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Titlebook: Introduction to Discrete Event Simulation and Agent-based Modeling; Voting Systems, Heal Theodore T. Allen Textbook 2011 Springer-Verlag Lo

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書(shū)目名稱Introduction to Discrete Event Simulation and Agent-based Modeling
副標(biāo)題Voting Systems, Heal
編輯Theodore T. Allen
視頻videohttp://file.papertrans.cn/474/473626/473626.mp4
概述Provides readers with the ability to competently apply technology that is considered key in many industries and branches of government.Gives a case study of the author‘s work on the 2008 USA president
圖書(shū)封面Titlebook: Introduction to Discrete Event Simulation and Agent-based Modeling; Voting Systems, Heal Theodore T. Allen Textbook 2011 Springer-Verlag Lo
描述Discrete 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 proble
出版日期Textbook 2011
關(guān)鍵詞Discrete Event Simulation; Elections; Statistical Simulation; Variance-Reduction; Waiting Lines; Engineer
版次1
doihttps://doi.org/10.1007/978-0-85729-139-4
isbn_softcover978-1-4471-5725-0
isbn_ebook978-0-85729-139-4
copyrightSpringer-Verlag London Limited 2011
The information of publication is updating

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Input Analysis,assumption-making. In many cases, the time and cost of input analysis will actually exceed expenses from all other phases. It may also be necessary to put instrumentation into place to provide the accurate time measures, greatly delaying the entire project.
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Decision Support and Voting Systems Case Study,is usually only useful for evaluating hypothetical changes. Without inspired alternatives, the value is limited. Dogmas like theory of constraints (.) and lean production (.) can provide the needed inspiration.
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Variance Reduction Techniques and Quasi-Monte Carlo, the expected worst precinct closing time estimate. In 5?years, the identical simulation might require less than a single minute. Yet, we might choose to include in the simulation details about registration process and voter demographics so that the resulting time might require more than 10?min again.
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Agents and New Directions,ontrasts with system dynamics and other differential equation-based modeling including finite element methods (FEM). In FEMs, e.g., there are relatively few entities and the interplay of physical or cognitive forces dominates.
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Introduction to ARENA Software,ies of software such as AutoMod, MODSIM, PROMODEL, SIMIO, and many others. Also, the Input Analyzer and the PAN lack statistical analysis features in more powerful software such as SAS. Yet, ARENA is comparatively easy-to-learn, of moderate cost (around $20K per a professional license), and can be successfully used for even large projects.
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