Optimization problems were and still are the focus of mathematics from antiquity to the present. Since the beginning of our civilization, the human race has had to confront numerous technological challenges, such as finding the optimal solution of various problems including control technologies, power sources construction, applications in economy, mechanical engineering and energy distribution amongst others. These examples encompass both ancient as well as modern technologies like the first electrical energy distribution network in USA etc. Some of the key principles formulated in the middle ages were done by Johannes Kepler (Problem of the wine barrels), Johan Bernoulli (brachystochrone problem), Leonhard Euler (Calculus of Variations), Lagrange (Principle multipliers), that were formulated primarily in the ancient world and are of a geometric nature. In the beginning of the modern era, works of L.V. Kantorovich and G.B. Dantzig (so-called linear programming) can be considered amongst others. This book discusses a wide spectrum of optimization methods from classical to modern, alike heuristics. Novel as well as classical techniques is also discussed in this book, including its mutual intersection. Together with many interesting chapters, a reader will also encounter various methods used for proposed optimization approaches, such as game theory and evolutionary algorithms or modelling of evolutionary algorithm dynamics like complex networks.

The objective of the book is to give a selection from the papers, which summarize several important results obtained within the framework of the Jozsef Hatvany Doctoral School operating at the University of Miskolc, Hungary. In accordance with the three main research areas of the Doctoral School established for Information Science, Engineering and Technology, the papers can be classified into three groups. They are as follows: (1) Applied Computational Science; (2) Production Information Engineering (IT for Manufacturing included); (3) Material Stream Systems and IT for Logistics. As regards the first area, some papers deal with special issues of algorithms theory and its applications, with computing algorithms for engineering tasks, as well as certain issues of data base systems and knowledge intensive systems. Related to the second research area, the focus is on Production Information Engineering with special regard to discrete production processes. In the second research area the papers show some new integrated systems suitable for optimizing discrete production processes in a top-down way. The papers connecting with the third research field deal with different issues of materials stream systems and logistics, taking into consideration of applied mathematical models and IT-tools. The book makes an effort to ensure certain equilibrium between theory and practice and to show some new approach both from theoretical modelling aspect, as well as experimental and practical point of view.

Although several books or monographs on multiobjective optimization under uncertainty have been published, there seems to be no book which starts with an introductory chapter of linear programming and is designed to incorporate both fuzziness and randomness into multiobjective programming in a unified way. In this book, five major topics, linear programming, multiobjective programming, fuzzy programming, stochastic programming, and fuzzy stochastic programming, are presented in a comprehensive manner. Especially, the last four topics together comprise the main characteristics of this book, and special stress is placed on interactive decision making aspects of multiobjective programming for human-centered systems in most realistic situations under fuzziness and/or randomness. Organization of each chapter is briefly summarized as follows: Chapter 2 is a concise and condensed description of the theory of linear programming and its algorithms. Chapter 3 discusses fundamental notions and methods of multiobjective linear programming and concludes with interactive multiobjective linear programming. In Chapter 4, starting with clear explanations of fuzzy linear programming and fuzzy multiobjective linear programming, interactive fuzzy multiobjective linear programming is presented. Chapter 5 gives detailed explanations of fundamental notions and methods of stochastic programming including two-stage programming and chance constrained programming. Chapter 6 develops several interactive fuzzy programming approaches to multiobjective stochastic programming problems. Applications to purchase and transportation planning for food retailing are considered in Chapter 7. The book is self-contained because of the three appendices and answers to problems. Appendix A contains a brief summary of the topics from linear algebra. Pertinent results from nonlinear programming are summarized in Appendix B. Appendix C is a clear explanation of the Excel Solver, one of the easiest ways to solve optimization problems, through the use of simple examples of linear and nonlinear programming.

