This book discusses emerging themes in the area of humanitarian logistics. It examines how humanitarian logistics and supply chains play a key role, focusing on rapidly delivering the correct amount of goods, people and monetary resources to the locations needed to achieve the success of relief efforts in response to global emergencies such as flood, earthquakes, wars etc. With an increase in the frequency, magnitude and impact of both natural and manmade disasters, effective delivery of humanitarian aid is an issue that is becoming increasingly important in the context of disaster management. The book focuses on how logistics systems and supply chains responsible for delivering this aid from origin to recipients can be made more effective and efficient. It also discusses how the development of information technology systems that can provide visibility to the disaster relief supply chain marks a huge step forward for the humanitarian sector as a whole. As more organizations begin to adopt and implement these systems and visibility is established, the use of key performance indicators will then become essential to further enhance the efficiency and effectiveness of these supply chains.

Making decisions is certainly the most important task managers are faced with, and it is often a very difficult one. This book offers a procedure for solving complex decision problems step by step. Unlike other texts, the book focuses on problem analysis, on developing potential solutions, and on establishing a decision-making matrix. In this fourth edition of the book, published under a new title, the authors present simplified, actionable guidelines that can be easily applied to the individual steps in the heuristic process. The book is intended for decision-makers at companies, non-profit organizations and in public administration whose work involves complex problems. It will also benefit students and participants in executive courses.

This handbook for the Methodology of Societal Complexity describes the theoretical development of the field and lays the foundation for the application of the Compram Methodology in the context of addressing complex societal problems. As such, it offers a valuable resource for scientists, practitioners, politicians, master and PhD students in the fields of methodology, the social sciences, operational research, management and political science and for all others who are professionally involved in handling complex societal problems. These problems are the kind that fill the front page of quality newspapers; they have a huge impact on society, involve a variety of phenomena and actors, and are therefore difficult to handle. The structured Compram Methodology provides sound guidelines for handling real-life societal problems democratically, sustainably and transparently. Examples of the use of the Compram Methodology are provided in the domain of global safety with regard to healthcare, economics, climate change, terrorism, large city problems, large technological projects and floods.

Complex societal problems must be treated as multi-disciplinary, multi-actor, multi-level and often as multi-continental issues. As such, they call for a multi-disciplinary and multi-actor approach that takes into account the emotional aspects of the problem and the problem handling process, including the micro, meso and macro level, which can be accomplished using the methods, models and tools from the field of the Methodology of Societal Complexity.

The Compram Methodology improves the problem handling process and increases the quality of interventions and therefore the quality of life. Handling complex societal problems can reduce conflicts, save money and ultimately even save lives.

Dorien J. DeTombe is an internationally recognized expert and founder of the theory of the Methodology of Societal Complexity and the Compram Methodology.

This is a supplementary volume to the major three-volume Handbook of Combinatorial Optimization set. It can also be regarded as a stand-alone volume presenting chapters dealing with various aspects of the subject in a self-contained way.

The volume is dedicated to Stephen Smale on the occasion of his 80th birthday.Besides his startling 1960 result of the proof of the Poincare conjecture for all dimensionsgreater than or equal to five, Smale's ground breaking contributions invarious fields in Mathematics have marked the second part of the 20th century andbeyond. Stephen Smale has done pioneering work in differential topology, globalanalysis, dynamical systems, nonlinear functional analysis, numerical analysis, theoryof computation and machine learning as well as applications in the physical andbiological sciences and economics. In sum, Stephen Smale has manifestly brokenthe barriers among the different fields of mathematics and dispelled some remainingprejudices. He is indeed a universal mathematician. Smale has been honoredwith several prizes and honorary degrees including, among others, the Fields Medal(1966), The Veblen Prize (1966), the National Medal of Science (1996) and theWolfPrize (2006/2007)."

This book showcases a large variety of multiple criteria decision applications (MCDAs), presenting them in a coherent framework provided by the methodology chapters and the comments accompanying each case study. The chapters describing MCDAs invite the reader to experiment with MCDA methods and perhaps develop new variants using data from these case studies or other cases they encounter, equipping them with a broader perception of real-world problems and how to overcome them with the help of MCDAs.

The main contents and character of the monograph did not change with respect to the first edition. However, within most chapters we incorporated quite a number of modifications which take into account the recent development of the field, the very valuable suggestions and comments that we received from numerous colleagues and students as well as our own experience while using the book. Some errors and misprints in the first edition are also corrected. Reiner Horst May 1992 Hoang Tuy PREFACE TO THE FIRST EDITION The enormous practical need for solving global optimization problems coupled with a rapidly advancing computer technology has allowed one to consider problems which a few years aga would have been considered computationally intractable. As a consequence, we are seeing the creation of a large and increasing number of diverse algorithms for solving a wide variety of multiextremal global optimization problems. The goal of this book is to systematically clarify and unify these diverse approaches in order to provide insight into the underlying concepts and their pro perties. Aside from a coherent view of the field much new material is presented."

