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This book offers a comprehensive reference guide to operations research theory and applications in health care systems.

This book offers an introduction to the ripple effect in the supply chain for a broad audience comprising recent developments. The chapters of this handbook are written by leading experts in supply chain risk management and resilience. For the first time, the chapters present in their synergy a multiple-faceted view of the ripple effect in supply chains, while considering organization, optimization, and informatics perspectives. Ripple effect describes the impact of a disruption propagation on supply chain performance, structural designs and operational parameters. The ripple effect manifests when the impact of a disruption cannot be localized and cascades along the supply chain. The resulting structural dynamics can lead to capacity and demand fulfilment downscaling and negatively influence the firm¿s financial and operational performance. The book delineates major features of the ripple effect and methodologies to mitigate the adverse impact of supply chain disruption propagation and to recover in case of severe disruptions. The book provides fresh insights for supply chain management and engineering regarding the following questions: - In what circumstance does one failure cause other failures? - Which structures of the supply chain are especially susceptible to the ripple effect? - What are the typical ripple effect scenarios and what are the most efficient ways to respond them? Distinctive Features: ¿ It considers ripple effect in the supply chain from an multi-disciplinary perspective¿ It offers an introduction to ripple effect mitigation and recovery policies in the framework of disruption risk management in supply chains for a broad audience¿ It integrates management and engineering perspectives on disruption risk management in the supply chain¿ It presents innovative optimization and simulation models for real-life management problems¿ It considers examples from both industrial and service supply chains¿ It reveals decision-making recommendations for tackling disruption risks in the supply chain in proactive and reactive domains.

This book covers a large spectrum of cutting-edge game theory applications in management science in which Professor Georges Zaccour has made significant contributions.

The Economic Order Quantity (EOQ) inventory model first appeared in 1913, and in its centennial, it is still one of the most important inventory models. It is organized into three parts: Part I presents three papers that provide an introduction and review of various EOQ related models.

This Handbook on Semidefinite, Conic and Polynomial Optimization provides the reader with a snapshot of the state-of-the-art in the growing and mutually enriching areas of semidefinite optimization, conic optimization, and polynomial optimization.

The scope of this book is limited to heuristics, metaheuristics, and approximate methods and algorithms as applied to planning and scheduling problems.

This handbook compiles state-of-the-art empirical studies and applications using Data Envelopment Analysis (DEA). It includes a collection of 18 chapters written by DEA experts. Chapter 1 examines the performance of CEOs of U.S. banks and thrifts. Chapter 2 describes the network operational structure of transportation organizations and the relative network data envelopment analysis model. Chapter 3 demonstrates how to use different types of DEA models to compute total-factor energy efficiency scores with an application to energy efficiency. In chapter 4, the authors explore the impact of incorporating customers' willingness to pay for service quality in benchmarking models on cost efficiency of distribution networks, and chapter 5 provides a brief review of previous applications of DEA to the professional baseball industry, followed by two detailed applications to Major League Baseball.Chapter 6 examines efficiency and productivity of U.S. property-liability (P-L) insurers using DEA, while chapter 7 presents a two-stage network DEA model that decomposes the overall efficiency of a decision-making unit into two components. Chapter 8 presents a review of the literature of DEA models for the perfoemance assessment of mutual funds, and chapter 9 discusses the management strategies formulation of the international tourist hotel industry in Taiwan. Chapter 10 presents a novel use of the two-stage network DEA to evaluate sustainable product design performances. In chapter 11 authors highlight limitations of some DEA environmental efficiency models, and chapter 12 reviews applications of DEA in secondary and tertiary education.Chapter 13 measures the relative performance of New York State school districts in the 2011-2012 academic year. Chapter 14 provides an introductory prelude to chapters 15 and 16, which both provide detailed applications of DEA in marketing. Chapter 17 then shows how to decompose a new total factor productivity index that satisfies all economically-relevant axioms from index theory with an application to U.S. agriculture. Finally, chapter 18 presents a unique study that conducts a DEA research front analysis, applying a network clustering method to group the DEA literature over the period 2000 to 2014.

The scope of this volume is primarily to analyze from different methodological perspectives similar valuation and optimization problems arising in financial applications, aimed at facilitating a theoretical and computational integration between methods largely regarded as alternatives.

