This book covers three fundamental problems at the interface of multi-project management and human resource management: the selection of projects, the composition of small project teams, and workload leveling. Matthias Walter proposes optimization models and solution methods for these problems, assuming multi-skilled workers with heterogeneous skill levels. For the first time, the author presents exact and heuristic methods that support managers to form small teams. Additionally, he outlines a new skill chaining strategy that increases workforce flexibility.

The connected dominating set has been a classic subject studied in graph theory since 1975. Since the 1990s, it has been found to have important applications in communication networks, especially in wireless networks, as a virtual backbone. Motivated from those applications, many papers have been published in the literature during last 15 years. Now, the connected dominating set has become a hot research topic in computer science. In this book, we are going to collect recent developments on the connected dominating set, which presents the state of the art in the study of connected dominating sets. The book consists of 16 chapters. Except the 1st one, each chapter is devoted to one problem, and consists of three parts, motivation and overview, problem complexity analysis, and approximation algorithm designs, which will lead the reader to see clearly about the background, formulation, existing important research results, and open problems. Therefore, this would be a very valuable reference book for researchers in computer science and operations research, especially in areas of theoretical computer science, computer communication networks, combinatorial optimization, and discrete mathematics.

This book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems and to schedule maintenance and logistics plans. It is also the first book that describes the basic data-driven remaining useful life prognosis theory systematically and in detail. The emphasis of the book is on the stochastic models, methods and applications employed in remaining useful life prognosis. It includes a wealth of degradation monitoring experiment data, practical prognosis methods for remaining useful life in various cases, and a series of applications incorporated into prognostic information in decision-making, such as maintenance-related decisions and ordering spare parts. It also highlights the latest advances in data-driven remaining useful life prognosis techniques, especially in the contexts of adaptive prognosis for linear stochastic degrading systems, nonlinear degradation modeling based prognosis, residual storage life prognosis, and prognostic information-based decision-making.

Organizations - whether profit or nonprofit, services or manufacturing - need to be able to adapt and transform their cultures to succeed. Yet cultural transformation can seem either too easy or completely overwhelming. "Transforming Culture" shows how effective and sustainable cultural transformation can be achieved even in a challenging environment such as a General Motors manufacturing plant. The authors offer both a practical approach and tools to draw on the energy and ideas of employees and executives, remove obstacles to change, and create durable improvements.

In the field of genetic and evolutionary algorithms (GEAs), a large amount of theory and empirical study has been focused on operators and test problems, while problem representation has often been taken as given. This book breaks with this tradition and provides a comprehensive overview on the influence of problem representations on GEA performance. The book summarizes existing knowledge regarding problem representations and describes how basic properties of representations, such as redundancy, scaling, or locality, influence the performance of GEAs and other heuristic optimization methods. Using the developed theory, representations can be analyzed and designed in a theory-guided matter. The theoretical concepts are used for solving integer optimization problems and network design problems more efficiently. The book is written in an easy-readable style and is intended for researchers, practitioners, and students who want to learn about representations. This second edition extends the analysis of the basic properties of representations and introduces a new chapter on the analysis of direct representations.

Stochastic models are everywhere. In manufacturing, queuing models are used for modeling production processes, realistic inventory models are stochastic in nature. Stochastic models are considered in transportation and communication. Marketing models use stochastic descriptions of the demands and buyer's behaviors. In finance, market prices and exchange rates are assumed to be certain stochastic processes, and insurance claims appear at random times with random amounts. To each decision problem, a cost function is associated. Costs may be direct or indirect, like loss of time, quality deterioration, loss in production or dissatisfaction of customers. In decision making under uncertainty, the goal is to minimize the expected costs. However, in practically all realistic models, the calculation of the expected costs is impossible due to the model complexity. Simulation is the only practicable way of getting insight into such models. Thus, the problem of optimal decisions can be seen as getting simulation and optimization effectively combined. The field is quite new and yet the number of publications is enormous. This book does not even try to touch all work done in this area. Instead, many concepts are presented and treated with mathematical rigor and necessary conditions for the correctness of various approaches are stated. Optimization of Stochastic Models: The Interface Between Simulation and Optimization is suitable as a text for a graduate level course on Stochastic Models or as a secondary text for a graduate level course in Operations Research.

This book analyzes the use of modeling in charting the survival of financial and industrial enterprises. The author shows how to use models effectively, and goes on to consider the pitfalls that can occur. The book contains plenty of practical examples, making this a useful 'how to' guide.

