This book covers several new areas in the growing field of analytics with some innovative applications in different business contexts, and consists of selected presentations at the 6th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence. The book is conceptually divided in seven parts. The first part gives expository briefs on some topics of current academic and practitioner interests, such as data streams, binary prediction and reliability shock models. In the second part, the contributions look at artificial intelligence applications with chapters related to explainable AI, personalized search and recommendation, and customer retention management. The third part deals with credit risk analytics, with chapters on optimization of credit limits and mitigation of agricultural lending risks. In its fourth part, the book explores analytics and data mining in the retail context. In the fifth part, the book presents some applications of analytics to operations management. This part has chapters related to improvement of furnace operations, forecasting food indices and analytics for improving student learning outcomes. The sixth part has contributions related to adaptive designs in clinical trials, stochastic comparisons of systems with heterogeneous components and stacking of models. The seventh and final part contains chapters related to finance and economics topics, such as role of infrastructure and taxation on economic growth of countries and connectedness of markets with heterogenous agents, The different themes ensure that the book would be of great value to practitioners, post-graduate students, research scholars and faculty teaching advanced business analytics courses.

When predicting the future of air traffic development, it is imperative for researchers and planners to have the most accurate information about airport capacity constraints. Airport capacity constraints and strategies for mitigation: A global perspective analyses airport capacity constraints with empirical methods that forecast future capacities and capacity shortfalls. The book discusses in detail the importance of airport capacity constraints on air traffic development, especially for international hubs, along with mitigation strategies for already congested airports. It analyses empirical data to provide greater insight into the problems of airport congestion and capacity shortage. The authors present detailed global traffic forecasts for the years 2030 and 2040, and mitigation strategies for overcoming the problem of limited airport capacity. As expanding current airports becomes increasingly difficult, and time consuming - especially for hubs - the study of current and future airport capacity constraints becomes ever more needed. This book provides detailed information about how to correctly assess and quantify the problem of limited airport capacity, while offering strategies for overcoming these issues for a healthy global air traffic network.

This book provides comprehensive coverage of the latest research on multiple criteria research analysis (MCDA) and related areas, gathering a collection of high-quality chapters prepared by leading scholars in the field. By covering the established streams in MCDA research and simultaneously exploring new and emerging areas of application, it offers a unique reference resource for the future development of MCDA. The book approaches MCDA as one of the most active areas in operations research and management science (OR/MS). It presents not only the significant advances achieved to date, but also the new opportunities and challenges arising for both the theory and practice of MCDA. Among many others, the book addresses behavioral and conceptual aspects of decision aiding and decision making, problem structuring issues in the framework of new technological and socio-economic advances, methodological and algorithmic advances for analytical modeling and decision aiding, as well as a number of new application areas in engineering, business, and the social sciences.

This book presents various concepts and applications related to risk-conscious operations management. It also provides an overview of the risk-based engineering - fundamental to the concept of risk-conscious operations management. It presents the reliability concept to support Dependency Modelling, which includes hardware systems structures and components for reliability improvement and risk reduction. The book further develops and builds attributes and model for risk-conscious culture - critical to characterize operational approach to risk and presents human factor modelling, where it works on developing an approach for human error precursor analysis. This book will be useful for students, researchers, academicians and professionals working on identifying risk and reliability issues in complex safety and mission critical systems. It will also be beneficial for industry risk-and-reliability experts and operational safety staff working in the complex engineering systems.

Many interesting and important results on stochastic scheduling problems have been developed in recent years, with the aid of probability theory. This book provides a comprehensive and unified coverage of studies in stochastic scheduling. The objective is two-fold: (i) to summarize the elementary models and results in stochastic scheduling, so as to offer an entry-level reading material for students to learn and understand the fundamentals of this area and (ii) to include in details the latest developments and research topics on stochastic scheduling, so as to provide a useful reference for researchers and practitioners in this area. Optimal Stochastic Scheduling is organized into two parts: Chapters 1-4 cover fundamental models and results, whereas Chapters 5-10 elaborate on more advanced topics. More specifically, Chapter 1 provides the relevant basic theory of probability and then introduces the basic concepts and notation of stochastic scheduling. In Chapters 2 and 3, the authors review well-established models and scheduling policies, under regular and irregular performance measures, respectively. Chapter 4 describes models with stochastic machine breakdowns. Chapters 5 and 6 introduce, respectively, the optimal stopping problems and the multi-armed bandit processes, which are necessary for studies of more advanced subjects in subsequent chapters. Chapter 7 is focused on optimal dynamic policies, which allow adjustments of policies based on up-to-date information. Chapter 8 describes stochastic scheduling with incomplete information in the sense that the probability distributions of random variables contain unknown parameters, which can however be estimated progressively according to updated information. Chapter 9 is devoted to the situation where the processing time of a job depends on the time when it is started. Lastly, in Chapter 10 the authors look at several recent models beyond those surveyed in the previous chapters.

