This is a book about how management and control decisions are made by persons who collaborate and possibly use the support of an information system. The decision is the result of human conscious activities aiming at choosing a course of action for attaining a certain objective (or a set of objectives). The act of collaboration implies that several entities who work together and share responsibilities to jointly plan, implement and evaluate a program of activities to achieve the common goals. The book is intended to present a balanced view of the domain to include both well-established concepts and a selection of new results in the domains of methods and key technologies. It is meant to answer several questions, such as: a) "How are evolving the business models towards the ever more collaborative schemes?"; b) "What is the role of the decision-maker in the new context?" c) "What are the basic attributes and trends in the domain of decision-supporting information systems?"; d) "Which are the basic methods to aggregate the individual preferences?" e)"What is the impact of modern information and communication technologies on the design and usage of decision support systems for groups of people?".

This is the first book presenting a broad overview of parallelism in constraint-based reasoning formalisms. In recent years, an increasing number of contributions have been made on scaling constraint reasoning thanks to parallel architectures. The goal in this book is to overview these achievements in a concise way, assuming the reader is familiar with the classical, sequential background. It presents work demonstrating the use of multiple resources from single machine multi-core and GPU-based computations to very large scale distributed execution platforms up to 80,000 processing units. The contributions in the book cover the most important and recent contributions in parallel propositional satisfiability (SAT), maximum satisfiability (MaxSAT), quantified Boolean formulas (QBF), satisfiability modulo theory (SMT), theorem proving (TP), answer set programming (ASP), mixed integer linear programming (MILP), constraint programming (CP), stochastic local search (SLS), optimal path finding with A*, model checking for linear-time temporal logic (MC/LTL), binary decision diagrams (BDD), and model-based diagnosis (MBD). The book is suitable for researchers, graduate students, advanced undergraduates, and practitioners who wish to learn about the state of the art in parallel constraint reasoning.

Risk is of fundamental importance in this era of the global economy. Supply chains must into account the uncertainty of demand. Moreover, the risk of uncertain demand can cut two ways: (1) there is the risk that unexpected demand will not be met on time, and the reverse problem (2) the risk that demand is over estimated and excessive inventory costs are incurred. There are other risks in unreliable vendors, delayed shipments, natural disasters, etc. In short, there are a host of strategic, tactical and operational risks to business supply chains. Supply Chain Risk: A Handbook of Assessment, Management, and Performance will focus on how to assess, evaluate, and control these various risks.

This contributed volume presents a collection of materials on supply chain management including industry-based case studies addressing petrochemical, pharmaceutical, manufacturing and reverse logistics topics. Moreover, the book covers sustainability issues, as well as optimization approaches. The target audience comprises academics, industry managers, and practitioners in the field of supply chain management, being the book also beneficial for graduate students

This book presents models and algorithms for complex scheduling problems. Besides resource-constrained project scheduling problems with applications also job-shop problems with flexible machines, transportation or limited buffers are discussed. Discrete optimization methods like linear and integer programming, constraint propagation techniques, shortest path and network flow algorithms, branch-and-bound methods, local search and genetic algorithms, and dynamic programming are presented. They are used in exact or heuristic procedures to solve the introduced complex scheduling problems. Furthermore, methods for calculating lower bounds are described. Most algorithms are formulated in detail and illustrated with examples. In this second edition some errors were corrected, some parts were explained in more detail, and new material has been added. In particular, further generalizations of the RCPSP, additional practical applications and some more algorithms were integrated.

Rubinstein is the pioneer of the well-known score function and cross-entropy methods.

Accessible to a broad audience of engineers, computer scientists, mathematicians, statisticians and in general anyone, theorist and practitioner, who is interested in smart simulation, fast optimization, learning algorithms, and image processing.

