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Simulation Modeling and Analysis
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Basic simulation modeling. The nature of simulation. Systems, models, and simulation. Discrete-event simulation. Simulation of a single-server queueing system. Simulation of an inventory system. Distributed simulation. Steps in a simulation study. Other types of simulation. Advantages, disadvantages, and pitfalls of simulation. Modeling complex systems. List processing in simulation. A simple simulation language, SIMLIB. Single-server queueing simulation with SIMLIB. Time-shared computer model. Multiteller bank with jockeying. Job-shop model. Efficient event-list manipulation. Simulation software. Comparison of simulation languages with general-purpose languages. Classification of simulation software. Desirable software features. GPSS. SIMAN/cinema. SIMSCRIPT II. 5. SLAM II and related software. Comparison of simulation languages. Additional simulation software. Review of basic probability and statistics. Random variables and their properties. Simulation output data and stochastic processes. Estimation of means, variances, and correlations. Confidence intervals and hypothesis tests for the mean. The strong law of large numbers. The danger of replacing a probability distribution by its mean. Building valid and credible simulation models. Introduction and definitions. Some principles of valid simulation modeling. Verification of simulation computer programs. General perspectives on validation. A three-step approach for developing valid and credible simulation models. Statistical procedures for comparing real-world observations and simulation output data. Selecting input probability distributions. Useful probability distributions. Techniques for assessing sample independence. Activity I: Hypothesizing families of distributions. Activity II: estimation of parameters. Activity III: determining how representative the fitted distributions are. An extended example. Shifed and truncated distributions. Selecting a distribution in the absence of data. Models of arrival processes. Assessing the homogeneity of different data sets. Random-number generators. Linear congruential generators. Other kinds of generators. Testing random-number generators. Random-number generation on microcomputers. Generators used by simulation languages. Generating random variates. General approaches to generating random variates. Generating continuous random variates. Generating random variates. General approaches to generating random variates. Generating continuous random variates. Generating discrete random variates. Generating correlated random variates. Generating arrival processes. Output data analysis for a single system. Transient and steady-state behavior of a stochastic process. Types of simulations with regard to output analysis. Statistical analysis for terminating simulations. Statistical analysis for steady-state parameters. Statistical analysis for steady-state cycle parameters. Multiple measures of performance. Time plots of important variables. Comparing alternative system configurations. Confidence intervals for the difference between. Confidence intervals for comparing more than two systems. Ranking and selection. Variance-reduction techniques. Common random numbers. Antithetic variates. Control variates. Indirect estimation. Conditioning. Experimental design and optimization. 2 factorial designs. Coping with many factors. Response surfaces and metamodels. Gradient estimation. Simulation of manufacturing systems. Objectives of simulation in manufacturing. Simulation software for manufacturing applications. Modeling system randomness. Machine downtimes. An extended example. A simulation case study of a metal-parts manufacturing facility.
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