This book grows from a conference on the state of the art and recent advances in Efficiency and Productivity. Papers were commissioned from leading researchers in the field, and include eight explorations into the analytical foundations of efficiency and productivity analysis. Chapters on modeling advances include reverse directional distance function, a new method for estimating technological production possibilities, a new distance function called a loss distance function, an analysis of productivity and price recovery indices, the relation of technical efficiency measures to productivity measures, the implications for benchmarking and target setting of imposing weight restrictions on DEA models, weight restrictions in a regulatory setting, and the Principle of Least Action.Chapters on empirical applications include a study of innovative firms that use innovation inputs to produce innovation outputs, a study of the impact of potential "coopetition" or cooperation among competitors on the financial performance of European automobile plants, using SFA to estimate the eco-efficiency of dairy farms in Spain, a DEA bankruptcy prediction model, a combined stochastic cost frontier analysis model/mixture hazard model, the evolution of energy intensity in nine Spanish manufacturing industries, and the productivity of US farmers as they age.

This book examines current and emerging challenges in manufacturing related to the ideal of developing production processes with variability and agility on one level of the system, combined with structures ensuring stability and robustness on another level;

This book provides a broad coverage of the recent advances in robustness analysis in decision aiding, optimization, and analytics. It offers a comprehensive illustration of the challenges that robustness raises in different operations research and management science (OR/MS) contexts and the methodologies proposed from multiple perspectives. Aside from covering recent methodological developments, this volume also features applications of robust techniques in engineering and management, thus illustrating the robustness issues raised in real-world problems and their resolution within advances in OR/MS methodologies.Robustness analysis seeks to address issues by promoting solutions, which are acceptable under a wide set of hypotheses, assumptions and estimates. In OR/MS, robustness has been mostly viewed in the context of optimization under uncertainty. Several scholars, however, have emphasized the multiple facets of robustness analysis in a broader OR/MS perspective that goes beyond the traditional framework, seeking to cover the decision support nature of OR/MS methodologies as well. As new challenges emerge in a "big-data'" era, where the information volume, speed of flow, and complexity increase rapidly, and analytics play a fundamental role for strategic and operational decision-making at a global level, robustness issues such as the ones covered in this book become more relevant than ever for providing sound decision support through more powerful analytic tools.

This focuses on the developing field of building probability models with the power of symbolic algebra systems. Computational Probability Applications is comprised of fifteen chapters, each presenting a specific application of computational probability using the APPL modeling and computer language.

This book presents classical Markov Decision Processes (MDP) for real-life applications and optimization. MDP allows users to develop and formally support approximate and simple decision rules, and this book showcases state-of-the-art applications in which MDP was key to the solution approach.

This book focuses on optimal control and systems engineering in the big data era. Part I offers reviews on optimization and control theories, and Part II examines the optimization and control applications.

This book is about elicitation: the facilitation of the quantitative expression of subjective judgement about matters of fact, interacting with subject experts, or about matters of value, interacting with decision makers or stakeholders. It offers an integrated presentation of procedures and processes that allow analysts and experts to think clearly about numbers, particularly the inputs for decision support systems and models. This presentation encompasses research originating in the communities of structured probability elicitation/calibration and multi-criteria decision analysis, often unaware of each other's developments.Chapters 2 through 9 focus on processes to elicit uncertainty from experts, including the Classical Method for aggregating judgements from multiple experts concerning probability distributions; the issue of validation in the Classical Method; the Sheffield elicitation framework; the IDEA protocol; approaches following the Bayesian perspective; the main elements of structured expert processes for dependence elicitation; and how mathematical methods can incorporate correlations between experts.Chapters 10 through 14 focus on processes to elicit preferences from stakeholders or decision makers, including two chapters on problems under uncertainty (utility functions), and three chapters that address elicitation of preferences independently of, or in absence of, any uncertainty elicitation (value functions and ELECTRE).  Two chapters then focus on cross-cutting issues for elicitation of uncertainties and elicitation of preferences: biases and selection of experts.Finally, the last group of chapters illustrates how some of the presented approaches are applied in practice, including a food security case in the UK; expert elicitation in health care decision making; an expert judgement based method to elicit nuclear threat risks in US ports; risk assessment in a pulp and paper manufacturer in the Nordic countries; and elicitation of preferences for crop planning in a Greek region.

The third edition of this handbook is designed to provide a broad coverage of the concepts, implementations, and applications in metaheuristics.

Part I covers recent up-to-date surveys on important areas of districting such as police districting, health care districting, and districting algorithms based on computational geometry.