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.

This book is an extension of the author's first book and serves as a guide and manual on how to specify and compute 2-, 3-, and 4-Event Bayesian Belief Networks (BBN). It walks the learner through the steps of fitting and solving fifty BBN numerically, using mathematical proof. The author wrote this book primarily for inexperienced learners as well as professionals, while maintaining a proof-based academic rigor. The author's first book on this topic, a primer introducing learners to the basic complexities and nuances associated with learning Bayes' theorem and inverse probability for the first time, was meant for non-statisticians unfamiliar with the theorem-as is this book. This new book expands upon that approach and is meant to be a prescriptive guide for building BBN and executive decision-making for students and professionals; intended so that decision-makers can invest their time and start using this inductive reasoning principle in their decision-making processes. It highlights the utility of an algorithm that served as the basis for the first book, and includes fifty 2-, 3-, and 4-event BBN of numerous variants.

This book is a rigorous but practical presentation of the techniques of uncertainty quantification, with applications in R and Python. This volume includes mathematical arguments at the level necessary to make the presentation rigorous and the assumptions clearly established, while maintaining a focus on practical applications of uncertainty quantification methods. Practical aspects of applied probability are also discussed, making the content accessible to students. The introduction of R and Python allows the reader to solve more complex problems involving a more significant number of variables. Users will be able to use examples laid out in the text to solve medium-sized problems. The list of topics covered in this volume includes linear and nonlinear programming, Lagrange multipliers (for sensitivity), multi-objective optimization, game theory, as well as linear algebraic equations, and probability and statistics. Blending theoretical rigor and practical applications, this volume will be of interest to professionals, researchers, graduate and undergraduate students interested in the use of uncertainty quantification techniques within the framework of operations research and mathematical programming, for applications in management and planning.

The field of discrete event systems has emerged to provide a formal treatment of many of the man-made systems such as manufacturing systems, communication networks, automated traffic systems, database management systems, and computer systems that are event-driven, highly complex, and not amenable to the classical treatments based on differential or difference equations. Discrete event systems is a growing field that utilizes many interesting mathematical models and techniques. In Modeling and Control of Logical Discrete Event Systems, the focus is on a high level treatment of discrete event systems, where the order of events, rather their occurrence times, is the principal concern. Such treatment is needed to guarantee that the system under study meets desired logical goals. In this framework, discrete event systems are modeled by formal languages or, equivalently, by state machines. The field of logical discrete event systems is an interdisciplinary field -- it includes ideas from computer science, control theory, and operations research. Our goal is to bring together in one book the relevant techniques from these fields. Modeling and Control of Logical Discrete Event Systems is the first book of this kind for professionals in the area of discrete event systems. The book is also designed for a graduate level course on logical discrete event systems. It contains all the necessary background material in formal language theory and lattice theory. The only prerequisite is some degree of mathematical maturity'. Several examples and exercise problems are included in each chapter to facilitate classroom teaching.

Equilibrium is a concept used in operations research and economics to understand the interplay of factors and problems arising from competitive systems in the economic world. The problems in this area are large and complex and have involved a variety of mathematical methodologies. In this monograph, the authors have widened the scope of theoretical work with a new approach, projected dynamical systems theory', to previous work in variational inequality theory. While most classical work in this area is static, the introduction to the theory of projected dynamical systems will allow many real-life dynamic situations and problems to be handled and modeled. This monograph includes: a new theoretical approach, projected dynamical system', which allows the researcher to model real-life situations more accurately; new mathematical methods allowing researchers to combine other theoretical approaches with the projected dynamical systems approach; a framework in which research can adequately model natural, financial and human (real life) situations in competitive equilibrium problems; the computational and numerical methods for the implementation of the methods and theory discussed in the book; stability analysis, algorithms and computational procedures are offered for each set of applications.

This book provides a hands-on introduction to Machine Learning (ML) from a multidisciplinary perspective that does not require a background in data science or computer science. It explains ML using simple language and a straightforward approach guided by real-world examples in areas such as health informatics, information technology, and business analytics. The book will help readers understand the various key algorithms, major software tools, and their applications. Moreover, through examples from the healthcare and business analytics fields, it demonstrates how and when ML can help them make better decisions in their disciplines. The book is chiefly intended for undergraduate and graduate students who are taking an introductory course in machine learning. It will also benefit data analysts and anyone interested in learning ML approaches.

Computer Science and Operations Research continue to have a synergistic relationship and this book represents the results of the cross-fertilization between OR/MS and CS/AI. It is this interface of OR/CS that makes possible advances that could not have been achieved in isolation. Taken collectively, these articles are indicative of the state of the art in the interface between OR/MS and CS/AI and of the high-caliber research being conducted by members of the INFORMS Computing Society.