This book highlights the use of an outcome-oriented view of performance to frame and assess the desirability of the effects produced by adopted policies, so to allow governments not only to consider effects in the short, but also the long run. Furthermore, it does not only focus on policy from the perspective of a single unit or institution, but also under an inter-institutional viewpoint. This book features theoretical and empirical research on how public organizations have evolved their performance management systems toward outcome measures that may allow one to better deal with wicked problems. Today, 'wicked problems' characterize most of governmental planning involving social issues. These are complex policy problems, underlying high risk and uncertainty, and a high interdependency among variables affecting them. Such problems cannot be clustered within the boundaries of a single organization, or referred to specific administrative levels or ministries. They are characterized by dynamic complexity, involving multi-level, multi-actor and multi-sectoral challenges. In the last decade, a number of countries have started to develop new approaches that may enable to improve cohesion, to effectively deal with wicked problems. The chapters in this book showcase these approaches, which encourage the adoption of more flexible and pervasive governmental systems to overcome such complex problems. Outcome-Based Performance Management in the Public Sector is divided into five parts. Part 1 aims at shedding light on problems and issues implied in the design and implementation of "outcome-based" performance management systems in the public sector. Then Part 2 illustrates the experiences, problems, and evolving trends in three different countries (Scotland, USA, and Italy) towards the adoption of outcome-based performance management systems in the public sector. Such analyses are conducted at both the national and local government levels. The third part of the book frames how outcome-based performance management can enhance public governance and inter-institutional coordination. Part 4 deals with the illustration of challenges and results from different public sector domains. Finally the book concludes in Part 5 as it examines innovative methods and tools that may support decision makers in dealing with the challenges of outcome-based performance management in the public sector. Though the book is specifically focused on a research target, it will also be useful to practitioners and master students in public administration .

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.

Applied Stochastic Models and Control for Finance and Insurance presents at an introductory level some essential stochastic models applied in economics, finance and insurance. Markov chains, random walks, stochastic differential equations and other stochastic processes are used throughout the book and systematically applied to economic and financial applications. In addition, a dynamic programming framework is used to deal with some basic optimization problems. The book begins by introducing problems of economics, finance and insurance which involve time, uncertainty and risk. A number of cases are treated in detail, spanning risk management, volatility, memory, the time structure of preferences, interest rates and yields, etc. The second and third chapters provide an introduction to stochastic models and their application. Stochastic differential equations and stochastic calculus are presented in an intuitive manner, and numerous applications and exercises are used to facilitate their understanding and their use in Chapter 3. A number of other processes which are increasingly used in finance and insurance are introduced in Chapter 4. In the fifth chapter, ARCH and GARCH models are presented and their application to modeling volatility is emphasized. An outline of decision-making procedures is presented in Chapter 6. Furthermore, we also introduce the essentials of stochastic dynamic programming and control, and provide first steps for the student who seeks to apply these techniques. Finally, in Chapter 7, numerical techniques and approximations to stochastic processes are examined. This book can be used in business, economics, financial engineering and decision sciences schools for second year Master's students, as well as in a number of courses widely given in departments of statistics, systems and decision sciences.

This book offers a new perspective on human decision-making by comparing the established methods in decision science with innovative modelling at the level of neurons and neural interactions. The book presents a new generation of computer models, which can predict with astonishing accuracy individual economic choices when people make them by quick intuition rather than by effort. A vision for a new kind of social science is outlined, whereby neural models of emotion and cognition capture the dynamics of socioeconomic systems and virtual social networks. The exposition is approachable by experts as well as by advanced students. The author is an Associate Professor of Decision Science with a doctorate in Computational Neuroscience, and a former software consultant to banks in the City of London.

This volume collects research papers addressing topical issues in economics and management with a particular focus on dynamic models which allow to analyze and foster the decision making of firms in dynamic complex environments. The scope of the contributions ranges from daily operational challenges firms face to strategic choices in dynamic industry environments and the analysis of optimal growth paths. The volume also highlights recent methodological developments in the areas of dynamic optimization, dynamic games and meta-heuristics, which help to improve our understanding of (optimal) decision making in a fast evolving economy.

Computer-Integrated Manufacturing has gained recognition as a most effective tool in increasing manufacturing competitiveness. This book discusses the fundamental knowledge of Computer-Aided Process Planning, the key to integrated manufacturing.

The work provides a rigorous basis for the understanding of process planning and the development of effective and efficient Computer-Aided Process Planning systems. It presents a current overview on the latest issues and directions in this field. Topics covered include Group Technology, process planning algorithms, artificial intelligence, and assembly process planning. Most importantly, in order to provide a solid foundation for discussion and development, basic theories and principles for process planning are comprehensively and thoroughly studied in this book.

Written for those who wish to understand the theory of process planning, the book will also prove an indispensable reference source on the approaches, methodologies and tools which can be applied to day-to-day process planning operations.