This book deals with complex variants of Travelling Salesman Problem (TSP) and Vehicle Routing Problem (VRP) within the manufacturing and service industries. The objective is to develop heuristics for these supply chain problems in order to offer practical solutions to improve operational efficiency. These heuristics are evaluated using benchmark and derived data-sets. Case studies pertaining to logistics in different industries including textile machinery manufacturing and banking are also included to demonstrate the created heuristics. High competition in today's global market has forced the organizations to invest in and focus on their logistics system. The critical function of logistics is the transportation within and across various supply chain entities. Both supply and distribution procedure require effective transportation management. A small improvement in routing problems can lead to huge logistics savings in absolute terms. This book should appeal to executives, researchers and consultants seeking supply chain management solutions.

This new edition of Markov Chains: Models, Algorithms and Applications has been completely reformatted as a text, complete with end-of-chapter exercises, a new focus on management science, new applications of the models, and new examples with applications in financial risk management and modeling of financial data. This book consists of eight chapters. Chapter 1 gives a brief introduction to the classical theory on both discrete and continuous time Markov chains. The relationship between Markov chains of finite states and matrix theory will also be highlighted. Some classical iterative methods for solving linear systems will be introduced for finding the stationary distribution of a Markov chain. The chapter then covers the basic theories and algorithms for hidden Markov models (HMMs) and Markov decision processes (MDPs). Chapter 2 discusses the applications of continuous time Markov chains to model queueing systems and discrete time Markov chain for computing the PageRank, the ranking of websites on the Internet. Chapter 3 studies Markovian models for manufacturing and re-manufacturing systems and presents closed form solutions and fast numerical algorithms for solving the captured systems. In Chapter 4, the authors present a simple hidden Markov model (HMM) with fast numerical algorithms for estimating the model parameters. An application of the HMM for customer classification is also presented. Chapter 5 discusses Markov decision processes for customer lifetime values. Customer Lifetime Values (CLV) is an important concept and quantity in marketing management. The authors present an approach based on Markov decision processes for the calculation of CLV using real data. Chapter 6 considers higher-order Markov chain models, particularly a class of parsimonious higher-order Markov chain models. Efficient estimation methods for model parameters based on linear programming are presented. Contemporary research results on applications to demand predictions, inventory control and financial risk measurement are also presented. In Chapter 7, a class of parsimonious multivariate Markov models is introduced. Again, efficient estimation methods based on linear programming are presented. Applications to demand predictions, inventory control policy and modeling credit ratings data are discussed. Finally, Chapter 8 re-visits hidden Markov models, and the authors present a new class of hidden Markov models with efficient algorithms for estimating the model parameters. Applications to modeling interest rates, credit ratings and default data are discussed. This book is aimed at senior undergraduate students, postgraduate students, professionals, practitioners, and researchers in applied mathematics, computational science, operational research, management science and finance, who are interested in the formulation and computation of queueing networks, Markov chain models and related topics. Readers are expected to have some basic knowledge of probability theory, Markov processes and matrix theory.

Contrary to their increasingly important role in the global economy, little is known so far about the financial reporting practices in emerging economies. This study therefore analyses the financial reporting practices of listed firms in Brazil and South Africa. It also investigates the determinants of financial reporting quality and its effect on information asymmetries. In addition, the author compares the results for the two emerging economies to those for Germany representing a developed economy. The empirical findings have several implications for researchers and lecturers as well as for practitioners, such as preparers and users of financial reports, legislators, standard setters and auditors.

This book presents the details of the BONUS algorithm and its real world applications in areas like sensor placement in large scale drinking water networks, sensor placement in advanced power systems, water management in power systems, and capacity expansion of energy systems. A generalized method for stochastic nonlinear programming based on a sampling based approach for uncertainty analysis and statistical reweighting to obtain probability information is demonstrated in this book. Stochastic optimization problems are difficult to solve since they involve dealing with optimization and uncertainty loops. There are two fundamental approaches used to solve such problems. The first being the decomposition techniques and the second method identifies problem specific structures and transforms the problem into a deterministic nonlinear programming problem. These techniques have significant limitations on either the objective function type or the underlying distributions for the uncertain variables. Moreover, these methods assume that there are a small number of scenarios to be evaluated for calculation of the probabilistic objective function and constraints. This book begins to tackle these issues by describing a generalized method for stochastic nonlinear programming problems. This title is best suited for practitioners, researchers and students in engineering, operations research, and management science who desire a complete understanding of the BONUS algorithm and its applications to the real world.

