Discrete-event System Simulation
Prentice Hall, 2001 - Business & Economics - 594 pages
This book 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. Includes simulation in C++, the latest versions of the most widely used packages, and features of simulation output analysis software. Covers properties, modeling and random-variate generation from the lognormal distribution. Clarifies the difficult distinctions between terminating and steady-state simulation, and between within- and across-replication statistics. Contains up-to-date treatment of simulation of manufacturing and material handling systems. Emphasizes the hierarchical nature of computing systems, and how simulation techniques vary, depending on the level of abstraction. For readers wanting to learn more about system simulation.
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algorithm analysis approximately arrival rate assumed autocorrelation average number batch means called Chapter clock confidence interval Consider conveyor correlated sampling cost CSIM cumulative cycle defined delay demand determine digits discrete discussed downtimes entity Equation Example exponentially distributed function gamma distribution given histogram hour independent input models interarrival inventory Kolmogorov-Smirnov test long-run machine method minutes normal distribution number of customers observations occur operation optimization output data packages parameters performance point estimator pointer Poisson distribution Poisson process probability problem procedure queue length queueing models queueing system random numbers random variable Random-Digit random-number reject response run length sample mean scheduling Section sequence server utilization shown in Figure simulation analyst simulation languages simulation model solution standard deviation statistical steady-state technique thread tion triangular distribution uniformly distributed validation variance variates waiting line Weibull Weibull distribution
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