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.
