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This attractive textbook with its easy-to-follow presentation
provides a down-to-earth introduction to operations research for
students in a wide range of fields such as engineering, business
analytics, mathematics and statistics, computer science, and
econometrics. It is the result of many years of teaching and
collective feedback from students.The book covers the basic models
in both deterministic and stochastic operations research and is a
springboard to more specialized texts, either practical or
theoretical. The emphasis is on useful models and interpreting the
solutions in the context of concrete applications.The text is
divided into several parts. The first three chapters deal
exclusively with deterministic models, including linear programming
with sensitivity analysis, integer programming and heuristics, and
network analysis. The next three chapters primarily cover basic
stochastic models and techniques, including decision trees, dynamic
programming, optimal stopping, production planning, and inventory
control. The final five chapters contain more advanced material,
such as discrete-time and continuous-time Markov chains, Markov
decision processes, queueing models, and discrete-event
simulation.Each chapter contains numerous exercises, and a large
selection of exercises includes solutions.
This attractive textbook with its easy-to-follow presentation
provides a down-to-earth introduction to operations research for
students in a wide range of fields such as engineering, business
analytics, mathematics and statistics, computer science, and
econometrics. It is the result of many years of teaching and
collective feedback from students.The book covers the basic models
in both deterministic and stochastic operations research and is a
springboard to more specialized texts, either practical or
theoretical. The emphasis is on useful models and interpreting the
solutions in the context of concrete applications.The text is
divided into several parts. The first three chapters deal
exclusively with deterministic models, including linear programming
with sensitivity analysis, integer programming and heuristics, and
network analysis. The next three chapters primarily cover basic
stochastic models and techniques, including decision trees, dynamic
programming, optimal stopping, production planning, and inventory
control. The final five chapters contain more advanced material,
such as discrete-time and continuous-time Markov chains, Markov
decision processes, queueing models, and discrete-event
simulation.Each chapter contains numerous exercises, and a large
selection of exercises includes solutions.
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