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008 110111s2011 xxk| s |||| 0|eng d
020 _a9780857291394
_9978-0-85729-139-4
040 _cMX-MeUAM
050 4 _aTA177.4-185
082 0 4 _a658.5
_223
100 1 _aAllen, Theodore T.
_eauthor.
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.
300 _aXII, 215 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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