|
Showing 1 - 3 of
3 matches in All Departments
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 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.
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 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.
This book provides an accessible one-volume introduction to Lean
Six Sigma and statistics in engineering for students and industry
practitioners. Lean production has long been regarded as critical
to business success in many industries. Over the last ten years,
instruction in Six Sigma has been linked more and more with
learning about the elements of lean production. Building on the
success of the first and second editions, this book expands
substantially on major topics of increasing relevance to
organizations interested in Lean Six Sigma. Each chapter includes
summaries and review examples plus problems with their solutions.
As well as providing detailed definitions and case studies of all
Six Sigma methods, the book uniquely describes the relationship
between operations research techniques and Lean Six Sigma. Further,
this new edition features more introductory material on probability
and inference and information about Deming's philosophy, human
factors engineering, and the motivating potential score - the
material is tied more directly to the Certified Quality Engineer
(CQE) exam. New sections that explore motivation and change
management, which are critical subjects for achieving valuable
results have also been added. The book examines in detail Design
For Six Sigma (DFSS), which is critical for many organizations
seeking to deliver desirable products. It covers reliability,
maintenance, and product safety, to fully span the CQE body of
knowledge. It also incorporates recently emerging formulations of
DFSS from industry leaders and offers more introductory material on
experiment design, and includes practical experiments that will
help improve students' intuition and retention. The emphasis on
lean production, combined with recent methods relating to DFSS,
makes this book a practical, up-to-date resource for advanced
students, educators and practitioners.
|
You may like...
Hampstead
Diane Keaton, Brendan Gleeson, …
DVD
R66
Discovery Miles 660
|