Programming And Probability Models In Operations Research

Programming and Probability Models in Operations Research

Operations Research: Introduction to Models and Methods

"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"--

Introduction to Stochastic Programming

This rapidly developing field encompasses many disciplines including operations research, mathematics, and probability. Conversely, it is being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors present a broad overview of the main themes and methods of the subject, thus helping students develop an intuition for how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems. The early chapters introduce some worked examples of stochastic programming, demonstrate how a stochastic model is formally built, develop the properties of stochastic programs and the basic solution techniques used to solve them. The book then goes on to cover approximation and sampling techniques and is rounded off by an in-depth case study. A well-paced and wide-ranging introduction to this subject.