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 consists of numerous exercises, and a large selection of exercises includes solutions.Key Features• All essential topics and even more are covered while keeping the size of the book down (competitive textbooks are lengthy at thousand pages, which is overwhelming for beginning students)• LP-sensitivity and post-optimality analysis are presented in an easily understandable manner• Much attention is focused on heuristic solution methods and dynamic optimization• Coverage of more advanced operations research topics, such as Markovian control, inventory and queueing approximations, and networks of queues• A carefully designed collection of motivational examples and problems

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