Simulation-Based Optimization

Operations Research/Computer Science Interfaces Series

Book 25
Springer Science & Business Media
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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.
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Additional Information

Publisher
Springer Science & Business Media
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Published on
Mar 14, 2013
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Pages
554
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ISBN
9781475737660
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Best For
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Language
English
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Genres
Business & Economics / Operations Research
Language Arts & Disciplines / Library & Information Science / General
Mathematics / Applied
Mathematics / Calculus
Mathematics / Functional Analysis
Mathematics / General
Mathematics / Optimization
Science / System Theory
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Content Protection
This content is DRM protected.
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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.

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.
In the years following her role as the lead author of the international bestseller, Limits to Growth—the first book to show the consequences of unchecked growth on a finite planet— Donella Meadows remained a pioneer of environmental and social analysis until her untimely death in 2001.

Thinking in Systems, is a concise and crucial book offering insight for problem solving on scales ranging from the personal to the global. Edited by the Sustainability Institute’s Diana Wright, this essential primer brings systems thinking out of the realm of computers and equations and into the tangible world, showing readers how to develop the systems-thinking skills that thought leaders across the globe consider critical for 21st-century life.

Some of the biggest problems facing the world—war, hunger, poverty, and environmental degradation—are essentially system failures. They cannot be solved by fixing one piece in isolation from the others, because even seemingly minor details have enormous power to undermine the best efforts of too-narrow thinking.

While readers will learn the conceptual tools and methods of systems thinking, the heart of the book is grander than methodology. Donella Meadows was known as much for nurturing positive outcomes as she was for delving into the science behind global dilemmas. She reminds readers to pay attention to what is important, not just what is quantifiable, to stay humble, and to stay a learner.

In a world growing ever more complicated, crowded, and interdependent, Thinking in Systems helps readers avoid confusion and helplessness, the first step toward finding proactive and effective solutions.

Despite differing origins, constraint programming and mathematical programming are beginning to merge. Constraint programming has grown out of the logic programming community as part of an effort to embed constraints in a programming language. Mathematical programming, a much older field, is rooted in the mathematics of optimization. Because these two areas have complementary strengths, there are ongoing efforts to integrate the two.Constraint and Integer Programming presents some of the basic ideas of constraint programming and mathematical programming, explores approaches to integration, brings us up to date on heuristic methods, and attempts to discern future directions in this fast-moving field.
Constraint and Integer Programming is organized as follows: Chapter 1 is a high level overview of the main concepts of Constraint Programming (CP) and Integer Programming (IP) used in the book. Chapter 2 informally introduces integration methods describing and classifying the main works in the field. Chapter 3 outlines a unifying framework which involves the main concepts of CP and IP, underlining similarities and differences, and stating the basis for possible integrations. Chapter 4 describes global constraints as a vehicle for integrating IP concepts in CP in a transparent way for the user. Chapter 5 presents various ways to integrate relaxations in Constraint Programming focussing on global constraints. Chapter 6 presents hybrid solvers and Chapter 7 outlines Column Generation and its integration in Constraint Programming. Chapter 8 examines randomization and problem structure as a basis for understanding the intrinsic difficulty of the combinatorial problems. Chapter 9 studies the use of local search and meta-heuristics in CP. Chapter 10 is devoted to open perspectives and future directions.
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques – especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms.

Key features of this revised and improved Second Edition include:

· Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search and meta-heuristics (simulated annealing, tabu search, and genetic algorithms)

· Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for discounted, average, and total reward performance metrics

· An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: Q-Learning, SARSA, and R-SMART algorithms, and policy search, via API, Q-P-Learning, actor-critics, and learning automata

· A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online) and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations

Themed around three areas in separate sets of chapters – Static Simulation Optimization, Reinforcement Learning and Convergence Analysis – this book is written for researchers and students in the fields of engineering (industrial, systems, electrical and computer), operations research, computer science and applied mathematics.

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