of figures List of tables Preface to the second edition Preface to the first edition book 2014/5/6 15:21 page v #3 Contents List of figures List of tables Preface to the second edition Preface to the first edition xvii xix xxi xxiii 1 Data input and output 1 1.1 Input........................................

More information

SAS (Statistical Analysis Software/System)

SAS (Statistical Analysis Software/System) SAS (Statistical Analysis Software/System) SAS Adv. Analytics or Predictive Modelling:- Class Room: Training Fee & Duration : 30K & 3 Months Online Training Fee & Duration : 33K & 3 Months Learning SAS:

More information

Microsoft Visual Studio 2010

Microsoft Visual Studio 2010 Microsoft Visual Studio 2010 A Beginner's Guide Joe Mayo Mc Grauu Hill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto Contents ACKNOWLEDGMENTS

More information

FUNDAMENTALS OF. Database S wctpmc. Shamkant B. Navathe College of Computing Georgia Institute of Technology. Addison-Wesley

FUNDAMENTALS OF. Database S wctpmc. Shamkant B. Navathe College of Computing Georgia Institute of Technology. Addison-Wesley FUNDAMENTALS OF Database S wctpmc SIXTH EDITION Ramez Elmasri Department of Computer Science and Engineering The University of Texas at Arlington Shamkant B. Navathe College of Computing Georgia Institute

More information

PTC Mathcad Prime 3.0

PTC Mathcad Prime 3.0 Essential PTC Mathcad Prime 3.0 A Guide for New and Current Users Brent Maxfield, P.E. AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO SINGAPORE SYDNEY TOKYO @ Academic

More information

The JMT Simulator for Performance Evaluation of Non-Product-Form Queueing Networks

The JMT Simulator for Performance Evaluation of Non-Product-Form Queueing Networks Politecnico di Milano - DEI Milan, Italy The JMT Simulator for Performance Evaluation of Non-Product-Form Queueing Networks Marco Bertoli, Giuliano Casale, Giuseppe Serazzi Speaker: Giuliano Casale March

More information

Modern C++ Design. Generic Programming and Design Patterns Applied. Andrei Alexandrescu

Modern C++ Design. Generic Programming and Design Patterns Applied. Andrei Alexandrescu Modern C++ Design Generic Programming and Design Patterns Applied Andrei Alexandrescu ADDISON-WESLEY Boston San Francisco New York Toronto Montreal London Munich Paris Madrid Capetown Sydney Tokyo Singapore

More information

Probabilistic Robotics

Probabilistic Robotics Probabilistic Robotics Sebastian Thrun Wolfram Burgard Dieter Fox The MIT Press Cambridge, Massachusetts London, England Preface xvii Acknowledgments xix I Basics 1 1 Introduction 3 1.1 Uncertainty in

More information

Analysis of Panel Data. Third Edition. Cheng Hsiao University of Southern California CAMBRIDGE UNIVERSITY PRESS

Analysis of Panel Data. Third Edition. Cheng Hsiao University of Southern California CAMBRIDGE UNIVERSITY PRESS Analysis of Panel Data Third Edition Cheng Hsiao University of Southern California CAMBRIDGE UNIVERSITY PRESS Contents Preface to the ThirdEdition Preface to the Second Edition Preface to the First Edition

More information

Discrete Event Simulation & VHDL. Prof. K. J. Hintz Dept. of Electrical and Computer Engineering George Mason University

Discrete Event Simulation & VHDL. Prof. K. J. Hintz Dept. of Electrical and Computer Engineering George Mason University Discrete Event Simulation & VHDL Prof. K. J. Hintz Dept. of Electrical and Computer Engineering George Mason University Discrete Event Simulation Material from VHDL Programming with Advanced Topics by

More information

Cloud Computing and SOA Convergence in Your Enterprise

Cloud Computing and SOA Convergence in Your Enterprise Cloud Computing and SOA Convergence in Your Enterprise A Step-by-Step Guide David S. Lint hicum A Addison-Wesley Upper Saddle River, NT Boston Indianapolis San Francisco New York Toronto Montreal London

More information

Institute for Statics und Dynamics of Structures Fuzzy Time Series

Institute for Statics und Dynamics of Structures Fuzzy Time Series Institute for Statics und Dynamics of Structures Fuzzy Time Series Bernd Möller 1 Description of fuzzy time series 2 3 4 5 Conclusions Folie 2 von slide422 1 Description of fuzzy time series 2 3 4 5 Conclusions

