Scatter Search

Operations Research/Computer Science Interfaces Series

Book 24
Springer Science & Business Media
Free sample

The book Scatter Search by Manuel Laguna and Rafael Mart! represents a long-awaited "missing link" in the literature of evolutionary methods. Scatter Search (SS)-together with its generalized form called Path Relinking-constitutes the only evolutionary approach that embraces a collection of principles from Tabu Search (TS), an approach popularly regarded to be divorced from evolutionary procedures. The TS perspective, which is responsible for introducing adaptive memory strategies into the metaheuristic literature (at purposeful level beyond simple inheritance mechanisms), may at first seem to be at odds with population-based approaches. Yet this perspective equips SS with a remarkably effective foundation for solving a wide range of practical problems. The successes documented by Scatter Search come not so much from the adoption of adaptive memory in the range of ways proposed in Tabu Search (except where, as often happens, SS is advantageously coupled with TS), but from the use of strategic ideas initially proposed for exploiting adaptive memory, which blend harmoniously with the structure of Scatter Search. From a historical perspective, the dedicated use of heuristic strategies both to guide the process of combining solutions and to enhance the quality of offspring has been heralded as a key innovation in evolutionary methods, giving rise to what are sometimes called "hybrid" (or "memetic") evolutionary procedures. The underlying processes have been introduced into the mainstream of evolutionary methods (such as genetic algorithms, for example) by a series of gradual steps beginning in the late 1980s.
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Additional Information

Publisher
Springer Science & Business Media
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Published on
Dec 6, 2012
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Pages
291
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ISBN
9781461503378
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Language
English
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Genres
Business & Economics / Operations Research
Computers / Intelligence (AI) & Semantics
Mathematics / Applied
Mathematics / Calculus
Mathematics / Functional Analysis
Mathematics / General
Mathematics / Optimization
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Content Protection
This content is DRM protected.
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Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization. Since it became possible to analyze random systems using computers, scientists and engineers have sought the means to optimize systems using simulation models. Only recently, however, has this objective had success in practice. Cutting-edge work in computational operations research, including non-linear programming (simultaneous perturbation), dynamic programming (reinforcement learning), and game theory (learning automata) has made it possible to use simulation in conjunction with optimization techniques. As a result, this research has given simulation added dimensions and power that it did not have in the recent past.

The book's objective is two-fold: (1) It examines the mathematical governing principles of simulation-based optimization, thereby providing the reader with the ability to model relevant real-life problems using these techniques. (2) It outlines the computational technology underlying these methods. Taken together these two aspects demonstrate that the mathematical and computational methods discussed in this book do work.
Broadly speaking, the book has two parts: (1) parametric (static) optimization and (2) control (dynamic) optimization. Some of the book's special features are:
*An accessible introduction to reinforcement learning and parametric-optimization techniques.
*A step-by-step description of several algorithms of simulation-based optimization.
*A clear and simple introduction to the methodology of neural networks.
*A gentle introduction to convergence analysis of some of the methods enumerated above.
*Computer programs for many algorithms of simulation-based optimization.

This book is written for students and researchers in the fields of engineering (electrical, industrial and computer), computer science, operations research, management science, and applied mathematics.
Welcome to ANALYZE, designed to provide computer assistance for analyzing linear programs and their solutions. Chapter 1 gives an overview of ANALYZE and how to install it. It also describes how to get started and how to obtain further documentation and help on-line. Chapter 2 reviews the forms of linear programming models and describes the syntax of a model. One of the routine, but important, functions of ANALYZE is to enable convenient access to rows and columns in the matrix by conditional delineation. Chapter 3 illustrates simple queries, like DISPLAY, LIST, and PICTURE. This chapter also introduces the SUBMAT command level to define any submatrix by an arbitrary sequence of additions, deletions and reversals. Syntactic explanations and a schema view are also illustrated. Chapter 4 goes through some elementary exercises to demonstrate computer assisted analysis and introduce additional conventions of the ANALYZE language. Besides simple queries, it demonstrates the INTERPRT command, which automates the analysis process and gives English explanations of results. The last 2 exercises are diagnoses of elementary infeasible instances of a particular model. Chapter 5 progresses to some advanced uses of ANALYZE. The first is blocking to obtain macro views of the model and for finding embedded substructures, like a netform. The second is showing rates of substitution described by the basic equations. Then, the use of the REDUCE and BASIS commands are illustrated for a variety of applications, including solution analysis, infeasibility diagnosis, and redundancy detection.
Most textbooks on business process management focus on either the nuts and bolts of computer simulation or the managerial aspects of business processes. Covering both technical and managerial aspects of business process management, Business Process Modeling, Simulation and Design, Second Edition presents the tools to design effective business processes and the management techniques to operate them efficiently.

