LEADER 05557nam 2200709Ia 450 001 9910821263903321 005 20240516003723.0 010 $a1-281-11898-2 010 $a9786611118983 010 $a0-08-054895-4 035 $a(CKB)1000000000364107 035 $a(EBL)305656 035 $a(OCoLC)271801396 035 $a(SSID)ssj0000246161 035 $a(PQKBManifestationID)11237221 035 $a(PQKBTitleCode)TC0000246161 035 $a(PQKBWorkID)10180462 035 $a(PQKB)11720641 035 $a(Au-PeEL)EBL305656 035 $a(CaPaEBR)ebr10188257 035 $a(CaONFJC)MIL111898 035 $a(OCoLC)173483685 035 $a(MiAaPQ)EBC305656 035 $a(EXLCZ)991000000000364107 100 $a20070307d2007 uy 0 101 0 $aeng 135 $aurun|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aSimulation modeling and analysis with Arena /$fTayfur Altiok, Benjamin Melamed 205 $a1st ed. 210 $aAmsterdam ;$aBoston $cAcademic Press$dc2007 215 $a1 recurso en línea (462 p.) 300 $aDescription based upon print version of record. 311 $a0-12-370523-1 320 $aIncludes bibliographical references and index. 327 $aSimulation Modeling and Analysis with Arena; Copyright Page; Contents; Preface; Acknowledgments; Chapter 1: Introduction to Simulation Modeling; 1.1 Systems and Models; 1.2 Analytical Versus Simulation Modeling; 1.3 Simulation Modeling and Analysis; 1.4 Simulation Worldviews; 1.5 Model Building; 1.6 Simulation Costs and Risks; 1.7 Example: A Production Control Problem; 1.8 Project Report; Exercises; Chapter 2: Discrete Event Simulation; 2.1 Elements of Discrete Event Simulation; 2.2 Examples of DES Models; 2.2.1 Single Machine; 2.2.2 Single Machine with Failures 327 $a2.2.3 Single Machine with an Inspection Station and Associated Inventory2.3 Monte Carlo Sampling and Histories; 2.3.1 Example: Work Station Subject to Failures and Inventory Control; 2.4 DES Languages; Exercises; Chapter 3: Elements of Probability and Statistics; 3.1 Elementary Probability Theory; 3.1.1 Probability Spaces; 3.1.2 Conditional Probabilities; 3.1.3 Dependence and Independence; 3.2 Random Variables; 3.3 Distribution Functions; 3.3.1 Probability Mass Functions; 3.3.2 Cumulative Distribution Functions; 3.3.3 Probability Density Functions; 3.3.4 Joint Distributions; 3.4 Expectations 327 $a3.5 Moments3.6 Correlations; 3.7 Common Discrete Distributions; 3.7.1 Generic Discrete Distribution; 3.7.2 Bernoulli Distribution; 3.7.3 Binomial Distribution; 3.7.4 Geometric Distribution; 3.7.5 Poisson Distribution; 3.8 Common Continuous Distributions; 3.8.1 Uniform Distribution; 3.8.2 Step Distribution; 3.8.3 Triangular Distribution; 3.8.4 Exponential Distribution; 3.8.5 Normal Distribution; 3.8.6 Lognormal Distribution; 3.8.7 Gamma Distribution; 3.8.8 Student's t Distribution; 3.8.9 F Distribution; 3.8.10 Beta Distribution; 3.8.11 Weibull Distribution; 3.9 Stochastic Processes 327 $a3.9.1 Iid Processes3.9.2 Poisson Processes; 3.9.3 Regenerative (Renewal) Processes; 3.9.4 Markov Processes; 3.10 Estimation; 3.11 Hypothesis Testing; Exercises; Chapter 4: Random Number and Variate Generation; 4.1 Variate and Process Generation; 4.2 Variate Generation Using the Inverse Transform Method; 4.2.1 Generation of Uniform Variates; 4.2.2 Generation of Exponential Variates; 4.2.3 Generation of Discrete Variates; 4.2.4 Generation of Step Variates from Histograms; 4.3 Process Generation; 4.3.1 Iid Process Generation; 4.3.2 Non-Iid Process Generation; Exercises; Chapter 5: Arena Basics 327 $a5.1 Arena Home Screen5.1.1 Menu Bar; 5.1.2 Project Bar; 5.1.3 Standard Toolbar; 5.1.4 Draw and View Bars; 5.1.5 Animate and Animate Transfer Bars; 5.1.6 Run Interaction Bar; 5.1.7 Integration Bar; 5.1.8 Debug Bar; 5.2 Example: A Simple Workstation; 5.3 Arena Data Storage Objects; 5.3.1 Variables; 5.3.2 Expressions; 5.3.3 Attributes; 5.4 Arena Output Statistics Collection; 5.4.1 Statistics Collection via the Statistic Module; 5.4.2 Statistics Collection via the Record Module; 5.5 Arena Simulation and Output Reports; 5.6 Example: Two Processes in Series; 5.7 Example: A Hospital Emergency Room 327 $a5.7.1 Problem Statement 330 $aSimulation Modeling and Analysis with Arena is a highly readable textbook which treats the essentials of the Monte Carlo discrete-event simulation methodology, and does so in the context of a popular Arena simulation environment.? It treats simulation modeling as an in-vitro laboratory that facilitates the understanding of complex systems and experimentation with what-if scenarios in order to estimate their performance metrics. The book contains chapters on the simulation modeling methodology and the underpinnings of discrete-event systems, as well as the relevant underlying probability, sta 606 $aMonte Carlo method 606 $aDigital computer simulation 606 $aIndustrial management$xComputer simulation 615 0$aMonte Carlo method. 615 0$aDigital computer simulation. 615 0$aIndustrial management$xComputer simulation. 676 $a518.282 676 $a519.282 676 $a519.282 700 $aAltiok$b Tayfur$0447950 701 $aMelamed$b Benjamin$0141789 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910821263903321 996 $aSimulation modeling and analysis with Arena$93968587 997 $aUNINA