More information

Computational Methods. Randomness and Monte Carlo Methods

Computational Methods. Randomness and Monte Carlo Methods Computational Methods Randomness and Monte Carlo Methods Manfred Huber 2010 1 Randomness and Monte Carlo Methods Introducing randomness in an algorithm can lead to improved efficiencies Random sampling

More information

Modern Information Retrieval

Modern Information Retrieval Modern Information Retrieval Ricardo Baeza-Yates Berthier Ribeiro-Neto ACM Press NewYork Harlow, England London New York Boston. San Francisco. Toronto. Sydney Singapore Hong Kong Tokyo Seoul Taipei. New

More information

MariaDB Crash Course. A Addison-Wesley. Ben Forta. Upper Saddle River, NJ Boston. Indianapolis. Singapore Mexico City. Cape Town Sydney.

MariaDB Crash Course. A Addison-Wesley. Ben Forta. Upper Saddle River, NJ Boston. Indianapolis. Singapore Mexico City. Cape Town Sydney. MariaDB Crash Course Ben Forta A Addison-Wesley Upper Saddle River, NJ Boston Indianapolis San Francisco New York Toronto Montreal London Munich Paris Madrid Cape Town Sydney Tokyo Singapore Mexico City

More information

CTS. Specialist. Certified Technology. Sven Laurik EXAM GUIDE. Mc Graw Hill. Chicago San Francisco Lisbon. New York. London Madrid Mexico City Milan

CTS. Specialist. Certified Technology. Sven Laurik EXAM GUIDE. Mc Graw Hill. Chicago San Francisco Lisbon. New York. London Madrid Mexico City Milan CTS Certified Technology Specialist EXAM GUIDE Sven Laurik Mc Graw Hill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto McGraw-Hill

More information

Simulation. Outline. Common Mistakes in Simulation (3 of 4) Common Mistakes in Simulation (2 of 4) Performance Modeling Lecture #8

Simulation. Outline. Common Mistakes in Simulation (3 of 4) Common Mistakes in Simulation (2 of 4) Performance Modeling Lecture #8 Introduction (1 of 3) The best advice to those about to embark on a very large simulation is often the same as Punch s famous advice to those about to marry: Don t! Bratley, Fox and Schrage (1986) Simulation

More information

Univariate Extreme Value Analysis. 1 Block Maxima. Practice problems using the extremes ( 2.0 5) package. 1. Pearson Type III distribution

Univariate Extreme Value Analysis. 1 Block Maxima. Practice problems using the extremes ( 2.0 5) package. 1. Pearson Type III distribution Univariate Extreme Value Analysis Practice problems using the extremes ( 2.0 5) package. 1 Block Maxima 1. Pearson Type III distribution (a) Simulate 100 maxima from samples of size 1000 from the gamma

More information

CCNA Cisco Certified Network Associate Study Guide

CCNA Cisco Certified Network Associate Study Guide CCNA Cisco Certified Network Associate Study Guide (Exam 640-407) Osborne/McGraw-Hill is an independent entity from Cisco Systems, Inc. and not affiliated with Cisco Systems, Inc. in any manner. Cisco

More information

Input Analysis. Input Analysis: Specifying Model Parameters, Distributions. Deterministic vs. Random Inputs

Input Analysis. Input Analysis: Specifying Model Parameters, Distributions. Deterministic vs. Random Inputs Input Analysis Input Analysis: Specifying Model Parameters, Distributions Structural modeling: what we ve done so far Logical aspects entities, resources, paths, etc. Quantitative modeling Numerical, distributional

More information

Adaptive System Identification and Signal Processing Algorithms

Adaptive System Identification and Signal Processing Algorithms Adaptive System Identification and Signal Processing Algorithms edited by N. Kalouptsidis University of Athens S. Theodoridis University of Patras Prentice Hall New York London Toronto Sydney Tokyo Singapore

More information

A New Statistical Procedure for Validation of Simulation and Stochastic Models

A New Statistical Procedure for Validation of Simulation and Stochastic Models Syracuse University SURFACE Electrical Engineering and Computer Science L.C. Smith College of Engineering and Computer Science 11-18-2010 A New Statistical Procedure for Validation of Simulation and Stochastic

More information

PeopleSoft PeopleTools Tips & Techniques

PeopleSoft PeopleTools Tips & Techniques ORACLE Oracle Press PeopleSoft PeopleTools Tips & Techniques Jim J. Marion Mc Graw Hill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto

More information

Quality Code. Software Testing Principles, Practices, and Patterns. Stephen Vance. AAddison-Wesley