This book combines, for the first time, the operations management and operations research concepts in lean and agile supply chain management (SCM) for achieving decreased uncertainty, increased productivity, and sustainability through the use of quality engineering techniques (QETs). The book serves as a beneficial supplementary read for supply chain management and logistics courses in operations management/operations research for industrial engineering or management departments as the book uses practical examples of QET applications in SCM in a variety of industries, such as manufacturing, international shipping, and services. By reading this book, a wide range of audiences from general readers to students in industrial engineering or management fields will learn practical skills that can be utilized in the application of quality engineering techniques in lean and agile SCM.

Parallel Scientific Computing and Optimization introduces new developments in the construction, analysis, and implementation of parallel computing algorithms. This book presents 23 self-contained chapters, including survey chapters and surveys, written by distinguished researchers in the field of parallel computing. Each chapter is devoted to some aspects of the subject: parallel algorithms for matrix computations, parallel optimization, management of parallel programming models and data, with the largest focus on parallel scientific computing in industrial applications.

This volume is intended for scientists and graduate students specializing in computer science and applied mathematics who are engaged in parallel scientific computing.

This book constitutes the proceedings of the 6th International Symposium on Chaos, Complexity and Leadership (ICCLS). Written by interdisciplinary researchers and students from the fields of mathematics, physics, education, economics, political science, statistics, the management sciences and social sciences, the peer-reviewed contributions explore chaotic and complex systems, as well as chaos and complexity theory in the context of their applicability to management and leadership. The book discusses current topics, such as complexity leadership in the healthcare fields and tourism industry, conflict management and organization intelligence, and presents practical applications of theoretical concepts, making it a valuable resource for managers and leaders.

This contributed volume explores innovative research in the modeling, simulation, and control of crowd dynamics. Chapter authors approach the topic from the perspectives of mathematics, physics, engineering, and psychology, providing a comprehensive overview of the work carried out in this challenging interdisciplinary research field. In light of the recent COVID-19 pandemic, special consideration is given to applications of crowd dynamics to the prevention of the spreading of contagious diseases. Some of the specific topics covered in this volume include: - Impact of physical distancing on the evacuation of crowds- Generalized solutions of opinion dynamics models- Crowd dynamics coupled with models for infectious disease spreading- Optimized strategies for leaders in controlling the dynamics of a crowd Crowd Dynamics, Volume 3 is ideal for mathematicians, engineers, physicists, and other researchers working in the rapidly growing field of modeling and simulation of human crowds.

Combinatorial optimisation is a ubiquitous discipline whose usefulness spans vast applications domains. The intrinsic complexity of most combinatorial optimisation problems makes classical methods unaffordable in many cases. To acquire practical solutions to these problems requires the use of metaheuristic approaches that trade completeness for pragmatic effectiveness. Such approaches are able to provide optimal or quasi-optimal solutions to a plethora of difficult combinatorial optimisation problems.

The application of metaheuristics to combinatorial optimisation is an active field in which new theoretical developments, new algorithmic models, and new application areas are continuously emerging. This volume presents recent advances in the area of metaheuristic combinatorial optimisation, with a special focus on evolutionary computation methods. Moreover, it addresses local search methods and hybrid approaches. In this sense, the book includes cutting-edge theoretical, methodological, algorithmic and applied developments in the field, from respected experts and with a sound perspective.