International Applications of Productivity and Efficiency Analysis features a complete range of techniques utilized in frontier analysis, including extensions of existing techniques and the development of new techniques. Another feature is that most of the contributions use panel data in a variety of approaches. Finally, the range of empirical applications is at least as great as the range of techniques, and many of the applications are of considerable policy relevance.

At the end of the nineteenth century Lyapunov and Poincare developed the so called qualitative theory of differential equations and introduced geometric- topological considerations which have led to the concept of dynamical systems. In its present abstract form this concept goes back to G.D. Birkhoff. This is also the starting point of Chapter 1 of this book in which uncontrolled and controlled time-continuous and time-discrete systems are investigated. Controlled dynamical systems could be considered as dynamical systems in the strong sense, if the controls were incorporated into the state space. We, however, adapt the conventional treatment of controlled systems as in control theory. We are mainly interested in the question of controllability of dynamical systems into equilibrium states. In the non-autonomous time-discrete case we also consider the problem of stabilization. We conclude with chaotic behavior of autonomous time discrete systems and actual real-world applications.

A smart city is a city that collates data via various technological methods, and uses insights gleaned from this data to manage assets, resources, services and operations more efficiently. Though the concept of 'smart cities' is fairly new, there is a vast amount of interest in the topic, exploring how technological advances can be used to better manage the integration of business and operations within a city, as well as how sustainable choices can be written into the fabric of an urban space. This book explores logistics within smart cities: the greater logistical demands of a smart city, how logistics can be adapted to new challenges, and what sort of new logistical support a smart city will need. The book pays particular attention to how logistical innovation within a smart city can lead to greater sustainability in the city, and on a global level. It will be of interest to academics working in logistics, urban planning, innovation management, digital technology, sustainability management, and operations management.

This indispensable text offers students a high quality treatment of strategic operations management. It provides the reader with a clear understanding of the importance and nature of operations strategy by determining exactly which management activities, core competencies, resources and technologies underpin an operational strategy. The book demonstrates how various operational elements and components can be combined and customised into unique operational strategies. When these strategies are correctly implemented, they provide sustainable competitive advantage and allow firms to provide a diverse range of services and goods in their increasingly demanding, complex and dynamic marketplaces and spaces.
Includes chapters covering customising operational strategies for retail, manufacturing, services and SMEs, and sections on eBusiness and complexity theory in relation to operations theory.
Features include:
*extended case-studies including several from Europe and the USA
*case vignettes
*learning objectives
*key terms
*chapter introduction and 'maps' to aid reader accessibility
*'time out' boxes to prompt the reader to reflect on what has been learnt
*'critical reflection' boxes that analyse theories and models.

Related link: www.routledge.com/textbooks/0415256542
eBook available with sample pages: 0203361520

This volume, Systems and Management Science by Extremal Methods, is the second in a series dedicated to honoring and extending the work of Abraham Charnes. The first volume, entitled Extremal Methods and Systems Analysis (Springer Verlag, Berlin, 1980), was edited by A.V. Fiacco and K.O. Kortanek. Subtitled "An International Symposium on the Occasion of Abraham Charnes' Sixtieth Birthday," this first volume consisted of a selection from papers presented at a conference in honor of Professor Charnes held at The University of Texas at Austin in September 1977. This second volume consists of papers, to be described more fully below, that were presented in a similar 2 conference held at the IC Institute of The University of Texas at Austin, Texas, in October of 1987, to honor Dr. Charnes on his seventieth birthday. All these papers were written by scholars and scientists whose own work has been affected by the contributions of this distinguished scholar and educator over a long period of time.

This book provides insights from research and practice in how organizations were able to sustain resilience in their global supply chains during the COVID-19 pandemic and to advance the understanding of supply chain risk management. The chapters highlight the lessons learned, insist on new models for resilience, suggest improved supply chain risk methodologies and bridge the gap between research and practice. It helps readers acquire greater knowledge, strategic approaches, new methods, and practical tools for ensuring global supply chain resilience.

"Optimization on Metric and Normed Spaces" is devoted to the recent progress in optimization on Banach spaces and complete metric spaces. Optimization problems are usually considered on metric spaces satisfying certain compactness assumptions which guarantee the existence of solutions and convergence of algorithms. This book considers spaces that do not satisfy such compactness assumptions. In order to overcome these difficulties, the book uses the Baire category approach and considers approximate solutions. Therefore, it presents a number of new results concerning penalty methods in constrained optimization, existence of solutions in parametric optimization, well-posedness of vector minimization problems, and many other results obtained in the last ten years. The book is intended for mathematicians interested in optimization and applied functional analysis.

