This is a thorough revision of the 2007 publication, and includes five new chapters and brings all existing chapters completely up to date. There have been many advances in hydropower and renewable technologies since the original publication, and Europe, and particularly Scandinavia, plan many more in the coming years. From a review of the original edition: "... it is important to note that the author deals well with his selected topics. ... I recommend this book to all readers who wish to learn more about the economics of hydroelectric power." (Amitrajeet A. Batabyal, Interfaces, Vol. 39 (1), January-February, 2009)

This addition to the ISOR series introduces complementarity models in a straightforward and approachable manner and uses them to carry out an in-depth analysis of energy markets, including formulation issues and solution techniques. In a nutshell, complementarity models generalize: a. optimization problems via their Karush-Kuhn-Tucker conditions b. on-cooperative games in which each player may be solving a separate but related optimization problem with potentially overall system constraints (e.g., market-clearing conditions) c. conomic and engineering problems that aren't specifically derived from optimization problems (e.g., spatial price equilibria) d. roblems in which both primal and dual variables (prices) appear in the original formulation (e.g., The National Energy Modeling System (NEMS) or its precursor, PIES). As such, complementarity models are a very general and flexible modeling format. A natural question is why concentrate on energy markets for this complementarity approach? s it turns out, energy or other markets that have game theoretic aspects are best modeled by complementarity problems. The reason is that the traditional perfect competition approach no longer applies due to deregulation and restructuring of these markets and thus the corresponding optimization problems may no longer hold. Also, in some instances it is important in the original model formulation to involve both primal variables (e.g., production) as well as dual variables (e.g., market prices) for public and private sector energy planning. Traditional optimization problems can not directly handle this mixing of primal and dual variables but complementarity models can and this makes them all that more effective for decision-makers.

This book presents real-world decision support systems, i.e., systems that have been running for some time and as such have been tested in real environments and complex situations; the cases are from various application domains and highlight the best practices in each stage of the system's life cycle, from the initial requirements analysis and design phases to the final stages of the project. Each chapter provides decision-makers with recommendations and insights into lessons learned so that failures can be avoided and successes repeated. For this reason unsuccessful cases, which at some point of their life cycle were deemed as failures for one reason or another, are also included. All decision support systems are presented in a constructive, coherent and deductive manner to enhance the learning effect. It complements the many works that focus on theoretical aspects or individual module design and development by offering 'good' and 'bad' practices when developing and using decision support systems. Combining high-quality research with real-world implementations, it is of interest to researchers and professionals in industry alike.

Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of dimensionality and so making practical solution of the resulting models intractable. In other cases, the system of interest is too complex to allow explicit specification of some of the MDP model parameters, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based algorithms have been developed to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function. Specific approaches include adaptive sampling, evolutionary policy iteration, evolutionary random policy search, and model reference adaptive search.
This substantially enlarged new edition reflects the latest developments in novel algorithms and their underpinning theories, and presents an updated account of the topics that have emerged since the publication of the first edition. Includes:
innovative material on MDPs, both in constrained settings and with uncertain transition properties;
game-theoretic method for solving MDPs;
theories for developing roll-out based algorithms; and
details of approximation stochastic annealing, a population-based on-line simulation-based algorithm.
The self-contained approach of this book will appeal not only to researchers in MDPs, stochastic modeling, and control, and simulation but will be a valuable source of tuition and reference for students of control and operations research.

Rapid Modelling and Quick Response presents new research developments in the fields of rapid modelling and quick response linked with performance improvements (based on lead time reduction, etc., as well as financial performance measures).

The papers and teaching cases in this book were presented at the second Rapid Modelling Conference: "Quick Response - Intersection of Theory and Practice." The main focus of this collection is the transfer of knowledge from theory to practice, providing the theoretical foundations for successful performance improvement.

This conference volume challenges the traditional notions of rapid modelling, and offers valuable contributions to the scientific communities of operations management, production management, supply chain management, industrial engineering and operations research.

Rapid Modelling and Quick Response will give the interested reader (researcher, as well as practitioner) a good overview of new developments in this field.

The emergence of high-performance computers and sophisticated software tech nology has led to significant advances in the development and application of operations research. In turn, the growing complexity of operations research models has posed an increasing challenge to computational methodology and computer technology. This volume focuses on recent advances in the fields of Computer Science and Operations Research, on the impact of technologi cal innovation on these disciplines, and on the close interaction between them. The papers cover many relevant topics: computational probability; design and analysis of algorithms; graphics; heuristic search and learning; knowledge-based systems; large-scale optimization; logic modeling and computation; modeling languages; parallel computation; simulation; and telecommunications. 1 This volume developed out of a conference held in Williamsburg, Virginia, January 5-7, 1994. It was sponsored by the Computer Science Technical Section of the Operations Research Society of America. The conference was attended by over 120 people from across the United States, and from many other countries. We would like to take this opportunity to thank the participants of the con ference, the authors, the anonymous referees, and the publisher for helping produce this volume. We express our special thanks to Bill Stewart and Ed Wasil for serving as Area Editors."