Cooperative game theory is a booming research area with many new developments in the last few years. So, our main purpose when prep- ing the second edition was to incorporate as much of these new dev- opments as possible without changing the structure of the book. First, this o?ered us the opportunity to enhance and expand the treatment of traditional cooperative games, called here crisp games, and, especially, that of multi-choice games, in the idea to make the three parts of the monograph more balanced. Second, we have used the opportunity of a secondeditiontoupdateandenlargethelistofreferencesregardingthe threemodels of cooperative games. Finally, we have bene?ted fromthis opportunity by removing typos and a few less important results from the ?rst edition of the book, and by slightly polishing the English style and the punctuation, for the sake of consistency along the monograph. The main changes are: (1) Chapter 3 contains an additional section, Section 3. 3, on the - erage lexicographic value, which is a recent one-point solution concept de?ned on the class of balanced crisp games. (2) Chapter 4 is new. It o?ers a brief overview on solution c- cepts for crisp games from the point of view of egalitarian criteria, and presents in Section 4. 2 a recent set-valued solution concept based on egalitarian considerations, namely the equal split-o? set. (3)Chapter5isbasicallyanenlargedversionofChapter4ofthe?rst edition because Section 5. 4 dealing with the relation between convex games and clan games with crisp coalitions is new.

Data envelopment analysis develops a set of nonparametric and semiparametric techniques for measuring economic efficiency among firms and nonprofit organizations. Over the past decade this technique has found most widespread applications in public sector organizations. However these applications have been mostly static. This monograph extends this static framework of efficiency analysis in several new directions. These include but are not limited to the following: (1) a dynamic view of the production and cost frontier, where capital inputs are treated differently from the current inputs, (2) a direct role of the technological progress and regress, which is so often stressed in total factor productivity discussion in modem growth theory in economics, (3) stochastic efficiency in a dynamic setting, where reliability improvement competes with technical efficiency, (4) flexible manufacturing systems, where flexibility of the production process and the economies of scope play an important role in efficiency analysis and (5) the role of economic factors such as externalities and input interdependences. Efficiency is viewed here in the framework of a general systems theory model. Such a view is intended to broaden the scope of applications of this promising new technique of data envelopment analysis. The monograph stresses the various applied aspects of the dynamic theory, so that it can be empirically implemented in different situations. As far as possible abstract mathematical treatments are avoided and emphasis placed on the statistical examples and empirical illustrations.

This book examines sustainable wealth formation and dynamic decision-making. The global economy experienced a veritable meltdown of asset markets in the years 2007-9, where many funds were overexposed to risky returns and suffered considerable losses. On the other hand, the long-term upswing in the stock market since 2010 has led to asset price booms and some new, but also uneven, wealth formation. In this book a broader set of constraints and guidelines for asset management and wealth accumulation is developed. The authors investigate how wealth formation and the proper management of financial funds can help to adequately buffer income risk and obtain sufficient risk-free income at a later stage of life, while also being socially and environmentally sustainable. The book explores behavioral and institutional rules for decision-making that reflect such constraints and guidelines, without necessarily being optimal in the narrow sense. The authors explain the need for such a dynamic decision-making and dynamic re-balancing of portfolios, by putting forward dynamic programming as an approach to dynamic decision-making that can allow sustainable wealth accumulation and dynamic asset allocation to be successfully integrated. This book provides a clear and comprehensive treatment of asset accumulation and dynamic portfolio models with an emphasis on long term and sustainable wealth formation. An important concern in public debate is the sustainability of our economy and this book employs cutting edge quantitative techniques and models to highlight important facts that cannot be disputed under any reasonable assumptions. It has the potential to become a standard reference for both academic researchers and quantitatively trained practitioners. Eckhard Platen, Professor of Quantitative Finance, University of Technology Sydney, Australia This book should be read by both academics and practitioners alike. The former will find intellectually rigorous discussions and innovative solutions. The latter may find a few of the concepts a bit challenging. Yet, theory and technology are there to help simplify the work of those who worry about what time it is rather than how to make a watch--- but they do need a watch. Jean Brunel, Founder of Brunel Associates and Editor of The Journal of Wealth Management

This volume is a comprehensive collection of extended contributions from the Workshop on Computational Optimization 2014, held at Warsaw, Poland, September 7-10, 2014. The book presents recent advances in computational optimization. The volume includes important real problems like parameter settings for controlling processes in bioreactor and other processes, resource constrained project scheduling, infection distribution, molecule distance geometry, quantum computing, real-time management and optimal control, bin packing, medical image processing, localization the abrupt atmospheric contamination source and so on. It shows how to develop algorithms for them based on new metaheuristic methods like evolutionary computation, ant colony optimization, constrain programming and others. This research demonstrates how some real-world problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks.