New to the Second Edition

Three completely revised chapters that incorporate ExtendSim 8 An introduction to simulation A chapter on business process analytics

Developed from the authors’ many years of teaching process design and simulation courses, the text provides students with a thorough understanding of numerous analytical tools that can be used to model, analyze, design, manage, and improve business processes. It covers a wide range of approaches, including discrete event simulation, graphical flowcharting tools, deterministic models for cycle time analysis and capacity decisions, analytical queuing methods, and data mining. Unlike other operations management books, this one emphasizes user-friendly simulation software as well as business processes, rather than only manufacturing processes or general operations management problems.

Taking an analytical modeling approach to process design, this book illustrates the power of simulation modeling as a vehicle for analyzing and designing business processes. It teaches how to apply process simulation and discusses the managerial implications of redesigning processes. The ExtendSim software is available online and ancillaries are available for instructors.

In Decision Modelling And Information Systems: The Information Value Chain the authors explain the interrelationships between the decision support, decision modelling, and information systems. The authors borrow from Porter's value chain concept originally set out in the organizational context and apply it to a corporate IS context. Thus data, information and knowledge is seen to be the progressive value added process leading to business intelligence. The book captures key issues that are of central interest to decision support researchers, professionals, and students. The book sets out an interdisciplinary and contemporary view of Decision Support System (DSS).

The first two parts of the book focus on the interdisciplinary decision support framework, in which mathematical programming (optimization) is taken as the inference engine. The role of business analytics and its relationship with recent developments in organisational theory, decision modelling, information systems and information technology are considered in depth. Part three of the book includes a carefully chosen selection of invited contributions from internationally-known researchers. These contributions are thought-provoking and cover key decision modelling and information systems issues.

These chapters include: Arthur Geoffrion on restoring transparency to computational solutions, Bill Inmon on the concept of the corporate information factory, Louis Ma and Efraim Turban on strategic information systems, and Erik Thomsen on information impact and its relationship to the value of information technology.

The final part of the book covers contemporary developments in the related area of business intelligence considered within an organizational context. The topics cover computing delivered across the web, management decision-making, and socio-economic challenges that lie ahead. It is now well accepted that globalisation and the impact of digital economy are profound; and the role of e-business and the delivery of decision models (business analytics) across the net lead to a challenging business environment. In this dynamic setting, decision support is one of the few interdisciplinary frameworks that can be rapidly adopted and deployed to so that businesses can survive and prosper by meeting these new challenges.

Artificial neural networks (ANNs) offer a general framework for representing non-linear mappings from several input variables to several output variables, and they can be considered as an extension of the many conventional mapping techniques. In addition to many considerations on their biological foundations and their really wide spectrum of applications, constructing appropriate ANNs can be seen as a really hard problem. A distinguished task in building ANNs is the tuning of a set of parameters known as weights. This will be the main focus of the present book. The trained ANNs can be later used in classification (or recognition) problems, where the ANN outputs represent categories, or in prediction (approximation) problems, where the outputs represent continuous variables.

METAHEURISTIC PROCEDURES FOR TRAINING NEURAL NETWORKS provides successful implementations of metaheuristic methods for neural network training. Moreover, the basic principles and fundamental ideas given in the book will allow the readers to create successful training methods on their own. Apart from Chapter 1, in which classical training methods are reviewed for the sake of the book’s completeness, we have classified the chapters in three main categories. The first one is devoted to local search based methods, in which we include Simulated Annealing, Tabu Search, and Variable Neighborhood Search. The second part of the book presents the most effective population based methods, such as Estimation Distribution algorithms, Scatter Search, and Genetic Algorithms. Finally, the third part includes other advanced techniques, such as Ant Colony Optimization, Co-evolutionary methods, GRASP, and Memetic algorithms. All these methods have been shown to work out high quality solutions in a wide range of hard optimization problems. However, the book's objective is engineered to provide a broad coverage of the concepts, methods, and tools of this important area of ANNs within the realm of continuous optimization.

Most textbooks on business process management focus on either the nuts and bolts of computer simulation or the managerial aspects of business processes. Covering both technical and managerial aspects of business process management, Business Process Modeling, Simulation and Design, Second Edition presents the tools to design effective business processes and the management techniques to operate them efficiently.

New to the Second Edition

Three completely revised chapters that incorporate ExtendSim 8 An introduction to simulation A chapter on business process analytics

Developed from the authors’ many years of teaching process design and simulation courses, the text provides students with a thorough understanding of numerous analytical tools that can be used to model, analyze, design, manage, and improve business processes. It covers a wide range of approaches, including discrete event simulation, graphical flowcharting tools, deterministic models for cycle time analysis and capacity decisions, analytical queuing methods, and data mining. Unlike other operations management books, this one emphasizes user-friendly simulation software as well as business processes, rather than only manufacturing processes or general operations management problems.

Taking an analytical modeling approach to process design, this book illustrates the power of simulation modeling as a vehicle for analyzing and designing business processes. It teaches how to apply process simulation and discusses the managerial implications of redesigning processes. The ExtendSim software is available online and ancillaries are available for instructors.

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