Every day decision making and decision making in complex human-centric systems are characterized by imperfect decision-relevant information. Main drawback of the existing decision theories is namely incapability to deal with imperfect information and modeling vague preferences. Actually, a paradigm of non-numerical probabilities in decision making has a long history and arose also in Keynes's analysis of uncertainty. There is a need for further generalization - a move to decision theories with perception-based imperfect information described in NL. The languages of new decision models for human-centric systems should be not languages based on binary logic but human-centric computational schemes able to operate on NL-described information. Development of new theories is now possible due to an increased computational power of information processing systems which allows for computations with imperfect information, particularly, imprecise and partially true information, which are much more complex than computations over numbers and probabilities. The monograph exposes the foundations of a new decision theory with imperfect decision-relevant information on environment and a decision maker's behavior. This theory is based on the synthesis of the fuzzy sets theory with perception-based information and the probability theory. The book is self containing and represents in a systematic way the decision theory with imperfect information into the educational systems. The book will be helpful for teachers and students of universities and colleges, for managers and specialists from various fields of business and economics, production and social sphere.

For decades, scholars have bemoaned the low relevancy and impact of most research in the leading journals in business, management, and marketing. The majority of the research that gets published, perhaps 70% of it, hardly has any measurable scholarly impact in terms of citations. Rather than low relevancy, 'Bad to Good' posits that the deeper issue is the pervasive use of bad research practices appearing in most articles in almost all ranked journals in the sub-disciplines of business. With the objective of reducing the high volume of bad practices in research in finance, management and marketing, the book offers tools for improving theory construction and empirical testing of theory especially by early-to-mid scholars. 'Bad to Good' covers 24 common bad practices, explaining why they are bad and how to replace them with good practices. Arch Woodside is a leading voice on how to improve business research. He served as the Editor-in-Chief of the 'Journal of Business Research' (JBR) for forty years. In 2016 the JBR ranked first among the top-twenty journals in marketing in the Google.com/scholar h-5 index (an impact metric) and seventh among the strategic management sub-discipline.

Network Analysis has become a major research topic over the last several years. The broad range of applications that can be described and analyzed by means of a network is bringing together researchers, practitioners and other scientific communities from numerous fields such as Operations Research, Computer Science, Transportation, Energy, Social Sciences, and more. The remarkable diversity of fields that take advantage of Network Analysis makes the endeavor of gathering up-to-date material in a single compilation a useful, yet very difficult, task. The purpose of these proceedings is to overcome this difficulty by collecting the major results found by the participants of the "First International Conference in Network Analysis," held at The University of Florida, Gainesville, USA, from the 14th to the 16th of December 2011. The contributions of this conference not only come from different fields, but also cover a broad range of topics relevant to the theory and practice of network analysis, including the reliability of complex networks, software, theory, methodology and applications.

Benjamin Krischan Schulte develops a process-model of consumer lock-in in service relationships by connecting three areas of research: path dependence, consumer behavior and service relationship marketing. He defines consumer lock-in as a situation of a potentially unaware inability to switch from or exit a consumption process due to entrenchment with increasing barriers on the individual and/or social level. Switching barriers are elaborated as consumer lock-in mechanisms. The resulting process model is outlined and empirically examined in an explorative panel study of a service relationship process in higher education. The authors findings support the presence of consumer lock-in in services as an idiosyncratic process of gradual entrapment. The phenomenon has relevance for researchers and practitioners in complex service relationships, where lock-in was found to be a likely occurrence but difficult to grasp.

A service economy era is coming! As the basic discipline of service dominant era, service science mainly studies common rules of service activities, aiming to provide theoretical bases for creating service value in the new era. The book, which integrates knowledge of service management, operational management, logistics and supply chain management, constructs a research system for this emerging discipline. Service science research system constitutes service philosophy, resource allocation, operational management and service technology. Many cases about China's service enterprises are incorporated in the book, in the hope of providing readers an insight into not only service science but also the development of China's service economy.