This book demonstrates what kind of problems, originating in a management accounting setting, may be solved with game theoretic models. Game theory has experienced growing interest and numerous applications in the field of management accounting. The main focus traditionally has been on the field of non-cooperative behaviour, but the area of cooperative game theory has developed rapidly and has received increasing attention. Intensive research, in combination with the changing culture of publishing, has produced a nearly unmanageable number of publications in the areas concerned. Therefore, one main purpose of this volume is providing an intensive analysis of the intersection of these areas. In addition, the book strengthens the relationship between the theory and the practical applications and it illustrates the two-sided relationship between game theory and management accounting: new game theoretic models offer new fields of applications and these applications raise new questions for the theory.

Model Management and Analytics for Large Scale Systems covers the use of models and related artefacts (such as metamodels and model transformations) as central elements for tackling the complexity of building systems and managing data. With their increased use across diverse settings, the complexity, size, multiplicity and variety of those artefacts has increased. Originally developed for software engineering, these approaches can now be used to simplify the analytics of large-scale models and automate complex data analysis processes. Those in the field of data science will gain novel insights on the topic of model analytics that go beyond both model-based development and data analytics. This book is aimed at both researchers and practitioners who are interested in model-based development and the analytics of large-scale models, ranging from big data management and analytics, to enterprise domains. The book could also be used in graduate courses on model development, data analytics and data management.

This volume represents the refereed proceedings of the Eighth International C- ference on Monte Carlo and Quasi-Monte Carlo Methods in Scienti c Computing, which was held at the University of Montreal, from 6-11 July, 2008. It contains a limited selection of articles based on presentations made at the conference. The program was arranged with the help of an international committee consisting of: Ronald Cools, Katholieke Universiteit Leuven Luc Devroye, McGill University Henri Faure, CNRS Marseille Paul Glasserman, Columbia University Peter W. Glynn, Stanford University Stefan Heinrich, University of Kaiserslautern Fred J. Hickernell, Illinois Institute of Technology Aneta Karaivanova, Bulgarian Academy of Science Alexander Keller, mental images GmbH, Berlin Adam Kolkiewicz, University of Waterloo Frances Y. Kuo, University of New South Wales Christian Lecot, Universite de Savoie, Chambery Pierre L'Ecuyer, Universite de Montreal (Chair and organizer) Jun Liu, Harvard University Peter Mathe, Weierstrass Institute Berlin Makoto Matsumoto, Hiroshima University Thomas Muller-Gronbach, Otto von Guericke Universitat Harald Niederreiter, National University of Singapore Art B. Owen, Stanford University Gilles Pages, Universite Pierre et Marie Curie (Paris 6) Klaus Ritter, TU Darmstadt Karl Sabelfeld, Weierstrass Institute Berlin Wolfgang Ch. Schmid, University of Salzburg Ian H. Sloan, University of New South Wales Jerome Spanier, University of California, Irvine Bruno Tuf n, IRISA-INRIA, Rennes Henryk Wozniak ' owski, Columbia University. v vi Preface The local arrangements (program production, publicity, web site, registration, social events, etc.

This book presents a compilation of over 200 numerical problems and solutions that students can use to learn, practice and master the Inventory Control and Management concepts. Intended as a companion to any of the standard textbooks in Inventory Control and Management and written in simple language, it illustrates very clearly the steps students need to follow in order to solve a given problem. It also explains which solution methodologies can be used under which circumstances. Offering an ideal one-stop resource for mid-level engineering and business students who have taken Inventory Management or a related subject as an elective, this book is the only one students will ever need to prepare and gain confidence for their examinations in this subject.

A seminal collection of research methodology themes, this two-volume work provides a set of key scholarly developments related to robustness, allowing scholars to advance their knowledge of research methods used outside of their own immediate fields. With a focus on emerging methodologies within management, key areas of importance are dissected with chapters covering statistical modelling, new measurements, digital research, biometrics and neuroscience, the philosophy of research, computer modelling approaches and new mathematical theories, among others. A genuinely pioneering contribution to the advancement of research methods in business studies, Innovative Research Methodologies in Management presents an analytical and engaging discussion on each topic. By introducing new research agendas it aims to pave the way for increased application of innovative techniques, allow ing the exploration of future research perspectives. Volume I covers a range of research methodologies within the realms of philosophy, measurement and modelling, and focusses on meta-modern mixed methods such as neurophilosophy, diagnostic measurement, and emotivity and ephemera research.