This book includes case studies that examine the application of operations research to improve or increase efficiency in industry and operational activities. This collection of "living case studies" is all based on the author's 30-year career of consulting and advisory work. These true-to life industrial applications illustrate the research and development of solutions, as well as potential implementation and integration problems that may occur when adopting these methods into a business. Among the topics covered in the chapters include optimization in circuit board manufacturing, Decision Support System (DSS) for plant loading and dispatch planning, as well as development of important test procedures for tyre and pharma industry with shelf life constraints. In particular, the study on deckle optimization should be of great help to managers in paper industry and consultants for development of deckle optimization software. The application of operations research throughout the industry makes it an ideal guide for industrial executives, professionals and practitioners responsible for quality and productivity improvement.

A. Dogramaci and N.R. Adam Productivity of a firm is influenced both by economic forces which act at the macro level and impose themselves on the individual firm as well as internal factors that result from decisions and processes which take place within the boundaries of the firm. Efforts towards increasing the produc tivity level of firms need to be based on a sound understanding of how the above processes take place. Our objective in this volume is to present some of the recent research work in this field. The volume consists of three parts. In part I, two macro issues are addressed (taxation and inflation) and their relation to produc tivity is analyzed. The second part of the volume focuses on methods for productivity analysis within the firm. Finally, the third part of the book deals with two additional productivity analysis techniques and their applications to public utilities. The objective of the volume is not to present a unified point of view, but rather to cover a sample of different methodologies and perspectives through original, scholarly papers."

Each concept is discussed from the basics and supported by sufficient mathematical background and worked examples.

Suitable for individual or group learning, the book offers numerous end-of-chapter problems for study and review.

The book includes the latest research advances and cutting-edge analyses of real case studies in the disciplines of Industrial Engineering and Operations Management from diverse international contexts. This work presents a revised version of the best papers presented at the XIII International Conference on Industrial Engineering and Industrial Management promoted by ADINGOR (Asociacion para el Desarrollo de la Ingenieria de Organizacion), which took place at the Polytechnic School of Engineering of Gijon (University of Oviedo), Asturias, Spain, from July 11th to 12th, 2019.

The importance of having ef cient and effective methods for data mining and kn- ledge discovery (DM&KD), to which the present book is devoted, grows every day and numerous such methods have been developed in recent decades. There exists a great variety of different settings for the main problem studied by data mining and knowledge discovery, and it seems that a very popular one is formulated in terms of binary attributes. In this setting, states of nature of the application area under consideration are described by Boolean vectors de ned on some attributes. That is, by data points de ned in the Boolean space of the attributes. It is postulated that there exists a partition of this space into two classes, which should be inferred as patterns on the attributes when only several data points are known, the so-called positive and negative training examples. The main problem in DM&KD is de ned as nding rules for recognizing (cl- sifying) new data points of unknown class, i. e. , deciding which of them are positive and which are negative. In other words, to infer the binary value of one more attribute, called the goal or class attribute. To solve this problem, some methods have been suggested which construct a Boolean function separating the two given sets of positive and negative training data points.

This new edition of Stochastic Linear Programming: Models, Theory and Computation has been brought completely up to date, either dealing with or at least referring to new material on models and methods, including DEA with stochastic outputs modeled via constraints on special risk functions (generalizing chance constraints, ICC's and CVaR constraints), material on Sharpe-ratio, and Asset Liability Management models involving CVaR in a multi-stage setup. To facilitate use as a text, exercises are included throughout the book, and web access is provided to a student version of the authors' SLP-IOR software. Additionally, the authors have updated the Guide to Available Software, and they have included newer algorithms and modeling systems for SLP. The book is thus suitable as a text for advanced courses in stochastic optimization, and as a reference to the field. From Reviews of the First Edition: "The book presents a comprehensive study of stochastic linear optimization problems and their applications. ... The presentation includes geometric interpretation, linear programming duality, and the simplex method in its primal and dual forms. ... The authors have made an effort to collect ... the most useful recent ideas and algorithms in this area. ... A guide to the existing software is included as well." (Darinka Dentcheva, Mathematical Reviews, Issue 2006 c) "This is a graduate text in optimisation whose main emphasis is in stochastic programming. The book is clearly written. ... This is a good book for providing mathematicians, economists and engineers with an almost complete start up information for working in the field. I heartily welcome its publication. ... It is evident that this book will constitute an obligatory reference source for the specialists of the field." (Carlos Narciso Bouza Herrera, Zentralblatt MATH, Vol. 1104 (6), 2007)