Quality Code. Software Testing Principles, Practices, and Patterns. Stephen Vance. AAddison-Wesley Quality Code Software Testing Principles, Practices, and Patterns Stephen Vance AAddison-Wesley Upper Saddle River, NJ Boston Indianapolis San Francisco New York Toronto Montreal London Munich Paris Madrid

More information

Modern C++ Design. Generic Programming and Design Patterns Applied. Andrei Alexandrescu. .~Addison-Wesley

Modern C++ Design. Generic Programming and Design Patterns Applied. Andrei Alexandrescu. .~Addison-Wesley Modern C++ Design Generic Programming and Design Patterns Applied Andrei Alexandrescu.~Addison-Wesley Boston " San Francisco " New York " Toronto " Montreal London " Munich " Paris " Madrid Capetown "

More information

Monte Carlo Methods and Statistical Computing: My Personal E

Monte Carlo Methods and Statistical Computing: My Personal E Monte Carlo Methods and Statistical Computing: My Personal Experience Department of Mathematics & Statistics Indian Institute of Technology Kanpur November 29, 2014 Outline Preface 1 Preface 2 3 4 5 6

More information

Section 6.2: Generating Discrete Random Variates

Section 6.2: Generating Discrete Random Variates Section 6.2: Generating Discrete Random Variates Discrete-Event Simulation: A First Course c 2006 Pearson Ed., Inc. 0-13-142917-5 Discrete-Event Simulation: A First Course Section 6.2: Generating Discrete

More information

1. Introduction. 2. Program structure. HYDROGNOMON components. Storage and data acquisition. Instruments and PYTHIA. Statistical

1. Introduction. 2. Program structure. HYDROGNOMON components. Storage and data acquisition. Instruments and PYTHIA. Statistical HYDROGNOMON: A HYDROLOGICAL DATA MANAGEMENT AND PROCESSING SOFTWARE TOOL European Geosciences Union (EGU) General Assembly, Vienna, Austria, 25-29 April 2005 Session HS29: Hydrological modelling software

More information

Time Series Analysis by State Space Methods

Time Series Analysis by State Space Methods Time Series Analysis by State Space Methods Second Edition J. Durbin London School of Economics and Political Science and University College London S. J. Koopman Vrije Universiteit Amsterdam OXFORD UNIVERSITY

More information

PYTHON. p ykos vtawynivis. Second eciitiovl. CO Ve, WESLEY J. CHUN

PYTHON. p ykos vtawynivis. Second eciitiovl. CO Ve, WESLEY J. CHUN CO Ve, PYTHON p ykos vtawynivis Second eciitiovl WESLEY J. CHUN. PRENTICE HALL Upper Saddle River, NJ Boston Indianapolis San Francisco New York Toronto Montreal London Munich Paris Madrid Capetown Sydney

More information

Data Structures and Abstractions with Java

Data Structures and Abstractions with Java Global edition Data Structures and Abstractions with Java Fourth edition Frank M. Carrano Timothy M. Henry Data Structures and Abstractions with Java TM Fourth Edition Global Edition Frank M. Carrano University

More information

OpenGL SUPERBIBLE. Fifth Edition. Comprehensive Tutorial and Reference. Richard S. Wright, Jr. Nicholas Haemel Graham Sellers Benjamin Lipchak

OpenGL SUPERBIBLE. Fifth Edition. Comprehensive Tutorial and Reference. Richard S. Wright, Jr. Nicholas Haemel Graham Sellers Benjamin Lipchak OpenGL SUPERBIBLE Fifth Edition Comprehensive Tutorial and Reference Richard S. Wright, Jr. Nicholas Haemel Graham Sellers Benjamin Lipchak AAddison-Wesley Upper Saddle River, NJ Boston Indianapolis San

More information

PATTERN CLASSIFICATION AND SCENE ANALYSIS

PATTERN CLASSIFICATION AND SCENE ANALYSIS PATTERN CLASSIFICATION AND SCENE ANALYSIS RICHARD O. DUDA PETER E. HART Stanford Research Institute, Menlo Park, California A WILEY-INTERSCIENCE PUBLICATION JOHN WILEY & SONS New York Chichester Brisbane

More information

MAKING PIC MICROCONTROLLER INSTRUMENTS AND CONTROLLERS

MAKING PIC MICROCONTROLLER INSTRUMENTS AND CONTROLLERS MAKING PIC MICROCONTROLLER INSTRUMENTS AND CONTROLLERS HARPRIT SINGH SANDHU New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto CONTENTS

