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 uses quantitative models to analyze and predict the behavior of systems and to provide information for decision makers. Two key concepts in operations research are optimization and uncertainty. This volume consists of a collection of peer reviewed papers from the International Workshop on Recent Advances in Stochastic Operations Research (RASOR 2005), August 25OCo26, 2005, Canmore, Alberta, Canada. In particular, the book focusses on models in stochastic operations research, including queueing models, inventory models, financial engineering models, reliability models, and simulations models."

The study of operations research arose during World War II to enhance the effectiveness of weapons and equipment used on the battlefield. Since then, operations research techniques have also been used to solve several sophisticated and complex defense-related problems. Operations Research for Military Organizations is a critical scholarly resource that examines the issues that have an impact on aspects of contemporary quantitative applications of operations research methods in the military. It also addresses innovative applications, techniques, and methodologies to assist in solving defense and military-related problems. Featuring coverage on a broad range of topics such as combat planning, tactical decision aids, and weapon system simulations, this book is geared towards defense contractors, military consultants, military personnel, policy makers, and government departments seeking current research on defense methodologies.

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.

Operations Research (OR) emerged in an effort to improve the effectiveness of newly inducted weapons and equipment during World War II. While rapid growth ofOR led to its becoming an important aid to decision making in all sectors including defense, its contribution in defense remained largely confined to classified reports. Very few books dealing with applications of quantitative decision making techniques in military have been published presumably due to limited availability ofrelevant information. The situation changed rapidly during the last few years. The recognition of the subject of Military Operations Research (MOR) gave tremendous boost to its development. Books and journals on MOR started appearing. The number of sessions on MOR at national and international conferences also registered an increase. The volume of teaching, training and research activities in the field of MOR at military schools and non-military schools enhanced considerably. Military executives and commanders started taking increasing interest in getting scientific answers to questions pertaining to weapon acquisition, threat perception and quantification, assessment of damage or casualties, evaluation of chance of winning a battle, force mix, deployment and targeting of weapons against enemy targets, war games and scenario evaluation. Most of these problems were being tackled on the basis of intuition, judgment and experience or analysis under very simple assumptions. In an increasingly sophisticated and complex defense scenario resulting in advances in equipment and communications, the need for supplementing these practices by scientific research in MOR became imperative.

Product proliferation has become a common phenomenon. Most companies now offer hundreds, if not thousands, of stock keeping units (SKUs) in order to compete in the market place. Companies with expanding product and service varieties face with problems of obtaining accurate demand forecasts, controlling production and inventory costs, and providing high quality and good delivery performance for the customers. Marketing managers often advocate widening product lines for increasing revenue and market share. However, the breadth of product line can also decrease the efficiency of manufacturing processes and distribution systems. Thus firms must weigh the benefits of product variety against its cost in order to determine the optimal level of product variety to offer to their customers. Academics and practitioners are interested in several fundamental questions about product variety. For instance, why do companies extend their product lines? Do consumers care about product variety? Will a brand with more variety enjoy higher market share? How should product variety be measured? How can a company exploit its product and process design to deliver a higher level of product variety quickly and cheaply? What should the level of product variety be and what should the price of each of the product variants be? What kind of 'challenges would a company face in offering a high level of product variety and how can these obstacles be overcome? The solutions to these questions span multiple functions and disciplines.