This book covers a large spectrum of cutting-edge game theory applications in management science in which Professor Georges Zaccour has made significant contributions.

Based on many years of applied research, modeling and educating future decision makers, the authors have selected the critical set of mathematical modeling skills for decision analysis to include in this book.

This book presents 27 methods of the Multiple Attribute Decision Making (MADM), which are not discussed in the existing books, nor studied in details, using more applications. Multiple Criteria Decision Making (MCDM) is sum of the decision making techniques.

This volume chronicles the high impact research career of Harvey Greenberg (1940-2018), and in particular, it reviews historical contributions, presents current research projects, and suggests future pursuits.

This book aims to provide relevant theoretical frameworks and the latest empirical research findings in Internet of Things (IoT) in Management Science and Operations Research. It starts with basic concept and present cases, applications, theory, and potential future. The contributed chapters to the book cover wide array of topics as space permits. Examples are from smart industry; city; transportation; home and smart devices. They present future applications, trends, and potential future of this new discipline. Specifically, this book provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning capabilities of managing IoT. This book deals with the implementation of latest IoT research findings in practice at the global economy level, at networks and organizations, at teams and work groups and, finally, IoT at the level of players in the networked environments.This book is intended for professionals in the field of engineering, information science, mathematics, economics, and researchers who wish to develop new skills in IoT, or who employ the IoT discipline as part of their work. It will improve their understanding of the strategic role of IoT at various levels of the information and knowledge organization. The book is complemented by a second volume of the same editors with practical cases.

Two-person zero-sum game theory deals with situations that are perfectly competitive-there are exactly two decision makers for whom there is no possibility of cooperation or compromise.

The 5th edition of this classic textbook covers the central concepts of practical optimization techniques, with an emphasis on methods that are both state-of-the-art and popular. One major insight is the connection between the purely analytical character of an optimization problem and the behavior of algorithms used to solve that problem. End-of-chapter exercises are provided for all chapters. The material is organized into three separate parts. Part I offers a self-contained introduction to linear programming. The presentation in this part is fairly conventional, covering the main elements of the underlying theory of linear programming, many of the most effective numerical algorithms, and many of its important special applications. Part II, which is independent of Part I, covers the theory of unconstrained optimization, including both derivations of the appropriate optimality conditions and an introduction to basic algorithms. This part of the book explores the general properties of algorithms and defines various notions of convergence. In turn, Part III extends the concepts developed in the second part to constrained optimization problems. Except for a few isolated sections, this part is also independent of Part I. As such, Parts II and III can easily be used without reading Part I and, in fact, the book has been used in this way at many universities. New to this edition are popular topics in data science and machine learning, such as the Markov Decision Process, Farkas¿ lemma, convergence speed analysis, duality theories and applications, various first-order methods, stochastic gradient method, mirror-descent method, Frank-Wolf method, ALM/ADMM method, interior trust-region method for non-convex optimization, distributionally robust optimization, online linear programming, semidefinite programming for sensor-network localization, and infeasibility detection for nonlinear optimization.

This book presents a structured approach to formulate, model, and solve mathematical optimization problems for a wide range of real world situations.

This book covers robust optimization theory and applications in the electricity sector. The advantage of robust optimization with respect to other methodologies for decision making under uncertainty are first discussed. Then, the robust optimization theory is covered in a friendly and tutorial manner. Finally, a number of insightful short- and long-term applications pertaining to the electricity sector are considered.Specifically, the book includes: robust set characterization, robust optimization, adaptive robust optimization, hybrid robust-stochastic optimization, applications to short- and medium-term operations problems in the electricity sector, and applications to long-term investment problems in the electricity sector. Each chapter contains end-of-chapter problems, making it suitable for use as a text. The purpose of the book is to provide a self-contained overview of robust optimization techniques for decision making under uncertainty in the electricity sector. The targeted audience includes industrial and power engineering students and practitioners in energy fields. The young field of robust optimization is reaching maturity in many respects. It is also useful for practitioners, as it provides a number of electricity industry applications described up to working algorithms (in JuliaOpt).

This graduate-level textbook covers modelling, programming and analysis of stochastic computer simulation experiments, including the mathematical and statistical foundations of simulation and why it works.

This book introduces readers to benchmarking techniques in the stochastic environment, primarily stochastic data envelopment analysis (DEA), and provides stochastic models in DEA for the possibility of variations in inputs and outputs.