More information

Programming with POSIX Threads

Programming with POSIX Threads Programming with POSIX Threads David R. Butenhof :vaddison-wesley Boston San Francisco New York Toronto Montreal London Munich Paris Madrid Capetown Sidney Tokyo Singapore Mexico City Contents List of

More information

DATA ANALYSIS USING HIERARCHICAL GENERALIZED LINEAR MODELS WITH R

DATA ANALYSIS USING HIERARCHICAL GENERALIZED LINEAR MODELS WITH R DATA ANALYSIS USING HIERARCHICAL GENERALIZED LINEAR MODELS WITH R Lee, Rönnegård & Noh LRN@du.se Lee, Rönnegård & Noh HGLM book 1 / 25 Overview 1 Background to the book 2 A motivating example from my own

More information

Implementation and. Oracle VM. Administration Guide. Oracle Press ORACLG. Mc Grauv Hill. Edward Whalen

Implementation and. Oracle VM. Administration Guide. Oracle Press ORACLG. Mc Grauv Hill. Edward Whalen ORACLG Oracle Press Oracle VM Implementation and Administration Guide Edward Whalen Mc Grauv Hill New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore

More information

DATA ANALYSIS USING HIERARCHICAL GENERALIZED LINEAR MODELS WITH R

DATA ANALYSIS USING HIERARCHICAL GENERALIZED LINEAR MODELS WITH R DATA ANALYSIS USING HIERARCHICAL GENERALIZED LINEAR MODELS WITH R Lee, Rönnegård & Noh LRN@du.se Lee, Rönnegård & Noh HGLM book 1 / 24 Overview 1 Background to the book 2 Crack growth example 3 Contents

More information

UNIVERSITY OF MUMBAI

UNIVERSITY OF MUMBAI AC 30/09/2016 Item No. 4.17 UNIVERSITY OF MUMBAI Revised Syllabus for PhdCourse Work (As per Credit Based Semester and Grading System with effect from the academic year 2017 2018) Course Work Structure

More information

Application Programming

Application Programming Multicore Application Programming For Windows, Linux, and Oracle Solaris Darryl Gove AAddison-Wesley Upper Saddle River, NJ Boston Indianapolis San Francisco New York Toronto Montreal London Munich Paris

More information

Computer Animation. Algorithms and Techniques. z< MORGAN KAUFMANN PUBLISHERS. Rick Parent Ohio State University AN IMPRINT OF ELSEVIER SCIENCE

Computer Animation. Algorithms and Techniques. z< MORGAN KAUFMANN PUBLISHERS. Rick Parent Ohio State University AN IMPRINT OF ELSEVIER SCIENCE Computer Animation Algorithms and Techniques Rick Parent Ohio State University z< MORGAN KAUFMANN PUBLISHERS AN IMPRINT OF ELSEVIER SCIENCE AMSTERDAM BOSTON LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO

More information

Verification and Validation of X-Sim: A Trace-Based Simulator

Verification and Validation of X-Sim: A Trace-Based Simulator http://www.cse.wustl.edu/~jain/cse567-06/ftp/xsim/index.html 1 of 11 Verification and Validation of X-Sim: A Trace-Based Simulator Saurabh Gayen, sg3@wustl.edu Abstract X-Sim is a trace-based simulator

More information

Using MATLAB, SIMULINK and Control System Toolbox

Using MATLAB, SIMULINK and Control System Toolbox Using MATLAB, SIMULINK and Control System Toolbox A practical approach Alberto Cavallo Roberto Setola Francesco Vasca Prentice Hall London New York Toronto Sydney Tokyo Singapore Madrid Mexico City Munich

More information

Modern C++ Design. Generic Programming and Design Patterns Applied. Andrei Alexandrescu. AAddison-Wesley

Modern C++ Design. Generic Programming and Design Patterns Applied. Andrei Alexandrescu. AAddison-Wesley Modern C++ Design Generic Programming and Design Patterns Applied Andrei Alexandrescu f AAddison-Wesley Boston San Francisco New York Toronto Montreal London Munich Paris Madrid Capetown Sydney Tokyo Singapore

More information

EP2200 Queueing theory and teletraffic systems

EP2200 Queueing theory and teletraffic systems EP2200 Queueing theory and teletraffic systems Viktoria Fodor Laboratory of Communication Networks School of Electrical Engineering Lecture 1 If you want to model networks Or a complex data flow A queue's

More information