The Economic Order Quantity (EOQ) inventory model first appeared in 1913, and in its centennial, it is still one of the most important inventory models. Despite the abundance of both classical and new research results, there was (until now) no comprehensive reference source that provides the state-of-the-art findings on both theoretical and applied research on the EOQ and its related models. This edited handbook puts together all these interesting works and the respective insights into an edited volume.

The handbook contains papers which explore both the deterministic and the stochastic EOQ-model based problems and applications. It is organized into three parts: Part I presents three papers that provide an introduction and review of the EOQ, a consideration of multi-period lot sizing with stationary demand, and EOQ models with supply disruptions. Part II includes four technical analyses on single-echelon EOQ- model based inventory problems. Part III consists of five papers on applications of the EOQ model for multi-echelon supply chain inventory analysis."

This book aims at stimulating discussion between researchers working on state of the art approaches for operational control and design of transport of water on the one hand and researchers working on state of the art approaches for transport over water on the other hand. The main contribution of the book as a whole is to present novel perspectives ultimately leading to the management of an envisioned unified management framework taking the recent advances from both worlds as a baseline. The book is intended to be a reference for control-oriented engineers who manage water systems with either or both purposes in mind (transport of water, transport of goods over water). It highlights the possible twofold nature of water projects, where water either acts as primary object of study or as a means. The book is dedicated to comparing and relating to one another different strategies for (operational) management and control of different but strongly related systems in the framework of the water. In that sense, the book presents different approaches treating both the transport of water and transport over water. It compares the different approaches within the same field, highlighting their distinguishing features and advantages according to selected qualitative indices, and demonstrates the interaction and cross-relations between both fields. It will also help to determine the gaps and common points for both fields towards the design of such a unifying framework, which is lacking in the literature. Additionally, the book looks at case studies where the design of modeling/control strategies of either transport of water or transport over water have been proposed, discussed or simulated.

The world of retailing has changed dramatically in the past decade. Sales originating at online channels have been steadily increasing, and even for sales transacted at brick-and-mortar channels, a much larger fraction of sales is affected by online channels in different touch points during the customer journey. Shopper behavior and expectations have been evolving along with the growth of digital channels, challenging retailers to redesign their fulfillment and execution processes, to better serve their customers. This edited book examines the challenges and opportunities arising from the shift towards omni- channel retail. We examine these issues through the lenses of operations management, emphasizing the supply chain transformations associated with fulfilling an omni-channel demand. The book is divided into three parts. In the first part, "Omni-channel business models", we present four studies that explore how retailers are adjusting their fundamental business models to the new omni-channel landscape. The second part, "Data-driven decisions in an omni-channel world", includes five chapters that study the evolving data opportunities enabled by omni-channel retail and present specific examples of data-driven analyses. Finally, in the third part, "Case studies in Omni-channel retailing", we include four studies that provide a deep dive into how specific industries, companies and markets are navigating the omni-channel world. Ultimately, this book introduces the reader to the fundamentals of operations in an omni-channel context and highlights the different innovative research ideas on the topic using a variety of methodologies.

"Decision Systems and Non-stochastic Randomness" is the first systematic presentation and mathematical formalization (including existence theorems) of the statistical regularities of non-stochastic randomness. The results presented in this book extend the capabilities of probability theory by providing mathematical techniques that allow for the description of uncertain events that do not fit standard stochastic models. The book demonstrates how non-stochastic regularities can be incorporated into decision theory and information theory, offering an alternative to the subjective probability approach to uncertainty and the unified approach to the measurement of information. This book is intended for statisticians, mathematicians, engineers, economists or other researchers interested in non-stochastic modeling and decision theory.

