Introduction To The Mathematics Of Operations Research With Mathematicar

The breadth of information about operations research and the overwhelming size of previous sources on the subject make it a difficult topic for non-specialists to grasp. Fortunately, Introduction to the Mathematics of Operations Research with Mathematica®, Second Edition delivers a concise analysis that benefits professionals in operations research and related fields in statistics, management, applied mathematics, and finance. The second edition retains the character of the earlier version, while incorporating developments in the sphere of operations research, technology, and mathematics pedagogy. Covering the topics crucial to applied mathematics, it examines graph theory, linear programming, stochastic processes, and dynamic programming. This self-contained text includes an accompanying electronic version and a package of useful commands. The electronic version is in the form of Mathematica notebooks, enabling you to devise, edit, and execute/reexecute commands, increasing your level of comprehension and problem-solving. Mathematica sharpens the impact of this book by allowing you to conveniently carry out graph algorithms, experiment with large powers of adjacency matrices in order to check the path counting theorem and Markov chains, construct feasible regions of linear programming problems, and use the "dictionary" method to solve these problems. You can also create simulators for Markov chains, Poisson processes, and Brownian motions in Mathematica, increasing your understanding of the defining conditions of these processes. Among many other benefits, Mathematica also promotes recursive solutions for problems related to first passage times and absorption probabilities.

Nice text for advanced undergraduates and beginning graduate students provides review, from the viewpoint of operations research and its (diverse) applications, of graph theory, linear programming, stochastic processes and dynamic programming, and of related material. The seven chapters are punctu

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.

This book on Operation Research has been specially written to meet the requirements of the M.Sc., M.Com and M.B.A. students. The subject matter has been discussed in such a simple way that the students will find no difficulty to understand it. The proof of various theorems and examples has been given with minute details. Each chapter of this book contains complete theory and fairly large number of solved examples, sufficient problems have also been selected from various universities examination papers. Contents: Introduction to Operation Research, Integer Programming, Dual Problem, Goal Programming, Sequencing Problem.

Introduction to Operations Research

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.

The first graduate-level text devoted to the subject, this classic offers a concise history and overview of methods as well as an excellent exposition of the mathematical foundations underlying classical operations research procedures. It begins with a review of historical, scientific, and mathematical aspects; examples and ideas related to classical methods of forming models introduce discussions of optimization, game theory, applications of probability, and queuing theory. Carefully selected exercises illustrate important and useful ideas. This text is an ideal introduction for students to the basic mathematics of operations research as well as a valuable source of references to early literature on operations research. 1959 edition.

"Although this textbook is intended for use in a two-semester sequence of courses introducing the mathematical methods of operations research, Part I can also be used alone for a one-semester course on linear programming. The authors have chosen to provide deep and thorough coverage of the most important methods in operations research, rather than a superficial treatment of a larger number of topics. The level of exposition is appropriate for juniors and seniors who are majoring in engineering, computer science, mathematics, and quantitative methods in management. A solutions manual is available to qualified instructors."

Operations research encompasses a wide range of problem-solving techniques and methods applied in the pursuit of improved decision-making and efficiency. Some of the tools used by operations researchers are statistics, optimization, probability theory, queuing theory, game theory, graph theory, decision analysis, mathematical modeling and simulation. An Information System is any combination of information technology and people's activities using that technology to support operations, management, and decision-making. In a very broad sense, the term information system is frequently used to refer to the interaction between people, algorithmic processes, data and technology. Operations Research is the scientific study of logistic networks to provide for decision support at all levels in order to optimize production and distribution of the commodity flows. Nowadays, these logistic networks have become very large and may range over several countries, while the demands for quality of service have grown similarly to ever higher standards. Generally one agrees that to maintain such large networks successfully, one needs the control of all the information flows through the network, that is, continuous information on the status of the resources. Operations research is an interdisciplinary branch of applied mathematics and formal science that uses advanced analytical methods such as mathematical modeling, statistical analysis, and mathematical optimization to arrive at optimal or near-optimal solutions to complex decision-making problems. It is often concerned with determining the maximum or minimum of some real-world objective. The book of operations management features the latest concepts and applications while not losing focus on the core concepts that has made this text a market leader.