Sample-Path Analysis of Queueing Systems uses a deterministic (sample-path) approach to analyze stochastic systems, primarily queueing systems and more general input-output systems. Among other topics of interest it deals with establishing fundamental relations between asymptotic frequencies and averages, pathwise stability, and insensitivity. These results are utilized to establish useful performance measures. The intuitive deterministic approach of this book will give researchers, teachers, practitioners, and students better insights into many results in queueing theory. The simplicity and intuitive appeal of the arguments will make these results more accessible, with no sacrifice of mathematical rigor. Recent topics such as pathwise stability are also covered in this context. The book consistently takes the point of view of focusing on one sample path of a stochastic process. Hence, it is devoted to providing pure sample-path arguments. With this approach it is possible to separate the issue of the validity of a relationship from issues of existence of limits and/or construction of stationary framework. Generally, in many cases of interest in queueing theory, relations hold, assuming limits exist, and the proofs are elementary and intuitive. In other cases, proofs of the existence of limits will require the heavy machinery of stochastic processes. The authors feel that sample-path analysis can be best used to provide general results that are independent of stochastic assumptions, complemented by use of probabilistic arguments to carry out a more detailed analysis. This book focuses on the first part of the picture. It does however, provide numerous examples that invoke stochastic assumptions, which typically are presented at the ends of the chapters.

This volume is the first (I) of four under the main themes of Digitizing Agriculture and Information and Communication Technologies (ICT). The four volumes cover rapidly developing processes including Sensors (I), Data (II), Decision (III), and Actions (IV). Volumes are related to 'digital transformation" within agricultural production and provision systems, and in the context of Smart Farming Technology and Knowledge-based Agriculture. Content spans broadly from data mining and visualization to big data analytics and decision making, alongside with the sustainability aspects stemming from the digital transformation of farming. The four volumes comprise the outcome of the 12th EFITA Congress, also incorporating chapters that originated from select presentations of the Congress. The focus in this volume is on different aspects of sensors implementation in agricultural production (e.g., types of sensors, parameters monitoring, network types, connectivity, accuracy, reliability, durability, and needs to be covered) and provides variety of information and knowledge in the subject of sensors design, development, and deployment for monitoring agricultural production parameters. The book consists of four (4) Sections. The first section presents an overview on the state-off-the art in sensing technologies applied in agricultural production while the rest of the sections are dedicated to remote sensing, proximal sensing, and wireless sensor networks applications. Topics include: Emerging sensing technologies Soil reflectance spectroscopy LoRa technologies applications in agriculture Wireless sensor networks deployment and applications Combined remote and proximal sensing solutions Crop phenology monitoring Sensors for geophysical properties Combined sensing technologies with geoinformation systems

This book pulls together robust practices in Partial Least Squares Structural Equation Modeling (PLS-SEM) from other disciplines and shows how they can be used in the area of Banking and Finance. In terms of empirical analysis techniques, Banking and Finance is a conservative discipline. As such, this book will raise awareness of the potential of PLS-SEM for application in various contexts. PLS-SEM is a non-parametric approach designed to maximize explained variance in latent constructs. Latent constructs are directly unobservable phenomena such as customer service quality and managerial competence. Explained variance refers to the extent we can predict, say, customer service quality, by examining other theoretically related latent constructs such as conduct of staff and communication skills. Examples of latent constructs at the microeconomic level include customer service quality, managerial effectiveness, perception of market leadership, etc.; macroeconomic-level latent constructs would be found in contagion of systemic risk from one financial sector to another, herd behavior among fund managers, risk tolerance in financial markets, etc. Behavioral Finance is bound to provide a wealth of opportunities for applying PLS-SEM. The book is designed to expose robust processes in application of PLS-SEM, including use of various software packages and codes, including R. PLS-SEM is already a popular tool in marketing and management information systems used to explain latent constructs. Until now, PLS-SEM has not enjoyed a wide acceptance in Banking and Finance. Based on recent research developments, this book represents the first collection of PLS-SEM applications in Banking and Finance. This book will serve as a reference book for those researchers keen on adopting PLS-SEM to explain latent constructs in Banking and Finance.