When I wrote the book Quantitative Sociodynamics, it was an early attempt to make methods from statistical physics and complex systems theory fruitful for the modeling and understanding of social phenomena. Unfortunately, the ?rst edition appeared at a quite prohibitive price. This was one reason to make these chapters available again by a new edition. The other reason is that, in the meantime, many of the methods discussed in this book are more and more used in a variety of different ?elds. Among the ideas worked out in this book are: 1 * a statistical theory of binary social interactions, * a mathematical formulation of social ?eld theory, which is the basis of social 2 force models, * a microscopic foundation of evolutionary game theory, based on what is known today as 'proportional imitation rule', a stochastic treatment of interactions in evolutionary game theory, and a model for the self-organization of behavioral 3 conventions in a coordination game. It, therefore, appeared reasonable to make this book available again, but at a more affordable price. To keep its original character, the translation of this book, which 1 D. Helbing, Interrelations between stochastic equations for systems with pair interactions. Ph- icaA 181, 29-52 (1992); D. Helbing, Boltzmann-like and Boltzmann-Fokker-Planck equations as a foundation of behavioral models. PhysicaA 196, 546-573 (1993). 2 D. Helbing, Boltzmann-like and Boltzmann-Fokker-Planck equations as a foundation of beh- ioral models. PhysicaA 196, 546-573 (1993); D.

This book presents the first part of a planned two-volume series devoted to a systematic exposition of some recent developments in the theory of discrete-time Markov control processes (MCPs). Interest is mainly confined to MCPs with Borel state and control (or action) spaces, and possibly unbounded costs and noncompact control constraint sets. MCPs are a class of stochastic control problems, also known as Markov decision processes, controlled Markov processes, or stochastic dynamic pro grams; sometimes, particularly when the state space is a countable set, they are also called Markov decision (or controlled Markov) chains. Regardless of the name used, MCPs appear in many fields, for example, engineering, economics, operations research, statistics, renewable and nonrenewable re source management, (control of) epidemics, etc. However, most of the lit erature (say, at least 90%) is concentrated on MCPs for which (a) the state space is a countable set, and/or (b) the costs-per-stage are bounded, and/or (c) the control constraint sets are compact. But curiously enough, the most widely used control model in engineering and economics--namely the LQ (Linear system/Quadratic cost) model-satisfies none of these conditions. Moreover, when dealing with "partially observable" systems) a standard approach is to transform them into equivalent "completely observable" sys tems in a larger state space (in fact, a space of probability measures), which is uncountable even if the original state process is finite-valued."

This book presents the latest tools, techniques, and solutions that decision makers use to overcome the challenges faced by their sustainable supply chains. Given the ever increasing significance of socio-economic and environmental factors, the management of sustainable supply chains has become a complex and dynamic task. Multiple and conflicting objectives of stakeholders including suppliers, manufacturers, service providers, and retailers add to the complexity of decisions that modern day managers of supply chains face. With the unprecedented technological developments and innovations at hand, sustainability can be maximized for all the activities of a supply chain including: service concept and product design, material sourcing and procurement, manufacturing processes, delivery of the final product, and end-of-life management of the product. Consequently, the sustainable supply chains' problems require a systematic and integrated approach. Modeling and simulation, in general, as well as system dynamics and agent-based modeling, in particular, have the capabilities to deal with the complexity of sustainable supply chain related problems. This book will appeal to professionals and researchers in the field.

What should the next generation of knowledge management practices be? "Living Knowledge" offers an empirical perspective on the dynamic and living nature of knowledge in organizations, based on research on professional service work. The book starts from a perspective on knowledge as being constituted in practice and guides the reader through a diverse set of organizational experiences. These cases present a series of new concepts for understanding and managing knowledge, such as half-worked boundary objects, knowledge hyperstories, activity centered knowledge support and knowledge dramas.

Industries rely more and more on advanced technology. Accelerated computer evolution makes large-scale computation practical. Many enterprises are be ginning to benefit from more efficient allocation of resources and more effective planning, scheduling, manufacturing, and distribution by adopting state-of-the art decision support systems. Academics increasingly emphasize application driven research. All these forces have moved optimization from a pure class room and textbook terminology to an accepted tool in today's business world. This book chronicles and describes applications of combinatorial optimization in industry. A wide range of applications is included: manpower planning * production planning * job sequencing and scheduling * manufacturing layout design * facility planning * vehicle scheduling and routing * retail seasonal planning * I! space shuttle scheduling, and telecommunication network design . * The applications covered in this book comprise a representative set of industry sectors including electronics, airlines, manufacturing, tobacco, retail, telecom munication, defense, and livestock. These examples should encourage opera tions researchers and applied mathematicians by pointing out how the impor tance and practicality of optimization is starting to be realized by the manage ment of various organizations and how some pioneering developments in this field are beginning to bear fruit.

