The University of Genoa - Ohio State University Joint Conference on New Trends in Systems Theory was held at the Badia di S. Andrea in Genoa on July 9-11, 1990. This Proceedings volume contains articles based on two of the three Plenary talks and most of the shorter presentations. The papers are arranged by author, and no attempt has been made to organize them by topic. We would like to thank the members of the Scientific Committee and of the Program Committee, the speakers and authors, and everyone who attended the conference. Approximately 120 researchers and students from all over the world visited Genoa for the meeting, representing a wide spectrum of areas in pure and applied control and systems theory. The success of the conference depended on their high level of scientific and engineering expertise, not to mention their enthusiasm. The Conference on New Trends in Systems Theory would not have been possible without the help of a great many institutions and people. We would like to thank the University of Genoa, particularly Professor Enrico Beltrametti, and the Ohio State University's Columbian Quincentenary Committee led by Professor Christian Zacher, for encouragement and financial assistance. The University of Genoa Mathematics Department and Communication, Computer and System Sciences Department supplied assistance and technical help. The staff of the Consorzio Genova Ricerche, particularly Ms. Piera Ponta and Ms. Camilla Marconi, worked diligently over many months and especially during the conference itself to insure a smooth and enjoyable meeting.

This book offers a bridge between our current understanding of supply chain risk in practice and theory, and the monumental shifts caused by the emergence of the fourth industrial revolution. Supply chain risk and its management have experienced significant attention in scholarship and practice over the past twenty years. Our understanding of supply chain risk and its many facets, such as uncertainty and vulnerability, has expanded beyond utilizing approaches such as deploying inventory to buffer the initial effects of disruptions. Even with our increased knowledge of supply chain risk, being in the era of lean supply chain practices, digitally managed global supply chains, and closely interconnected networks, firms are exposed as ever to supply chain uncertainties that can damage, or even destroy, their ability to compete in the marketplace. The book acknowledges the criticality of big data analytics in Supply Chain Risk Management (SCRM) processes and provides appropriate tools and approaches for creating robust SCRM processes. Revisiting Supply Chain Risk presents a state-of-the-art look at SCRM through current research and philosophical thought. It is divided into six sections that highlight established themes, as well as provide new insights to developing areas of inquiry and contexts on the topic. Section 1 examines the first step in managing supply chain risk, risk assessment. The chapters in Section 2 encompass resiliency in supply chains, while Section 3 looks at relational and behavioral perspectives from varying units of analysis including consortiums, teams and decision makers. Section 4 focuses on examining supply chain risk in the contexts of sustainability and innovation. Section 5 provides insight on emerging typologies and taxonomies for classifying supply chain risk. The book concludes with Section 6, featuring illustrative case studies as real-world examples in assessing and managing supply chain risk.

This volume presents new concepts and methods in Air Traffic Management, in particular: Collaborative Decision Making, as it incorporates for the first time airline companies in the management process; Congestion Pricing, as many part of the systems are and will remain saturated, hence only leveling of demand can contribute to global efficiency; Flow Management Methods, as the most important tools in planning and analysis; Models of Controller-Pilot Interaction, as deregulation increases the workload of this communication; Weather Forecast, as airport capacity is strongly affected by weather conditions.

I ?rst met Jingqiao when he had just commenced his PhD research in evolutionary algorithms with Arthur Sanderson at Rensselaer. Jingqiao's goals then were the investigation and development of a novel class of se- adaptivedi?erentialevolutionalgorithms, later calledJADE. I had remarked to Jingqiao then that Arthur always appreciated strong theoretical foun- tions in his research, so Jingqiao's prior mathematically rigorous work in communications systems would be very useful experience. Later in 2007, whenJingqiaohadcompletedmostofthetheoreticalandinitialexperimental work on JADE, I invited him to spend a year at GE Global Research where he applied his developments to several interesting and important real-world problems. Most evolutionary algorithm conferences usually have their share of in- vative algorithm oriented papers which seek to best the state of the art - gorithms. The best algorithms of a time-frame create a foundation for a new generationof innovativealgorithms, and so on, fostering a meta-evolutionary search for superior evolutionary algorithms. In the past two decades, during whichinterest andresearchin evolutionaryalgorithmshavegrownworldwide by leaps and bounds, engaging the curiosity of researchers and practitioners frommanydiversescienceandtechnologycommunities, developingstand-out algorithms is getting progressively harder.

Metaheuristics have been a very active research topic for more than two decades. During this time many new metaheuristic strategies have been devised, they have been experimentally tested and improved on challenging benchmark problems, and they have proven to be important tools for tackling optimization tasks in a large number of practical applications. In other words, metaheuristics are nowadays established as one of the main search paradigms for tackling computationally hard problems. Still, there are a large number of research challenges in the area of metaheuristics. These challenges range from more fundamental questions on theoretical properties and performance guarantees, empirical algorithm analysis, the effective configuration of metaheuristic algorithms, approaches to combine metaheuristics with other algorithmic techniques, towards extending the available techniques to tackle ever more challenging problems. This edited volume grew out of the contributions presented at the ninth Metaheuristics International Conference that was held in Udine, Italy, 25-28 July 2011. The conference comprised 117 presentations of peer-reviewed contributions and 3 invited talks, and it has been attended by 169 delegates. The chapters that are collected in this book exemplify contributions to several of the research directions outlined above.

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.

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.

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.

Strategic management deals with a very broad set of questions, issues, and problems associated with general management. This book introduces research carried out in several frontier areas of strategic management: Strategy, Information Systems, R&D/Engineering, Marketing, Ethics, Governance, Issues Management and New Ventures.
Especially noteworthy is its major emphasis on extending the purview and scope of strategic management by the integration of established and emerging functional areas into the mainstream of the strategic management process. The scope of areas covered ranges from the corporate to public sector, and from corporate to new ventures. Academics and practitioners interested in the ramifications of latest developments in the theory and practice of strategic management will find this book of interest.

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 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 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.

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.

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.