The Analytic Hierarchy Process (AHP) has been one of the foremost mathematical methods for decision making with multiple criteria and has been widely studied in the operations research literature as well as applied to solve countless real-world problems. This book is meant to introduce and strengthen the readers' knowledge of the AHP, no matter how familiar they may be with the topic. This book provides a concise, yet self-contained, introduction to the AHP that uses a novel and more pedagogical approach. It begins with an introduction to the principles of the AHP, covering the critical points of the method, as well as some of its applications. Next, the book explores further aspects of the method, including the derivation of the priority vector, the estimation of inconsistency, and the use of AHP for group decisions. Each of these is introduced by relaxing initial assumptions. Furthermore, this booklet covers extensions of AHP, which are typically neglected in elementary expositions of the methods. Such extensions concern different numerical representations of preferences and the interval and fuzzy representations of preferences to account for uncertainty. During the whole exposition, an eye is kept on the most recent developments of the method.

Improving Risk Analysis shows how to better assess and manage uncertain risks when the consequences of alternative actions are in doubt. The constructive methods of causal analysis and risk modeling presented in this monograph will enable to better understand uncertain risks and decide how to manage them. The book is divided into three parts. Parts 1 shows how high-quality risk analysis can improve the clarity and effectiveness of individual, community, and enterprise decisions when the consequences of different choices are uncertain. Part 2 discusses social decisions. Part 3 illustrates these methods and models, showing how to apply them to health effects of particulate air pollution. "Tony Cox's new book addresses what risk analysts and policy makers most need to know: How to find out what causes what, and how to quantify the practical differences that changes in risk management practices would make. The constructive methods in Improving Risk Analysis will be invaluable in helping practitioners to deliver more useful insights to inform high-stakes decisions and policy,in areas ranging from disaster planning to counter-terrorism investments to enterprise risk management to air pollution abatement policies. Better risk management is possible and practicable; Improving Risk Analysis explains how." Elisabeth Pate-Cornell, Stanford University "Improving Risk Analysis offers crucial advice for moving policy-relevant risk analyses towards more defensible, causally-based methods. Tony Cox draws on his extensive experience to offer sound advice and insights that will be invaluable to both policy makers and analysts in strengthening the foundations for important risk analyses. This much-needed book should be required reading for policy makers and policy analysts confronting uncertain risks and seeking more trustworthy risk analyses." Seth Guikema, Johns Hopkins University "Tony Cox has been a trail blazer in quantitative risk analysis, and his new book gives readers the knowledge and tools needed to cut through the complexity and advocacy inherent in risk analysis. Cox's careful exposition is detailed and thorough, yet accessible to non-technical readers interested in understanding uncertain risks and the outcomes associated with different mitigation actions. Improving Risk Analysis should be required reading for public officials responsible for making policy decisions about how best to protect public health and safety in an uncertain world." Susan E. Dudley, George Washington University

These proceedings consist of 30 selected research papers based on results presented at the 10th Balkan Conference & 1st International Symposium on Operational Research (BALCOR 2011) held in Thessaloniki, Greece, September 22-24, 2011. BALCOR is an established biennial conference attended by a large number of faculty, researchers and students from the Balkan countries but also from other European and Mediterranean countries as well. Over the past decade, the BALCOR conference has facilitated the exchange of scientific and technical information on the subject of Operations Research and related fields such as Mathematical Programming, Game Theory, Multiple Criteria Decision Analysis, Information Systems, Data Mining and more, in order to promote international scientific cooperation. The carefully selected and refereed papers present important recent developments and modern applications and will serve as excellent reference for students, researchers and practitioners in these disciplines.