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 of the probability distributions of the search variates (mutation random variables).

Michael Porter is recognized as one of the top authorities on corporate strategy and business competition. The historical review of strategic management clearly shows that Porter's research has bridged up two general paradigms (before and after the 1980s) thus helping both researchers and practitioners to better understand unanticipated global changes. His two generic strategies: costs and diversification, the two interdependent strategic options, are key in the context of the competitiveness of orthodox microeconomic theory. This is where Porter went further, constructing a popular value chain concept that provides the ability to disaggregate the key activities of business process in creating products and services in terms of cost analysis and value creation. This book is a collection of seven interconnected chapters that provides a coherent understanding of Michael Porter's contribution to the field of strategic management. It addresses key changes and challenges in the global business environment. The value chain concept has become highly applicable in both theory and practice. In the book, the authors offer an original interpretation of the Porters' research on strategic management in order to unravel or simplify his key theoretical concepts. It will be of interest to researchers, academics, practitioners, and students in the fields of strategic management and international business.

This book presents the multi-criteria approach to decision support, as well as the various multi-criteria tools to help avoid multi-objective optimization. The book is intended as a tool for understanding the multi-criteria tools for decision support and modeling in mathematical programming. It helps to structure models, to easily model complex constraints, to have a basic modeling guide for any multi-criteria system and to better understand models already existing in the literature. The book is structured in the same order as components of the methodology, established in a multi-criteria optimization problem. It introduces the elements of the actors, the decision-making activity under criteria, calculations, specifications and objective criterion.

This handbook covers various areas of Higher Education (HE) in which operations research/management science (OR/MS) techniques are used. Key examples include: international comparisons, university rankings, and rating academic efficiency with Data Envelopment Analysis (DEA); formulating academic strategy with balanced scorecard; budgeting and planning with linear and quadratic models; student forecasting; E-learning evaluation; faculty evaluation with questionnaires and multivariate statistics; marketing for HE; analytic and educational simulation; academic information systems; technology transfer with systems analysis; and examination timetabling. Overviews, case studies and findings on advanced OR/MS applications in various functional areas of HE are included.

This book presents state-of-the-art research on responsible operations practices. The book identifies the challenges and opportunities arising from the shift towards responsible business operations and examines these issues through the lenses of operations management, emphasizing the supply chain transformations associated with these changes. Developing a responsible business model presents a great opportunity for firms to differentiate in the marketplace through innovative models and insights around responsible operations and supply chain management. To do so, companies in many industries are changing their practices around sourcing materials, supplier compliance around processes and labor, scientific and sustainable approaches to farming in emerging countries, managing counterfeiting risks, and public health management. Responsible Business Operations: Challenges and Opportunities is divided into three sections. Section 1 focuses on environmental responsibility for companies. It also explores alternative energy solutions for both the developed and developing world, as well as worldwide carbon footprint reduction efforts. Section 2 is dedicated to social responsibility, with chapters covering topics including improving agricultural food chains and humanitarian challenges for businesses. Finally Section 3 promotes ethical responsibility, analyzing ways to improve supplier compliance to product, process and ethical standards.

This book presents a diverse range of recent operational research techniques that have been applied to agriculture and tourism management. It covers both the primary sector of agriculture and agricultural economics, and the tertiary sector of the tourism industry. Findings and lessons learned from these innovations can be readily applied to various other contexts. The book chiefly focuses on cooperative management issues, and on developing solutions to provide decision support in multi-criteria scenarios.

DEA is computational at its core and this book will be one of several books that we will look to publish on the computational aspects of DEA. This book by Zhu and Cook will deal with the micro aspects of handling and modeling data issues in modeling DEA problems. DEA's use has grown with its capability of dealing with complex service industry and the public service domain types of problems that require modeling both qualitative and quantitative data. This will be a handbook treatment dealing with specific data problems including the following: (1) imprecise data, (2) inaccurate data, (3) missing data, (4) qualitative data, (5) outliers, (6) undesirable outputs, (7) quality data, (8) statistical analysis, (9) software and other data aspects of modeling complex DEA problems. In addition, the book will demonstrate how to visualize DEA results when the data is more than 3-dimensional, and how to identify efficiency units quickly and accurately.