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008 | 110111s2011 xxk| s |||| 0|eng d | ||
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_a9780857291394 _9978-0-85729-139-4 |
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040 | _cMX-MeUAM | ||
050 | 4 | _aTA177.4-185 | |
082 | 0 | 4 |
_a658.5 _223 |
100 | 1 |
_aAllen, Theodore T. _eauthor. |
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245 | 1 | 0 |
_aIntroduction to Discrete Event Simulation and Agent-based Modeling _h[recurso electrónico] : _bVoting Systems, Health Care, Military, and Manufacturing / _cby Theodore T. Allen. |
264 | 1 |
_aLondon : _bSpringer London, _c2011. |
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300 |
_aXII, 215 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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505 | 0 | _a1. Introduction -- 2. Probability Theory and Monte Carlo -- 3. Input Analysis -- 4. Simulating Waiting Times -- 5. Output Analysis -- 6. Theory of Queues -- 7. Decision Support and Voting Systems Case Study -- 8. Variance Reduction Techniques and Quasi-Monte Carlo -- 9. Simulation Software and Visual Basic -- 10. Introduction to ARENA Software -- 11. Advanced Modeling with ARENA -- 12. Agents and New Directions -- 13. Answers to Odd Problems. | |
520 | _aDiscrete event simulation and agent-based modeling are increasingly recognized as critical for diagnosing and solving process issues in complex systems. Introduction to Discrete Event Simulation and Agent-based Modeling covers the techniques needed for success in all phases of simulation projects. These include: Definition – The reader will learn how to plan a project and communicate using a charter. Input analysis – The reader will discover how to determine defensible sample sizes for all needed data collections. They will also learn how to fit distributions to that data. Simulation – The reader will understand how simulation controllers work, the Monte Carlo (MC) theory behind them, modern verification and validation, and ways to speed up simulation using variation reduction techniques and other methods. Output analysis – The reader will be able to establish simultaneous intervals on key responses and apply selection and ranking, design of experiments (DOE), and black box optimization to develop defensible improvement recommendations. Decision support – Methods to inspire creative alternatives are presented, including lean production. Also, over one hundred solved problems are provided and two full case studies, including one on voting machines that received international attention. Introduction to Discrete Event Simulation and Agent-based Modeling demonstrates how simulation can facilitate improvements on the job and in local communities. It allows readers to competently apply technology considered key in many industries and branches of government. It is suitable for undergraduate and graduate students, as well as researchers and other professionals. | ||
650 | 0 | _aEngineering. | |
650 | 0 | _aComputer simulation. | |
650 | 0 | _aEngineering economy. | |
650 | 1 | 4 | _aEngineering. |
650 | 2 | 4 | _aEngineering Economics, Organization, Logistics, Marketing. |
650 | 2 | 4 | _aSimulation and Modeling. |
650 | 2 | 4 | _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. |
650 | 2 | 4 | _aOperations Research/Decision Theory. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9780857291387 |
856 | 4 | 0 |
_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-0-85729-139-4 |
596 | _a19 | ||
942 | _cLIBRO_ELEC | ||
999 |
_c198375 _d198375 |