This book offers the first comprehensive and critical literature review of fuzzy pairwise comparison methods derived from methods originally developed for crisp pairwise comparison matrices. It proposes new fuzzy extensions of these methods and provides a detailed study of the differences and analogies between all the reviewed methods, as well as a detailed description of their drawbacks, with the help of many numerical examples. In order to prevent the drawbacks related to the reviewed fuzzy pairwise comparison methods, the book introduces constrained fuzzy arithmetic in fuzzy extension of the pairwise comparison methods. It proposes new fuzzy pairwise comparison methods based on constrained fuzzy arithmetic and critically compares them with the reviewed methods. It describes the application of the newly developed methods to incomplete large-dimensional pairwise comparison matrices showcased in a real-life case study. Written for researchers, graduate and PhD students interested in multi-criteria decision making methods based on both crisp and fuzzy pairwise comparison matrices, this self-contained book offers an overview of cutting-edge research and all necessary information to understand the described tools and use them in real-world applications.

Data mining is the process of extracting hidden patterns from data, and it's commonly used in business, bioinformatics, counter-terrorism, and, increasingly, in professional sports. First popularized in Michael Lewis' best-selling Moneyball: The Art of Winning An Unfair Game, it is has become an intrinsic part of all professional sports the world over, from baseball to cricket to soccer. While an industry has developed based on statistical analysis services for any given sport, or even for betting behavior analysis on these sports, no research-level book has considered the subject in any detail until now. Sports Data Mining brings together in one place the state of the art as it concerns an international array of sports: baseball, football, basketball, soccer, greyhound racing are all covered, and the authors (including Hsinchun Chen, one of the most esteemed and well-known experts in data mining in the world) present the latest research, developments, software available, and applications for each sport. They even examine the hidden patterns in gaming and wagering, along with the most common systems for wager analysis.

Drawn from a conference honoring Gerald L. Thompson, the pioneer of operations research, this volume brings together some of the latest writings of major figures in the field. The volume is divided into four parts: the first part reviews the career and significance of Thompson, the second concentrates on linear and nonlinear optimization, the third looks at network and integer programming, and the fourth provides examples of applications-oriented research in manufacturing.

This volume will be an invaluable resource for all scholars and researchers involved in theory and methodology in operations research and management science.

The fields of similarity and preference are still broadening due to the exploration of new fields of application. This is caused by the strong impact of vagueness, imprecision, uncertainty and dominance on human and agent information, communication, planning, decision, action, and control as well as by the technical progress of the information technology itself. The topics treated in this book are of interest to computer scientists, statisticians, operations researchers, experts in AI, cognitive psychologists and economists.

1. 1. Motivation This book is based on the view-tx)int that both public and private decision making, in practice, can often be ilrproved upon by means of fonnal (nonnative) decision nodels and methods. To sane extent, the validity of this statement can be measured by the irrpressive number of su=esses of disciplines as operations research and management science. Hcwever, as witnessed by the many discussions in the professional journals in these fields, many rrodels and methods do not completely meet the requirements of decision making in prac- tice. Of all possible origins of these clear shortcomings, we main-* ly focus on only one: the fact that nost of these nodels and methods are unsuitable for decision situations in which multiple and possi- bly conflicting objectives playa role, because they are concentra- ted on the (optimal) fulfilment of only one objective. The need to account for multiple goals was observed relatively early. Hoffman [1955], while describing 'what seem to be the prin- cipal areas (in linear prograrrrning) where new ideas and new methods are needed' gives an exanple with conflicting goals. In this pro- blem, the assignrrent of relative weights is a great problem for the planning staff and is 'probably not the province of the mathemati- cian engaged in solving this problem'. These remarks were true pre- cursors of later develor:nents. Nevertheless, the need for methods dealing with multiple goals was not widely recognized until much later.

The Cloud Computing and Services Science book comprises a collection of the best papers presented at the International Conference on Cloud Computing and Services Science (CLOSER), which was held in The Netherlands in May 2011. In netting papers from the conference researchers and experts from all over the world explore a wide-ranging variety of the emerging Cloud Computing platforms, models, applications and enabling technologies. Further, in several papers the authors exemplify essential links to Services Science as service development abstraction, service innovation, and service engineering, acknowledging the service-orientation in most current IT-driven structures in the Cloud.

The Cloud Computing and Services Science book is organized around important dimensions of technology trends in the domain of cloud computing in relation to a broad scientific understanding of modern services emerging from services science. The papers of this book are inspired by scholarly and practical work on the latest advances related to cloud infrastructure, operations, security, services, and management through the global network. This book includes several features that will be helpful, interesting, and inspirational to students, researchers as well as practitioners. Professionals and decision makers working in this field will also benefit from this book