Bøger i International Series in Operations Research & Management Science serien

This book considers the problem of determining how many barrels of crude oil an oil-producing and exporting country should produce annually for export along with several other important problems that decision-makers in the crude oil industry face and discusses procedures for finding optimum solutions for them.

This book highlights quantitative risk assessment and modeling methods for assessing health risks caused by air pollution, as well as characterizing and communicating remaining uncertainties.

This book provides basic tools for learning how to model in mathematical programming, from models without much complexity to complex system models.

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.

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

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 provides a comprehensive introduction to nonlinear programming, featuring a broad range of applications and solution methods in the field of continuous optimization. It begins with a summary of classical results on unconstrained optimization, followed by a wealth of applications from a diverse mix of fields, e.g.

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.

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 surveys the state-of-the-art in efficiency and productivity analysis, examining advances in the analytical foundations and empirical applications. The analytical techniques developed in this book for efficiency provide alternative ways of defining optimum outcome sets, typically as a (technical) production frontier or as an (economic) cost, revenue or profit frontier, and alternative ways of measuring efficiency relative to an appropriate frontier. Simultaneously, the analytical techniques developed for efficiency analysis extend directly to productivity analysis, thereby providing alternative methods for estimating productivity levels, and productivity change through time or productivity variation across producers.This book includes chapters using data envelopment analysis (DEA) or stochastic frontier analysis (SFA) as quantitative techniques capable of measuring efficiency and productivity. Across the book¿s 15 chapters, it broadly extends into popular application areas including agriculture, banking and finance, and municipal performance, and relatively new application areas including corporate social responsibility, the value of intangible assets, land consolidation, and the measurement of economic well-being. The chapters also cover topics such as permutation tests for production frontier shifts, new indices of total factor productivity, and also randomized controlled trials and production frontiers.

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.

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.

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

In the first volume of the set, Evaluation and Decision Models: A Critical Perspective, the purpose is to provide a critical thinking framework for all individuals utilizing decision and evaluation models, whether it be for research or applications.

Chapter 1. Introduction to scheduling in Industry 4.0 and cloud manufacturing systems.- Chapter 2. Proactive scheduling and reactive real time control in Industry 4.0 .- Chapter 3. Using a digital-twin for production planning and control in Industry 4.0.- Chapter 4. Adaptive Scheduling in the era of Cloud Manufacturing.- Chapter 5. Cloud Material Handling Systems: conceptual model and cloud-based scheduling of handling activities.- Chapter 6. Coupling robust optimization and Model-Checking techniques for robust scheduling in the context of Industry 4.0.- Chapter 7. Integrated scheduling of information services and logistics flows in the omnichannel system.- Chapter 8. Human-oriented assembly line balancing and sequencing model in the Industry 4.0 era.- Chapter 9. A generic decision support tool to planning and assignment problems: Industrial applications & Industry 4.0.- Chapter 10. The Manufacturing Planning and Control system: a journey towards the new perspectives in Industry 4.0 architectures.- Chapter 11. Multi-criteria single batch machine scheduling under time-of-use tariffs.- Chapter 12. Service composition in cloud manufacturing: a DQN-based approach.- Chapter 13. The Tolerance Scheduling Problem in a single machine case.

This book includes a spectrum of concepts, such as performance, productivity, operations research, econometrics, and data science, for the practically and theoretically important areas of ¿productivity analysis/data envelopment analysis¿ and ¿data science/big datä. Data science is defined as the collection of scientific methods, processes, and systems dedicated to extracting knowledge or insights from data and it develops on concepts from various domains, containing mathematics and statistical methods, operations research, machine learning, computer programming, pattern recognition, and data visualisation, among others.Examples of data science techniques include linear and logistic regressions, decision trees, Naïve Bayesian classifier, principal component analysis, neural networks, predictive modelling, deep learning, text analysis, survival analysis, and so on, all of which allow using the data to make more intelligent decisions. On the other hand, it is without a doubt that nowadays the amount of data is exponentially increasing, and analysing large data sets has become a key basis of competition and innovation, underpinning new waves of productivity growth. This book aims to bring a fresh look onto the various ways that data science techniques could unleash value and drive productivity from these mountains of data.Researchers working in productivity analysis/data envelopment analysis will benefit from learning about the tools available in data science/big data that can be used in their current research analyses and endeavours. The data scientists, on the other hand, will also get benefit from learning about the plethora of applications available in productivity analysis/data envelopment analysis.

This includes the concept of nondominance, Simon's bounded rationality, Tversky and Kahneman's prospect theory, and the concepts of behavioral vs. 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.

This book analyzes the security of critical infrastructures such as road, rail, water, health, and electricity networks that are vital for a nation's society and economy, and assesses the resilience of these networks to intentional attacks.

This updated edition expands and illuminates the connection between the purely analytical character of an optimization problem, expressed by properties of the necessary conditions, and the behavior of algorithms used to solve a problem.

This book considers the problem of determining how many barrels of crude oil an oil-producing and exporting country should produce annually for export along with several other important problems that decision-makers in the crude oil industry face and discusses procedures for finding optimum solutions for them.

This book presents the methodology and applications of Data Envelopment Analysis (DEA) in measuring productivity, efficiency and effectiveness in Financial Services firms such as banks, bank branches, stock markets, pension funds, mutual funds, insurance firms, credit unions, risk tolerance, and corporate failure prediction.

This book provides a comprehensive overview of the most important and frequently considered optimization problems concerning cutting and packing.

This book explores a new framework of Adaptive Water Management (AWM) for evaluating existing approaches in urban water management.

This book is an expository introduction to the methodology of sensitivity analysis of model output.

This textbook on Linear and Nonlinear Optimization is intended for graduate and advanced undergraduate students in operations research and related fields.