This book addresses the question: how can institutions develop and maintain a good purpose? And how can managers contribute to this endeavour? Twelve contributions explore this question, using MacIntyrean inquiry as a basis for exploring four main themes: Can management be considered a practice in the MacIntyrean sense? What is the role of specific virtues in the development of a virtuous institution? What are management vices and what are the conditions in which they flourish? And, can we use MacIntyrean ideas to consider the management of all forms of institutions? The volume is an international and multidisciplinary collection, with contributions from well-known writers in the field of management ethics, and innovative contributions that use MacIntyrean inquiry as a lens to examine fields such as hospitality, user generated music content and social sustainability. The papers are unified by their concern for the achievement of organizational excellence and integrity through ethical management. Unlike single author texts this edited volume brings together multiple perspectives on the topic of virtue ethics in management. In doing so, it explores the topic both more deeply and more widely than a single author can do. Because of its breadth, this book has the potential to become a turn-to research tool for those interested in virtue theory's relevance to other academic interests such as organizational behavior (including motivation theory and social psychology), literature, contemporary social issue criticism, and business management. "Editors Harris, Wijesinghe, and McKenzie have crafted a tight, slim, and thematically consistent volume that will be indispensable to scholars and students with twin interests in business and virtue ethics. In particular, those working with MacIntyre's ideas will find the thorough and complimentary explorations and applications of his ideas to serve, overall, as a cornerstone for their own work." Brenden E. Kendall (2014), Harris, H., Wijesinghe, G., & McKenzie, S. (Eds.). (2013). The Heart of the Good Institution: Virtue Ethics as a Framework for Responsible Management. Dordrecht, The Netherlands: Springer, in Michael Schwartz , Howard Harris (ed.) Achieving Ethical Excellence (Research in Ethical Issues in Organizations, Volume 12) Emerald Group Publishing Limited, pp.155 - 161

This is a sequel to the book by Dr. A. Ashimov and his colleagues, Macroeconomic Analysis and Economic Policy Based on Parametric Control. The authors have expanded both the developed mathematical apparatus and the scope of problems and applications stemming from the practice of steering a national economy of a small country in the dynamic environment of the international economic order. The developed theoretical foundation is used in Chapter 1 to suggest a decision support system operating in the framework of state economic policy making. Chapter 2 presents various mathematical models built on the basis of the available statistical data and provides quality assessment of these models. Parametric control problems are being formulated on the basis of these models as problems of mathematical programming, and the obtained solutions are subjected to analyses and interpretations. This is demonstrated by the analysis of the effect of uncontrollable factors on the problem solutions. Chapter 3 is aimed at the modelling and analysis of cyclic phenomena in economics and their structural stability. Chapter 4 presents solutions of specific problems of national economy and analysis and interpretation of their solutions. In summary, the authors formulate comprehensive mathematical models of some critical mechanisms in micro economics previously known only on a qualitative level. They provide vigorous mathematical analysis of the models that justifies their applicability for the formulation of parametric control problems, and the existence of model-based solutions. The complexity of the resultant problems is addressed by the formulation of the appropriate algorithms. The described methodology leads to the development of computer-based decision support systems.

7. 1. 1 Background Uncertainty can be considered as the lack of adequate information to make a decision. It is important to quantify uncertainties in mathematical models used for design and optimization of nondeterministic engineering systems. In general, - certainty can be broadly classi?ed into three types (Bae et al. 2004; Ha-Rok 2004; Klir and Wierman 1998; Oberkampf and Helton 2002; Sentz 2002). The ?rst one is aleatory uncertainty (also referred to as stochastic uncertainty or inherent - certainty) - it results from the fact that a system can behave in random ways. For example, the failure of an engine can be modeled as an aleatory uncertaintybecause the failure can occur at a random time. One cannot predict exactly when the engine will fail even if a large quantity of failure data is gathered (available). The second one is epistemic uncertainty (also known as subjective uncertainty or reducible - certainty) - it is the uncertainty of the outcome of some random event due to lack of knowledge or information in any phase or activity of the modeling process. By gaining information about the system or environmental factors, one can reduce the epistemic uncertainty. For example, a lack of experimental data to characterize new materials and processes leads to epistemic uncertainty.