This book focuses on research methodologies that apply to business research, particularly for researchers and managers embarking thereon to support managerial decision-making in the industry. In doing so, the book's objective is to guide business researchers in identifying, defining, and applying rigorous academic methodologies that will enable them to formulate, design, and execute effective research that answers their specific management problems. Such guidance can empower organizational managers to understand that business research can contribute to practical solutions to actual problems experienced in the industry. In addition, by emphasizing the integrative nature between (1) academic research and (2) experienced industry problems, it becomes possible to foster an awareness of such research's potential impact on organizational performance management, sustainability, and resilience. With that, attention is given to narrowing the gap between theory and practice, which requires that fundamentals of scientific research be adhered to while maintaining the delicate balance between a practice-friendly guide to pragmatically sound and academically rigorous business research.

This volume reflects the theme of the INFORMS 2004 Meeting in Denver: Back to OR Roots. Emerging as a quantitative approach to problem-solving in World War II, our founders were physicists, mathematicians, and engineers who quickly found peace-time uses. It is fair to say that Operations Research (OR) was born in the same incubator as computer science, and it has spawned many new disciplines, such as systems engineering, health care management, and transportation science. Although people from many disciplines routinely use OR methods, many scientific researchers, engineers, and others do not understand basic OR tools and how they can help them. Disciplines ranging from finance to bioengineering are the beneficiaries of what we do - we take an interdisciplinary approach to problem-solving. Our strengths are modeling, analysis, and algorithm design. We provide a quanti- tive foundation for a broad spectrum of problems, from economics to medicine, from environmental control to sports, from e-commerce to computational - ometry. We are both producers and consumers because the mainstream of OR is in the interfaces. As part of this effort to recognize and extend OR roots in future probl- solving, we organized a set of tutorials designed for people who heard of the topic and want to decide whether to learn it. The 90 minutes was spent addre- ing the questions: What is this about, in a nutshell? Why is it important? Where can I learn more? In total, we had 14 tutorials, and eight of them are published here.

This book applies Multicriteria Decision Making (MCDM) tools and techniques to problems in location analysis. It begins with a generic model for MCDM and subsequently develops specific versions of the technique for particular location problems. Throughout the book, MCDM is understood to encompass all tools and techniques that choose or rank existing or feasible solutions, including discrete multi-attribute decision making (MADM) problems, which typically include an attribute table that specifies the consequences of each decision with regard to the given criteria, as well as multi-objective linear problems (MOLPs), which incorporate all objectives in a single optimization problem.  The book is organized as follows: the first four chapters introduce readers to the basic tools and techniques used in single-objective optimization, multicriteria decision making, location analysis, and other tools, such as statistical regression and geographical information systems. This is followed by ten chapters on model applications, each of which introduces readers to a specific location problem and applies one technique to solve it. The book is then wrapped up in a closing chapter that looks at the location process from a practitioner’s point of view.  This book is intended as a textbook for upper-undergraduate and master-level courses on location analysis. It will also benefit decision-makers who actually need to locate facilities. 

This addition to the ISOR series is a readable yet rigorous advanced text/reference on models and decision-making under uncertainty in the growing area of electricity markets. It is the first book to show how to use stochastic programming procedures to carry out in-depth analysis of decision-making models under uncertainty in these markets, including formulation issues and solution techniques. Due to the recent creation of futures markets for electricity in the past decade, much of the book is groundbreaking and reflects the most recent advances in operations research and its application in energy markets in general.

An electricity market is simply a system for effecting the purchase and sale of electricity using supply and demand to set the price. These markets are competitive, and have been a growing worldwide trend since the 1980 s, and coming to prominence (and notoriety) in 2001 when both the California electricity crisis and the Enron scandal occurred. Though the phenomenon of the electricity market grew from deregulation, and will likely continue to move toward increased openness, the situation in California resulted entirely from faulty regulation, particularly in modeling risk. The fact is, there are so many constraints to consider in modeling these markets, with so many possible points of failure, that it s a wonder it s taken this long for a rigorous text on stochastic programming to appear.

This is an advanced expository book on solving the most current and relevant short- and medium-term decision-making problems pertaining to producers, consumers, retailers, and market operators. Among its unique features: it addresses essentially all operational problems that arise in electricity markets; practical applications are developed up to the stage of working algorithms, coded in the GAMS (General Algebraic Modeling System) so that practitioners can put the book to use immediately; applications encompass areas in applied mathematics and business, as well as electrical and energy engineering; it presents a unified treatment of risk; it includes two chapters on wind power; and it provides an appropriate blend of theoretical background and practical applications. It can be used in graduate level courses (or Conejo s own PhD course in electricity markets) in a broad range of programs, whether economic, mathematic, or engineering, and will also be well-suited for the practitioner.

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