260 2 Crew Legalities and Crew Pairing Repair 264 3 Model and Mathematical Formulation 266 4 Solution Methodology 271 5 Computational Experiences 277 6 Conclusion 285 REFERENCES 286 10 THE USE OF OPTIMIZATION TO PERFORM AIR TRAFFIC FLOW MANAGEMENT Kenneth Lindsay, E. Andrew Boyd, George Booth, and Charles Harvey 287 1 Introduction 288 2 The Traffic Flow Management (TFM) Problem 289 3 Recent TFM Optimization Models 292 4 The Time Assignment Model (TAM) 302 5 Summary and Conclusions 307 REFERENCES 309 11 THE PROCESSES OF AIRLINE SYSTEM OPERATIONS CONTROL Seth C. Grandeau, Michael D. Clarke, and Dennis F.X. Mathaisel 312 1 Introduction 313 2 The Four Phases of Airline Schedule Development 315 The Airline Operations Control Center (OCC) 3 320 4 Analysis of Operational Problems 331 5 Areas For Improvement 352 6 Case Study: PT Garuda Indonesia Airlines 357 REFERENCES 368 12 THE COMPLEX CONFIGURATION MODEL Bruce W. Patty and Jim Diamond 370 1 Introduction 370 Problem Description 2 371 Problem Formulation 3 375 4 Model Implementation 379 ix Contents 383 5 Summary REFERENCES 383 13 INTEGRATED AIRLINE SCHEDULE PLANNING Cynthia Barnhart, Fang Lu, and Rajesh Shenoi 384 1 Introduction 385 2 Fleet Assignment and Crew Pairing Problems: Existing M- els and Algorithms 388 3 An Integrated Approximate Fleet Assignment and Crew Pa- ing Model 393 4 An Advanced Integrated Solution Approach 395 5 Case Study 396 6 Conclusions and Future Research Directions 399 REFERENCES 401 14 AIRLINE SCHEDULE PERTURBATION PROBLEM: LANDING AND TAKEOFF WITH

Scheduling and multicriteria optimisation theory have been subject, separately, to numerous studies. Since the last twenty years, multicriteria scheduling problems have been subject to a growing interest. However, a gap between multicriteria scheduling approaches and multicriteria optimisation field exits. This book is an attempt to collect the elementary of multicriteria optimisation theory and the basic models and algorithms of multicriteria scheduling. It is composed of numerous illustrations, algorithms and examples which may help the reader in understanding the presented concepts. This book covers general concepts such as Pareto optimality, complexity theory, and general method for multicriteria optimisation, as well as dedicated scheduling problems and algorithms: just-in-time scheduling, flexibility and robustness, single machine problems, parallel machine problems, shop problems, etc. The second edition contains revisions and new material.

This book presents a variety of advanced research papers in optimization and dynamics written by internationally recognized researchers in these fields. As an example of applying optimization in sport, it introduces a new method for finding the optimal bat sizes in baseball and softball. The book is divided into three parts: operations research, dynamics, and applications. The operations research section deals with the convergence of Newton-type iterations for solving nonlinear equations and optimum problems, the limiting properties of the Nash bargaining solution, the utilization of public goods, and optimizing lot sizes in the automobile industry. The topics in dynamics include special linear approximations of nonlinear systems, the dynamic behavior of industrial clusters, adaptive learning in oligopolies, periodicity in duopolies resulting from production constraints, and dynamic models of love affairs. The third part presents applications in the fields of reverse logistic network design for end-of-life wind turbines, fuzzy optimization of the structure of agricultural products, water resources management in the restoration plans for a lake and also in groundwater supplies. In addition it discusses applications in reliability engineering to find the optimal preventive replacement times of deteriorating equipment and using bargaining theory to determine the best maintenance contract. The diversity of the application areas clearly illustrates the usefulness of the theory and methodology of optimization and dynamics in solving practical problems.

"Focusing on turning an initial idea into a project with a successful outcome, this book fills a gap in current literature on project management and is thoroughly grounded in the latest research in this field. It emphasizes the practical application of decision making based on qualitative and judgmental information"--

Multiple Criteria Decision Making and its Applications to Economic Problems ties Multiple Criteria Decision Making (MCDM)/Multiple Objective Optimization (MO) and economics together. It describes how MCDM methods (goal programming) can be used in economics.