Leadership is demanding and challenging. How do leaders cope? How do they remain fit and strong, and thrive? The authors of Leadership Resilience, a business school academic and a police officer, suggest that many challenges faced by leaders are similar to the challenges experienced by police officers. The isolation; the pressure not to show personal emotions; the expectation that they will deal effectively with confused, frustrated and angry people; and that they can deal with delivering bad news; all contribute to the pressures bearing on leaders and police officers everywhere. The authors argue that these challenges are more pronounced in policing and so more readily identifiable than in other leadership situations. They explore challenges experienced by police officers, look at how they cope with them, and draw lessons for those undertaking leadership roles more generally. Leadership Resilience provides accounts from police officers, in their own words, of difficult experiences they encounter. They describe their feelings about what was important and how they coped with it. Each account is followed by an analysis highlighting what is discussed, and not discussed, in the accounts and identifying lessons that can be drawn by leaders in other situations. All is presented so that it is relevant to different cultures demanding different styles of leadership. Analysis of the engaging experiences featured will help leaders struggling with the gap between leadership education and capability and the demands made of them to survive and thrive, while maintaining their physical, mental, emotional, and spiritual health.

Behavioral Operations Management has been identified in the last years as one of the most promising emerging fields in Operations Management. Behavioral Issues in Operations Management explains and examines up-to-date research in this field, which works to analyze the impact of human behavior on the management of complex operating systems. A collection of studies from leading scholars presents different methodologies and approaches, supported by real data and case studies. Issues such as building trust and strong cooperative relationships with suppliers, enhancing motivation and designing proper incentives for stimulating more effective decision maker behaviours are considered. The main decision-making processes affected by behavioral issues are also analyzed with a focus on new product development, logistics, and supply chain integration. The broad coverage of methodologies and practical implications makes Behavioral Issues in Operations Management an ideal reference for both researchers developing new topics such as NK fitness landscapes and managers with an interest in behavioral management operations.

In today's competitive environment, manufacturing and service companies are intensifying their customization processes. Customization means companies must meet the challenge of providing individualized products and services, without introducing high costs. Therefore, companies must address both customization and cost factors to gain a competitive advantage. While product customization is the manufacturing of products according to individual customer needs, it does not involve any focus on the cost perspective. Information and Management Systems for Product Customization will concentrate on both product customization and costs' efficiency, which is termed as mass customization. Moreover, mass customization with its multi-dimensions is the new business paradigm challenging today's manufacturing companies.

This volume collects recent results in supply chain optimisation. It presents new approaches and methods based on operations research, artificial intelligence and advanced computing techniques for design of production systems, supply and inventory management, production planning and scheduling, location, transportation and logistics, and simulation in supply flow optimisation. The text presents a wide spectrum of optimisation problems taking into account supply chain paradigms, which are pivotal to improving productivity.

This volume addresses advanced DEA methodology and techniques developed for modeling unique new performance evaluation issues. Many numerical examples, real management cases and verbal descriptions make it very valuable for researchers and practitioners.

Till Albert presents a machine learning based approach to harnessing information contained in big data from different media sources such as patents, scientific publications, or the internet. He shows how this information can be used for automated maturity evaluation of yet unknown technologies. Elaborate patent based indicators contain very useful information on technological aspects of maturity but lack for others such as social, economic, ecological, or political factors. The approach presented in this book is able to incorporate these other factors and provide a firm basis for robust technology maturity and speed of maturity evaluation.

GERAD celebrates this year its 25th anniversary. The Center was created in 1980 by a small group of professors and researchers of HEC Montreal, McGill University and of the Ecole Polytechnique de Montreal. GERAD's activities achieved sufficient scope to justify its conversion in June 1988 into a Joint Research Centre of HEC Montreal, the Ecole Polytechnique de Montreal and McGill University. In 1996, the U- versite du Quebec a Montreal joined these three institutions. GERAD has fifty members (professors), more than twenty research associates and post doctoral students and more than two hundreds master and Ph.D. students. GERAD is a multi-university center and a vital forum for the devel- ment of operations research. Its mission is defined around the following four complementarily objectives: * The original and expert contribution to all research fields in GERAD's area of expertise; * The dissemination of research results in the best scientific outlets as well as in the society in general; * The training of graduate students and post doctoral researchers; * The contribution to the economic community by solving important problems and providing transferable tools.