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discrete-event system simulation fourth edition jerry banks independent consultanf john s carson i i brooks automation barry l nelson north western university david m nicol university o f illinois urbana-champaign prentice lnternational serieslndustrial and systems engineering in hall w j fabrycky and j h mize editors upper saddle river new jersey 07458

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contents preface about the authors xiii i introduction to discrete-event system simulation chapter 1 introduction to simulation 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 1.10 1.11 when simulation 1s the appropnate tool when simulation 1s 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 references exercises chapter 2 simulation examples 2.1 simulation of queueing systems 2.2 simulation of inventory systems

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vi contents 2.3 other examples of simulation 2.4 summary references exercises chapter 3 general principles 3.1 concepts in discrete-event simulation 3.1.1 the event schedulingitime advance algorithm 3.1.2 world views 3.1.3 manual simulation using event scheduling 3.2 list processing 3.2.1 lists basic properties and operations 3.2.2 using arrays for list processing 3.2.3 using dynamic allocation and linked lists 3.2.4 advanced techniques 3.3 summary references exercises chapter 4 simulation software 4.1 history of simulation software 4.1.1 the period of search 1955-60 4.1.2 the advent 1961-65 4.1.3 the formative period 1966-70 4.1.4 the expansion period 1971-78 4.1.5 consolidation and regeneration 1979-86 4.1.6 integrated environments 1987-present 4.2 selection of simulation software 4.3 an example simulation 4.4 simulation in java 4.5 simulation in gpss 4.6 simulation in ssf 4.7 simulation software 4.7.1 arena 4.7.2 automod 4.7.3 extend 4.7.4 flexsim 4.7.5 micro saint 4.7.6 promodel 4.7.7 quest 4.7.8 simul8 4.7.9 witness

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contents vii 4.8 experimentation and statistical-analysis tools 4.8.1 common features 4.8.2 products references exercises i1 mathematical and statistical models chapter 5 5.1 5.2 5.3 5.4 5.5 147 149 statistical models in simulation review of tenninology and concepts useful statistical models discrete distributions continuous distributions poisson process 5.5.1 properties of a poisson process 5.5.2 nonstationary poisson process 5.6 empirical distributions 5.7 summary references exercises chapter 6 queueing models 201 202 202 204 204 205 206 208 208 209 21 1 212 213 218 220 221 227 233 235 6.1 characteristics of queueing systems 6.1.1 the calling population 6.1.2 system capacity 6.1.3 the arrival process 6.1.4 queue behavior and queue discipline 6.1.5 service times and the service mechanism 6.2 queueing notation 6.3 long-run measures of performance of queueing systems 6.3.1 time-average number in system l 6.3.2 average time spent in system per customer w 6.3.3 the conservation equation l hw 6.3.4 server utilization 6.3.5 costs in queueing problems 6.4 steady-state behavior of infinite-population markovian models 6.4.1 single-server queues with poisson arrivals and unlimited capacity m/g/1 6.4.2 multiserver queue m/m/c/oo/oo 6.4.3 multiserver queues with poisson arrivals and limited capacity m/m/c/n/oo 6.5 steady-state behavior of finite-population models m/m/c/wk

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viii 6.6 networks of queues 6.7 summary references exercises contents i11 random numbers chapter 7 random-number generation 7.1 properties of random numbers 7.2 generation of pseudo-random numbers 7.3 techniques for generating random numbers 7.3.1 linear congruential method 7.3.2 combined linear congruential generators 7.3.3 random-number streams 7.4 tests for random numbers 7.4.1 frequency tests 7.4.2 tests for autocorrelation 7.5 summary references exercises chapter 8 random-variate generation 8.1 inverse-transform technique 8.1.1 exponential distribution 8.1.2 uniform distribution 8.1.3 weibull distribution 8.1.4 triangular distribution 8.1.5 empirical continuous distributions 8.1.6 continuous distributions without a closed-form inverse 8.1.7 discrete distributions 8.2 acceptance-rejection technique 8.2.1 poisson distribution 8.2.2 nonstationary poisson process 8.2.3 gamma distribution 8.3 special properties 8.3.1 direct transformation for the normal and lognormal distributions 8.3.2 convolution method 8.3.3 more special properties 8.4 summary references exercises

