International Series in Operations Research & Management Science

Linear programming (LP), modeling, and optimization are very much the fundamentals of OR, and no academic program is complete without them. No matter how highly developed one’s LP skills are, however, if a fine appreciation for modeling isn’t developed to make the best use of those skills, then the truly ‘best solutions’ are often not realized, and efforts go wasted. Katta Murty studied LP with George Dantzig, the father of linear programming, and has written the graduate-level solution to that problem. While maintaining the rigorous LP instruction required, Murty's new book is unique in his focus on developing modeling skills to support valid decision making for complex real world problems. He describes the approach as 'intelligent modeling and decision making' to emphasize the importance of employing the best expression of actual problems and then applying the most computationally effective and efficient solution technique for that model.

One of the primary purposes of any model is to build intuition and generate insights. Typically, a model is developed to be able to better understand phenomena that are otherwise difficult to comprehend. Models can also help in verifying the correctness of an intuition or judgment. In spite of the fact that many educators and practitioners recognize the intuition-building power of simple models, this is the first book in the field that uses the power of the basic models and principles to provide students and managers with an “intuitive understanding” of operations management. Building Intuition: Insights From Basic Operations Management Models and Principles touches on nine fundamental models and principles, and outlines the key insights behind each one. To ensure that the volume is accurate, authoritative, accessible and well-written, the chapters are developed by leading experts in each chapter area and written with the students and managers in mind. The book’s primary purpose is to motivate and to enable readers to develop insights with respect to a number of models that are central to the study and practice of operations management.  

Practical Goal Programming is intended to allow academics and practitioners to be able to build effective goal programming models,  to detail the current state of the art, and to lay the foundation for its future development and continued application to new and varied fields.  Suitable as both a text and reference, its nine chapters first provide a brief history, fundamental definitions, and underlying philosophies, and then detail the goal programming variants and define them algebraically.  Chapter 3 details the step-by-step formulation of the basic goal programming model, and Chapter 4 explores more advanced modeling issues and highlights some recently proposed extensions. Chapter 5 then details the solution methodologies of goal programming, concentrating on computerized solution by the Excel Solver and LINGO packages for each of the three main variants, and  includes a discussion of the viability of the use of specialized goal programming packages.  Chapter 6 discusses the linkages between Pareto Efficiency and goal programming.  Chapters 3 to 6 are supported by a set of ten exercises, and an Excel spreadsheet giving the basic solution of each example is available at an accompanying website. Chapter 7 details the current state of the art in terms of the integration of goal programming with other techniques, and the text concludes with two case studies which were chosen to demonstrate the application of goal programming in practice and to illustrate the principles developed in Chapters 1 to 7.  Chapter 8 details an application in healthcare, and Chapter 9 describes applications in portfolio selection.

This Fourth Edition introduces the latest theory and applications in optimization. It emphasizes constrained optimization, beginning with a substantial treatment of linear programming and then proceeding to convex analysis, network flows, integer programming, quadratic programming, and convex optimization. Readers will discover a host of practical business applications as well as non-business applications. Topics are clearly developed with many numerical examples worked out in detail. Specific examples and concrete algorithms precede more abstract topics. With its focus on solving practical problems, the book features free C programs to implement the major algorithms covered, including the two-phase simplex method, primal-dual simplex method, path-following interior-point method, and homogeneous self-dual methods. In addition, the author provides online JAVA applets that illustrate various pivot rules and variants of the simplex method, both for linear programming and for network flows. These C programs and JAVA tools can be found on the book's website. The website also includes new online instructional tools and exercises.

This book promotes the use of mathematical optimization and operations research methods in rail transportation.  The editors assembled thirteen contributions from leading scholars to present a unified voice, standardize terminology, and assess the state-of-the-art. There are three main clusters of articles, corresponding to the classical stages of the planning process: strategic, tactical, and operational.  These three clusters are further subdivided into five parts which correspond to the main phases of the railway network planning process: network assessment, capacity planning, timetabling, resource planning, and operational planning.  Individual chapters cover: Simulation Capacity Assessment Network Design Train Routing Robust Timetabling Event Scheduling Track Allocation Blocking Shunting Rolling Stock Crew Scheduling Dispatching Delay Propagation

This book offers a comprehensive introduction to decision-making in an MCDM framework. Designed as a tutorial, it presents the main concepts and methods to be applied, together with essential background information. This includes the concept of nondominance, Simon’s bounded rationality, Tversky and Kahneman’s prospect theory, and the concepts of behavioral vs. mathematical convergence and premature stopping put forward by Korhonen, Moskowitz and Wallenius. The book concludes with a non-technical review of many popular decision algorithms, including the Analytic Hierarchy Process (AHP), VIMDA, and a number of classic interactive man-machine algorithms. In essence, the book is a “one-stop” source on everything you need to know about managerial decision-making in the multiple-criteria setting.

This book introduces readers to essential strategies, practices, and benchmarking for asset maintenance in operations intensive industries. Drawing on a case study from the oil and gas sector, it offers a methodology and practical solutions to help maintenance practitioners select and formulate an asset maintenance strategy, and to establish best maintenance practices at an organizational level using the frameworks developed here. It is intended for industry practitioners, young maintenance professionals, and students of engineering management who aspire to a career in operations intensive industries.

This book examines application and methods to incorporating stochastic parameter variations into the optimization process to decrease expense in corrective measures. Basic types of deterministic substitute problems occurring mostly in practice involve i) minimization of the expected primary costs subject to expected recourse cost constraints (reliability constraints) and remaining deterministic constraints, e.g. box constraints, as well as ii) minimization of the expected total costs (costs of construction, design, recourse costs, etc.) subject to the remaining deterministic constraints. After an introduction into the theory of dynamic control systems with random parameters, the major control laws are described, as open-loop control, closed-loop, feedback control and open-loop feedback control, used for iterative construction of feedback controls. For approximate solution of optimization and control problems with random parameters and involving expected cost/loss-type objective,constraint functions, Taylor expansion procedures, and Homotopy methods are considered, Examples and applications to stochastic optimization of regulators are given. Moreover, for reliability-based analysis and optimal design problems, corresponding optimization-based limit state functions are constructed. Because of the complexity of concrete optimization/control problems and their lack of the mathematical regularity as required of Mathematical Programming (MP) techniques, other optimization techniques, like random search methods (RSM) became increasingly important. Basic results on the convergence and convergence rates of random search methods are presented. Moreover, for the improvement of the – sometimes very low – convergence rate of RSM, search methods based on optimal stochastic decision processes are presented. In order to improve the convergence behavior of RSM, the random search procedure is embedded into a stochastic decision process for an optimal control ofthe probability distributions of the search variates (mutation random variables).

This book provides a guide to businesses on how to use analytics to help drive from ideas to execution. Analytics used in this way provides “full lifecycle support” for business and helps during all stages of management decision-making and execution. The framework presented in the book enables the effective interplay of business, analytics, and information technology (business intelligence) both to leverage analytics for competitive advantage and to embed the use of business analytics into the business culture. It lays out an approach for analytics, describes the processes used, and provides guidance on how to scale analytics and how to develop analytics teams. It provides tools to improve analytics in a broad range of business situations, regardless of the level of maturity and the degree of executive sponsorship provided. As a guide for practitioners and managers, the book will benefit people who work in analytics teams, the managers and leaders who manage, use and sponsor analytics, and those who work with and support business analytics teams.