Operations Research is a field whose major contribution has been to propose a rigorous fonnulation of often ill-defmed problems pertaining to the organization or the design of large scale systems, such as resource allocation problems, scheduling and the like. While this effort did help a lot in understanding the nature of these problems, the mathematical models have proved only partially satisfactory due to the difficulty in gathering precise data, and in formulating objective functions that reflect the multi-faceted notion of optimal solution according to human experts. In this respect linear programming is a typical example of impressive achievement of Operations Research, that in its detenninistic fonn is not always adapted to real world decision-making : everything must be expressed in tenns of linear constraints ; yet the coefficients that appear in these constraints may not be so well-defined, either because their value depends upon other parameters (not accounted for in the model) or because they cannot be precisely assessed, and only qualitative estimates of these coefficients are available. Similarly the best solution to a linear programming problem may be more a matter of compromise between various criteria rather than just minimizing or maximizing a linear objective function. Lastly the constraints, expressed by equalities or inequalities between linear expressions, are often softer in reality that what their mathematical expression might let us believe, and infeasibility as detected by the linear programming techniques can often been coped with by making trade-offs with the real world.

Internet is starting to permeate politics much as it has previously revolutionised education, business or the arts. Thus, there is a growing interest in areas of e-government and, more recently, e-democracy. However, most attempts in this field have just envisioned standard political approaches facilitated by technology, like e-voting or e-debating. Alternatively, we could devise a more transforming strategy based on deploying web based group decision support tools and promote their use for public policy decision making.

This book delineates how this approach could be implemented. It addresses foundations, basic methodologies, potential implementation and applications, together with a thorough discussion of the many challenging issues.

This innovative text will be of interest to students, researchers and practitioners in the fields of e-government, e-democracy and e-participation and research in decision analysis, negotiation analysis and group decision support.

This book describes the inferential and modeling advantages that this distribution, together with its generalizations and modifications, offers. The exposition systematically unfolds with many examples, tables, illustrations, and exercises. A comprehensive index and extensive bibliography also make this book an ideal text for a senior undergraduate and graduate seminar on statistical distributions, or for a short half-term academic course in statistics, applied probability, and finance.

A Perspective on Two Decades of Rapid Modeling It is an honor for me to be asked to write a foreword to the Proceedings of the 1st Rapid Modeling Conference. In 1987, when I coined the term "Rapid Modeling" to denote queuing modeling of manufacturing systems, I never imagined that two decades later there would be an international conference devoted to this topic I am delighted to see that there will be around 40 presentations at the conference by leading researchers from aroundthe world, and about half of these presentationsare represented by written papers published in this book. I congratulate the conference organizers and program committee on the success of their efforts to hold the ?rst ever conference on Rapid Modeling. Attendees at this conferencemight?nd it interesting to learn about the history of the term Rapid Modeling in the context it is used here. During the fall of 1986 I was invited to a meeting at the Headquarters of the Society of Manufacturing Engineers (SME) in Dearborn, Michigan. By that time I had successfully demonstrated s- eral industry applications of queuing network models at leading manufacturers in the USA. Although in principle the use of queuing networks to model manufact- ing systems was well known in the OR/MS community and many papers had been published, the actual use of suchmodelsby manufacturingprofessionalswas almost nonexistent.

Finite-dimensional optimization problems occur throughout the mathematical sciences. The majority of these problems cannot be solved analytically. This introduction to optimization attempts to strike a balance between presentation of mathematical theory and development of numerical algorithms. Building on students' skills in calculus and linear algebra, the text provides a rigorous exposition without undue abstraction. Its stress on statistical applications will be especially appealing to graduate students of statistics and biostatistics. The intended audience also includes students in applied mathematics, computational biology, computer science, economics, and physics who want to see rigorous mathematics combined with real applications. In this second edition the emphasis remains on finite-dimensional optimization. New material has been added on the MM algorithm, block descent and ascent, and the calculus of variations. Convex calculus is now treated in much greater depth. Advanced topics such as the Fenchel conjugate, subdifferentials, duality, feasibility, alternating projections, projected gradient methods, exact penalty methods, and Bregman iteration will equip students with the essentials for understanding modern data mining techniques in high dimensions.