Simulation Modeling and Analysis with ARENASimulation 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, statistics, stochastic processes, input analysis, model validation and output analysis. All simulation-related concepts are illustrated in numerous Arena examples, encompassing production lines, manufacturing and inventory systems, transportation systems, and computer information systems in networked settings.
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Contents
| 1 | |
| 11 | |
| 23 | |
Random Number and Variate Generation | 55 |
Arena Basics | 65 |
Model Testing and Debugging Facilities | 107 |
Input Analysis | 123 |
Model Goodness Verification and Validation | 141 |
Modeling Production Lines | 223 |
Modeling Supply Chain Systems | 263 |
Modeling Transportation Systems | 313 |
Modeling Computer Information Systems | 369 |
Frequently Used Arena Constructs | 405 |
VBA in Arena | 415 |
| 431 | |
| 435 | |
Other editions - View all
Simulation Modeling and Analysis with Arena Tayfur Altiok,Benjamin Melamed No preview available - 2007 |
Common terms and phrases
Arena model arrival rate Assign module attribute autocorrelation function average number backorder batch box is displayed button called confidence interval corresponding Create module customer entity Decide module demand depicted in Figure dialog box dialog spreadsheet displayed in Figure entity enters entity proceeds estimate event example exit exponentially distributed failures FIFO FX(x gamma distribution gear entity histogram Hold module interarrival inventory level machine management segment menu model logic null hypothesis number of jobs operation option order entity parameters patient entities performance measures Poisson process probability Process module product type queue random variable Record module Release replication request resource Section Seize module sequence server node server process SIMAN simulation model simulation run specified Station module stochastic processes Tally TESþ throughput tollbooth toolbar transaction entity transporter tug boat unit circle unit entity utilization variate waiting workstation
Popular passages
Page 432 - The Transition and Autocorrelation structure of TES Processes Part I: General Theory".
Page 8 - The replacement time is normally distributed with a mean of 90 minutes and a standard deviation of 10 minutes, during which time the machine is nonproductive.
Page 40 - Essentially, this means that the probability of the time to the next arrival is independent of the time elapsed since the previous arrival.