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contents ix iv analysis of simulation data chapter 9 input modeling data collection identifying the distribution with data 9.2.1 histograms 9.2.2 selecting the family of distributions 9.2.3 quantile-quantile plots parameter estimation 9.3.1 preliminary statistics sample mean and sample variance 9.3.2 suggested estimators goodness-of-fit tests 9.4.1 chi-square test 9.4.2 chi-square test with equal probabilities 9.4.3 kolmogorov-srnirnov goodness-of-fit test 9.4.4 p-values and best fits fitting a nonstationary poisson process selecting input models without data multivariate and time-senes input models 9.7.1 covariance and correlation 9.7.2 multivariate input models 9.7.3 time-series input models 9.7.4 the normal-to-anything transformation summary references exercises chapter 10 verification and validation of simulation models 10.1 model-building verification and validation 10.2 verification of simulation models 10.3 calibration and validation of models 10.3.1 face validity 10.3.2 validation of model assumptions 10.3.3 validating input-output transformations 10.3.4 input-output validation using historical input data 10.3.5 input-output validation using a tunng test 10.4 summary references exercises chapter 11 output analysis for a single model 11.1 types of simulations with respect to output analysis 11.2 stochastic nature of output data

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x contents 11.3 measures of performance and their estimation 11.3.1 point estimation 11.3.2 confidence-interval estimation 11.4 output analysis for terminating simulations 11.4.1 statistical background 11.4.2 confidence intervals with specified precision 11.4.3 quantiles 11.4.4 estimating probabilities and quantiles from summary data 11.5 output analysis for steady-state simulations 11.5.1 initialization bias in steady-state simulations 11.5.2 error estimation for steady-state simulation 11.5.3 replication method for steady-state simulations 11.5.4 sample size in steady-state simulations 11.5.5 batch means for interval estimation in steady-state simulations 11.5.6 quantiles 11.6 summary references exercises chapter 12 comparison and evaluation of alternative system designs 12.1 comparison of two system designs 12.1.1 independent sampling with equal variances 12.1.2 independent sampling with unequal variances 12.1.3 common random numbers crn 12.1.4 confidence intervals with specified precision 12.2 comparison of several system designs 12.2.1 bonferroni approach to multiple comparisons 12.2.2 bonferroni approach to selecting the best 12.2.3 bonferroni approach to screening 12.3 metamodeling 12.3.1 simple linear regression 12.3.2 testing for significance of regression 12.3.3 multiple linear regression 12.3.4 random-number assignment for regression 12.4 optimization via simulation 12.4.1 what does optimization via simulation mean 12.4.2 why is optimization via simulation difficult 12.4.3 using robust heunstics 12.4.4 an illustration random search

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contents xi 12.5 summary references exercises v applications chapter 13 simulation of manufacturing and material-handling systems 13.1 manufactunng and material-handling simulations 13.1.1 models of manufacturing systems 13.1.2 models of material-handling 13.1.3 some cornmon material-handling equipment 13.2 goals and performance measures 13.3 issues in manufacturing and material-handling simulations 13.3.1 modeling downtimes and failures 13.3.2 trace-driven models 13.4 case studies of the simulation of manufacturing and material-handling systems 13.5 manufacturing example a job-shop simulation 13.5.1 system description and model assumptions 13.5.2 presimulation analysis 13.5.3 simulation model and analysis of the designed system 13.5.4 analysis of station utilization 13.5.5 analysis of potential system improvements 13.5.6 concluding words 13.6 summary references exercises chapter 14 simulation of computer systems 14.1 introduction 14.2 simulation tools 14.2.1 process orientation 14.2.2 event orientation 14.3 model input 14.3.1 modulated poisson process 14.3.2 virtual-memory referencing 14.4 high-level computer-system simulation 14.5 cpu simulation 14.6 memory simulation

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xii 14.7 summary references exercises contents chapter 15 simulation of computer networks 15.1 introduction 15.2 traffic modeling 15.3 media access control 15.3.1 token-passing protocols 15.3.2 ethernet 15.4 data link layer 15.5 tcp 15.6 model construction 15.6.1 construction 15.6.2 example 15.7 summary references exercises 550 appendix index

