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Operations Research: A Practical Introduction is just that: a
hands-on approach to the field of operations research (OR) and a
useful guide for using OR techniques in scientific decision making,
design, analysis and management. The text accomplishes two goals.
First, it provides readers with an introduction to standard
mathematical models and algorithms. Second, it is a thorough
examination of practical issues relevant to the development and use
of computational methods for problem solving. Highlights: All
chapters contain up-to-date topics and summaries A succinct
presentation to fit a one-term course Each chapter has references,
readings, and list of key terms Includes illustrative and current
applications New exercises are added throughout the text Software
tools have been updated with the newest and most popular software
Many students of various disciplines such as mathematics,
economics, industrial engineering and computer science often take
one course in operations research. This book is written to provide
a succinct and efficient introduction to the subject for these
students, while offering a sound and fundamental preparation for
more advanced courses in linear and nonlinear optimization, and
many stochastic models and analyses. It provides relevant
analytical tools for this varied audience and will also serve
professionals, corporate managers, and technical consultants.
Operations Research: A Practical Introduction is just that: a
hands-on approach to the field of operations research (OR) and a
useful guide for using OR techniques in scientific decision making,
design, analysis and management. The text accomplishes two goals.
First, it provides readers with an introduction to standard
mathematical models and algorithms. Second, it is a thorough
examination of practical issues relevant to the development and use
of computational methods for problem solving. Highlights: All
chapters contain up-to-date topics and summaries A succinct
presentation to fit a one-term course Each chapter has references,
readings, and list of key terms Includes illustrative and current
applications New exercises are added throughout the text Software
tools have been updated with the newest and most popular software
Many students of various disciplines such as mathematics,
economics, industrial engineering and computer science often take
one course in operations research. This book is written to provide
a succinct and efficient introduction to the subject for these
students, while offering a sound and fundamental preparation for
more advanced courses in linear and nonlinear optimization, and
many stochastic models and analyses. It provides relevant
analytical tools for this varied audience and will also serve
professionals, corporate managers, and technical consultants.
The scope of this book is limited to heuristics, metaheuristics,
and approximate methods and algorithms as applied to planning and
scheduling problems. While it is not possible to give a
comprehensive treatment of this topic in one book, the aim of this
work is to provide the reader with a diverse set of planning and
scheduling problems and different heuristic approaches to solve
them. The problems range from traditional single stage and parallel
machine problems to more modern settings such as robotic cells and
flexible job shop networks. Furthermore, some chapters deal with
deterministic problems while some others treat stochastic versions
of the problems. Unlike most of the literature that deals with
planning and scheduling problems in the manufacturing and
production environments, in this book the environments were
extended to nontraditional applications such as spatial scheduling
(optimizing space over time), runway scheduling, and surgical
scheduling. The solution methods used in the different chapters of
the book also spread from well-established heuristics and
metaheuristics such as Genetic Algorithms and Ant Colony
Optimization to more recent ones such as Meta-RaPS.
The scope of this book is limited to heuristics, metaheuristics,
and approximate methods and algorithms as applied to planning and
scheduling problems. While it is not possible to give a
comprehensive treatment of this topic in one book, the aim of this
work is to provide the reader with a diverse set of planning and
scheduling problems and different heuristic approaches to solve
them. The problems range from traditional single stage and parallel
machine problems to more modern settings such as robotic cells and
flexible job shop networks. Furthermore, some chapters deal with
deterministic problems while some others treat stochastic versions
of the problems. Unlike most of the literature that deals with
planning and scheduling problems in the manufacturing and
production environments, in this book the environments were
extended to nontraditional applications such as spatial scheduling
(optimizing space over time), runway scheduling, and surgical
scheduling. The solution methods used in the different chapters of
the book also spread from well-established heuristics and
metaheuristics such as Genetic Algorithms and Ant Colony
Optimization to more recent ones such as Meta-RaPS.
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