The volume consists of two parts. Part One of the book introduces the MCDM approaches. This first part, comprising Chapters 1-5, is basically an overview of MCDM methods that can most likely be used to address a wide range of economic problems. Readers looking for an in-depth discussion of multi-criteria analysis can grasp and become acquainted with the initial MCDM tools, language and definitions.

Part Two, which comprises Chapters 6-8, focuses on the theoretical core of the book. Thus in Chapter 6 an economic meaning is given to several key concepts on MCDM, such as ideal point, distance function, etc. It illustrates how Compromise Programming (CP) can support the standard premise of utility optimisation in economics as well as how it is capable of approximating the standard utility optimum when the decision-makers' preferences are incompletely specified. Chapter 7 deals entirely with production analysis. The main characteristic throughout the Chapter refers to a standard joint production scenario, analysed from the point of view of MCDM schemes. Chapter 8 focuses on the utility specification problem in the n-arguments space within a risk aversion context. A link between Arrows' risk aversion coefficient and CP utility permits this task.

The book is intended for postgraduate students and researchers in economics with an OR/MS orientation or in OR/MS with an economic orientation. In short, it attempts to fruitfully link economics and MCDM.

In the two decades since its inception by L. Zadeh, the theory of fuzzy sets has matured into a wide-ranging collection of concepts, models, and tech niques for dealing with complex phenomena which do not lend themselves to analysis by classical methods based on probability theory and bivalent logic. Nevertheless, a question which is frequently raised by the skeptics is: Are there, in fact, any significant problem areas in which the use of the theory of fuzzy sets leads to results which could not be obtained by classical methods? The approximately 5000 publications in this area, which are scattered over many areas such as artificial intelligence, computer science, control engineering, decision making, logic, operations research, pattern recognition, robotics and others, provide an affirmative answer to this question. In spite of the large number of publications, good and comprehensive textbooks which could facilitate the access of newcomers to this area and support teaching were missing until recently. To help to close this gap and to provide a textbook for courses in fuzzy set theory which can also be used as an introduction to this field, the first volume ofthis book was published in 1985 Zimmermann 1985 b]. This volume tried to cover fuzzy set theory and its applications as extensively as possible. Applications could, therefore, only be described to a limited extent and not very detailed."

This book presents recent work in the physics and economics of management through the developmental theory and practice of management science/operations research (MS/OR) that goes beyond the author s earlier book on the same subject. (Volume 125 in Springer s MS/OR series) This current work makes a useful contribution to the next-generation discrete system of science and management for a better society.

The scope of the book is focused on the science and management of the 3M&I Time system in the discrete world, where that system is a complex class consisting of humans, material/machine, money and time. The system is treated by a stochastic/intelligence (medium) approach. The science of this system is the interdisciplinary science of physics, management, economics and related fields and is based on synthesis and intelligence in the new discrete world. Here, this domain is referred to as a discrete and complex science (of physics and economics) in industry and society.

Another domain, which is referred to as higher management science and operations in this book, stems from the change in traditional management to higher management driven by the power of information and communications technology (ICT) in the cloud computing/global age. This domain exists to meet the needs of logic for real-time/systematic decisions and management in a changeable, speeded-up, and risk environment."

Entropy optimization is a useful combination of classical engineering theory (entropy) with mathematical optimization. The resulting entropy optimization models have proved their usefulness with successful applications in areas such as image reconstruction, pattern recognition, statistical inference, queuing theory, spectral analysis, statistical mechanics, transportation planning, urban and regional planning, input-output analysis, portfolio investment, information analysis, and linear and nonlinear programming. While entropy optimization has been used in different fields, a good number of applicable solution methods have been loosely constructed without sufficient mathematical treatment. A systematic presentation with proper mathematical treatment of this material is needed by practitioners and researchers alike in all application areas. The purpose of this book is to meet this need. Entropy Optimization and Mathematical Programming offers perspectives that meet the needs of diverse user communities so that the users can apply entropy optimization techniques with complete comfort and ease. With this consideration, the authors focus on the entropy optimization problems in finite dimensional Euclidean space such that only some basic familiarity with optimization is required of the reader.

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.

This book presents an analysis of the dynamics and the complexity of new product development projects which are organized according to the concept of concurrent engineering. The approach of the authors includes both a theoretical and an empirical treatment of the topic, based on the theory of design structure matrices. Readers will discover diverse perspectives and mathematical models, as well as an extensive discussion of two case studies.

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.

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.

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.

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 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)."

For first courses in operations research, operations management. Covers a broad range of optimization techniques, including linear programming, network flows, integer/combinational optimization, and nonlinear programming. Emphasizes the importance of modeling and problem formulation, this text teaches students how to apply algorithms to real-world problems to arrive at optimal solutions. Visit the author-maintained web site athttp: //comp.uark.edu/ rrardin/oorboo