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www.ncetianz.webs.com chapter ­ 1 introduction to simulation nc et -1 ia nz

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www.ncetianz.webs.com simulation a simulation is the imitation of the operation of a real-world process or system over time brief explanation · the behavior of a system as it evolves over time is studied by developing a simulation model · this model takes the form of a set of assumptions concerning the operation of the system the assumptions are expressed in o mathematical relationships o logical relationships o symbolic relationships between the entities of the system measures of performance the model solved by mathematical methods such as differential calculus probability theory algebraic methods has the solution usually consists of one or more numerical parameters which are called measures of performance 1.1 when simulation is the appropriate tool · simulation enables the study of and experimentation with the internal interactions of a complex system or of a subsystem within a complex system · informational organizational and environmental changes can be simulated and the effect of those alternations on the model s behavior can be observer · the knowledge gained in designing a simulation model can be of great value toward suggesting improvement in the system under investigation · simulation can be used as a pedagogical device to reinforce analytic solution methodologies nc et ia -2 · by changing simulation inputs and observing the resulting outputs valuable insight may be obtained into which variables are most important and how variables interact nz

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www.ncetianz.webs.com · simulation can be used to experiment with new designs or policies prior to implementation so as to prepare for what may happen · simulation can be used to verify analytic solutions · by simulating different capabilities for a machine requirements can be determined · simulation models designed for training allow learning without the cost and disruption of on-the-job learning · animation shows a system in simulated operation so that the plan can be visualized · the modern systemfactory water fabrication plant service organization etc is so complex that the interactions can be treated only through simulation 1.2 when simulation is not appropriate · simulation should be used when the problem cannot be solved using common sense · simulation should not be used if the problem can be solved analytically · simulation should not be used if it is easier to perform direct experiments · simulation should not be used if the costs exceeds savings · simulation should not be performed if the resources or time are not available · if no data is available not even estimate simulation is not advised · if there is not enough time or the person are not available simulation is not appropriate nc et ia -3 nz

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www.ncetianz.webs.com · if managers have unreasonable expectation say too much soon ­ or the power of simulation is over estimated simulation may not be appropriate · if system behavior is too complex or cannot be defined simulation is not appropriate 1.3 advantages of simulation · simulation can also be used to study systems in the design stage · simulation models are run rather than solver · new policies operating procedures decision rules information flow etc can be explored without disrupting the ongoing operations of the real system · new hardware designs physical layouts transportation systems can be tested without committing resources for their acquisition · hypotheses about how or why certain phenomena occur can be tested for feasibility · time can be compressed or expanded allowing for a speedup or slowdown of the phenomena under investigation · insight can be obtained about the interaction of variables · insight can be obtained about the importance of variables to the performance of the system · a simulation study can help in understanding how the system operates rather than how individuals think the system operates · what-if questions can be answered useful in the design of new systems nc et ia -4 nz · bottleneck analysis can be performed indication where work-inprocess information materials and so on are being excessively delayed.

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www.ncetianz.webs.com 1.4 disadvantages of simulation · model building requires special training · simulation results may be difficult to interpret · simulation modeling and analysis can be time consuming and expensive · simulation is used in some cases when an analytical solution is possible or even preferable 1.5 applications of simulation manufacturing applications 1 analysis of electronics assembly operations 2 design and evaluation of a selective assembly station for highprecision scroll compressor shells 3 comparison of dispatching rules for semiconductor manufacturing using large facility models 4 evaluation of cluster tool throughput for thin-film head production 5 determining optimal lot size for a semiconductor backend factory 6 optimization of cycle time and utilization in semiconductor test manufacturing 7 analysis of storage and retrieval strategies in a warehouse 8 investigation of dynamics in a service oriented supply chain 9 model for an army chemical munitions disposal facility semiconductor manufacturing 1 comparison of dispatching rules using large-facility models 2 the corrupting influence of variability 3 a new lot-release rule for wafer fabs 4 assessment of potential gains in productivity due to proactive retied management 5 comparison of a 200 mm and 300 mm x-ray lithography cell 6 capacity planning with time constraints between operations 7 300 mm logistic system risk reduction construction engineering 1 construction of a dam embankment 2 trench less renewal of underground urban infrastructures nc et ia -5 nz

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