Operations research originated during World War II with the military's need for a scientific method of providing executives with a quantitative decision-making basis. This text explores strategical kinematics, tactical analysis, gunnery and bombardment problems, more.

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.

Uniquely blends mathematical theory and algorithm design for understanding and modeling real-world problems Optimization modeling and algorithms are key components to problem-solving across various fields of research, from operations research and mathematics to computer science and engineering. Addressing the importance of the algorithm design process. Deterministic Operations Research focuses on the design of solution methods for both continuous and discrete linear optimization problems. The result is a clear-cut resource for understanding three cornerstones of deterministic operations research: modeling real-world problems as linear optimization problem; designing the necessary algorithms to solve these problems; and using mathematical theory to justify algorithmic development. Treating real-world examples as mathematical problems, the author begins with an introduction to operations research and optimization modeling that includes applications form sports scheduling an the airline industry. Subsequent chapters discuss algorithm design for continuous linear optimization problems, covering topics such as convexity. Farkas’ Lemma, and the study of polyhedral before culminating in a discussion of the Simplex Method. The book also addresses linear programming duality theory and its use in algorithm design as well as the Dual Simplex Method. Dantzig-Wolfe decomposition, and a primal-dual interior point algorithm. The final chapters present network optimization and integer programming problems, highlighting various specialized topics including label-correcting algorithms for the shortest path problem, preprocessing and probing in integer programming, lifting of valid inequalities, and branch and cut algorithms. Concepts and approaches are introduced by outlining examples that demonstrate and motivate theoretical concepts. The accessible presentation of advanced ideas makes core aspects easy to understand and encourages readers to understand how to think about the problem, not just what to think. Relevant historical summaries can be found throughout the book, and each chapter is designed as the continuation of the “story” of how to both model and solve optimization problems by using the specific problems-linear and integer programs-as guides. The book’s various examples are accompanied by the appropriate models and calculations, and a related Web site features these models along with MapleTM and MATLAB® content for the discussed calculations. Thoroughly class-tested to ensure a straightforward, hands-on approach, Deterministic Operations Research is an excellent book for operations research of linear optimization courses at the upper-undergraduate and graduate levels. It also serves as an insightful reference for individuals working in the fields of mathematics, engineering, computer science, and operations research who use and design algorithms to solve problem in their everyday work.

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.

Research Methods for Operations Management, second edition is a toolkit of research approaches primarily for advanced students and beginner researchers but also a reference book for any researcher in OM. Many students begin their career in research limited by the one or few approaches taken by their department. The concise, accessible overviews found here equip them with an understanding of a variety of methods and how to use them, enabling them to tailor their research project to their own strengths and goals. The more seasoned researcher will find comprehensive descriptions and analyses on a wide variety of research approaches. This updated and enhanced edition responds to the latest developments in OM, including the growing prominence of services and production of intangible products, and the increasing use of secondary data and of mixed approaches. Alternative research approaches are included and explored to help with the early planning of research. This edition also includes expanded literature review and analysis to guide students towards the next steps in their reading, and more detailed step-by-step advice to tie theory with the researcher’s own practice. Including contributions from an impressive range of the field’s leading thinkers in OM research, this is a guide that no-one embarking on an OM research project should be without.

Research Methods for Operations and Supply Chain Management, third edition, is a toolkit of research approaches primarily for advanced students and beginner researchers, but also a reference book for any researcher in operations and supply chain management (OSCM). Many students begin their careers in research limited by the one or few approaches taken by their department. The concise, accessible overviews found here equip them with an understanding of a variety of methods and how to use them, enabling students to tailor their research project to their own strengths and goals. The more seasoned researcher will find comprehensive descriptions and analyses on a wide variety of research approaches. This updated and enhanced edition responds to the latest developments in OSCM, including the growing prominence of services and production of intangible products, the complete supply chain, and the increasing use of secondary data and of mixed approaches. Alternative research approaches are included and explored to help with the planning of research. This edition also includes expanded literature reviews and analysis to guide students towards the next steps in their reading, and more detailed step-by-step advice to tie theory with the research. Including contributions from an impressive range of the field’s leading thinkers in OSCM research, this is a guide that no one embarking on an OSCM research project should be without. Previous editions of this book were published under the title Research Methods for Operations Management and Researching Operations Management.