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Applying Experimental Design And Regression Splines To High Dimensional Continuous State Stochastic Dynamic Programming
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Book Synopsis Applying Experimental Design and Regression Splines to High-dimensional Continuous-state Stochastic Dynamic Programming by : Victoria Chung-Ping Chen
Download or read book Applying Experimental Design and Regression Splines to High-dimensional Continuous-state Stochastic Dynamic Programming written by Victoria Chung-Ping Chen and published by . This book was released on 1993 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :Kit-Yee Daisy Fan Publisher :Ann Arbor, Mich. : University Microfilms International ISBN 13 : Total Pages :282 pages Book Rating :4.E/5 ( download)
Book Synopsis Regression Dynamic Programming for High-dimensional Continuous-state Problems with Application to Stochastic Multiple-reservoir Optimization by : Kit-Yee Daisy Fan
Download or read book Regression Dynamic Programming for High-dimensional Continuous-state Problems with Application to Stochastic Multiple-reservoir Optimization written by Kit-Yee Daisy Fan and published by Ann Arbor, Mich. : University Microfilms International. This book was released on 2002 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Handbook of Industrial Engineering by : Gavriel Salvendy
Download or read book Handbook of Industrial Engineering written by Gavriel Salvendy and published by John Wiley & Sons. This book was released on 2001-05-25 with total page 2846 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unrivaled coverage of a broad spectrum of industrial engineering concepts and applications The Handbook of Industrial Engineering, Third Edition contains a vast array of timely and useful methodologies for achieving increased productivity, quality, and competitiveness and improving the quality of working life in manufacturing and service industries. This astoundingly comprehensive resource also provides a cohesive structure to the discipline of industrial engineering with four major classifications: technology; performance improvement management; management, planning, and design control; and decision-making methods. Completely updated and expanded to reflect nearly a decade of important developments in the field, this Third Edition features a wealth of new information on project management, supply-chain management and logistics, and systems related to service industries. Other important features of this essential reference include: * More than 1,000 helpful tables, graphs, figures, and formulas * Step-by-step descriptions of hundreds of problem-solving methodologies * Hundreds of clear, easy-to-follow application examples * Contributions from 176 accomplished international professionals with diverse training and affiliations * More than 4,000 citations for further reading The Handbook of Industrial Engineering, Third Edition is an immensely useful one-stop resource for industrial engineers and technical support personnel in corporations of any size; continuous process and discrete part manufacturing industries; and all types of service industries, from healthcare to hospitality, from retailing to finance. Of related interest . . . HANDBOOK OF HUMAN FACTORS AND ERGONOMICS, Second Edition Edited by Gavriel Salvendy (0-471-11690-4) 2,165 pages 60 chapters "A comprehensive guide that contains practical knowledge and technical background on virtually all aspects of physical, cognitive, and social ergonomics. As such, it can be a valuable source of information for any individual or organization committed to providing competitive, high-quality products and safe, productive work environments."-John F. Smith Jr., Chairman of the Board, Chief Executive Officer and President, General Motors Corporation (From the Foreword)
Book Synopsis Advances in Imaging and Electron Physics by : Peter W. Hawkes
Download or read book Advances in Imaging and Electron Physics written by Peter W. Hawkes and published by Elsevier. This book was released on 2011-07-29 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Imaging and Electron Physics merges two long-running serials-Advances in Electronics and Electron Physics and Advances in Optical and Electron Microscopy. This series features extended articles on the physics of electron devices (especially semiconductor devices), particle optics at high and low energies, microlithography, image science and digital image processing, electromagnetic wave propagation, electron microscopy, and the computing methods used in all these domains.
Book Synopsis Parallel Algorithms of Continuous State and Control Stochastic Dynamic Programming Applied to Multi-reservoir Management by : Elizabeth Allen Eschenbach
Download or read book Parallel Algorithms of Continuous State and Control Stochastic Dynamic Programming Applied to Multi-reservoir Management written by Elizabeth Allen Eschenbach and published by . This book was released on 1994 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Neural Approximations for Optimal Control and Decision by : Riccardo Zoppoli
Download or read book Neural Approximations for Optimal Control and Decision written by Riccardo Zoppoli and published by Springer Nature. This book was released on 2019-12-17 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Approximations for Optimal Control and Decision provides a comprehensive methodology for the approximate solution of functional optimization problems using neural networks and other nonlinear approximators where the use of traditional optimal control tools is prohibited by complicating factors like non-Gaussian noise, strong nonlinearities, large dimension of state and control vectors, etc. Features of the text include: • a general functional optimization framework; • thorough illustration of recent theoretical insights into the approximate solutions of complex functional optimization problems; • comparison of classical and neural-network based methods of approximate solution; • bounds to the errors of approximate solutions; • solution algorithms for optimal control and decision in deterministic or stochastic environments with perfect or imperfect state measurements over a finite or infinite time horizon and with one decision maker or several; • applications of current interest: routing in communications networks, traffic control, water resource management, etc.; and • numerous, numerically detailed examples. The authors’ diverse backgrounds in systems and control theory, approximation theory, machine learning, and operations research lend the book a range of expertise and subject matter appealing to academics and graduate students in any of those disciplines together with computer science and other areas of engineering.
Book Synopsis Statistics in Industry by : Ravindra Khattree
Download or read book Statistics in Industry written by Ravindra Khattree and published by Gulf Professional Publishing. This book was released on 2003-07-18 with total page 1224 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents an exposition of topics in industrial statistics. It serves as a reference for researchers in industrial statistics/industrial engineering and a source of information for practicing statisticians/industrial engineers. A variety of topics in the areas of industrial process monitoring, industrial experimentation, industrial modelling and data analysis are covered and are authored by leading researchers or practitioners in the particular specialized topic. Targeting the audiences of researchers in academia as well as practitioners and consultants in industry, the book provides comprehensive accounts of the relevant topics. In addition, whenever applicable ample data analytic illustrations are provided with the help of real world data.
Book Synopsis Handbook of Markov Decision Processes by : Eugene A. Feinberg
Download or read book Handbook of Markov Decision Processes written by Eugene A. Feinberg and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: Eugene A. Feinberg Adam Shwartz This volume deals with the theory of Markov Decision Processes (MDPs) and their applications. Each chapter was written by a leading expert in the re spective area. The papers cover major research areas and methodologies, and discuss open questions and future research directions. The papers can be read independently, with the basic notation and concepts ofSection 1.2. Most chap ters should be accessible by graduate or advanced undergraduate students in fields of operations research, electrical engineering, and computer science. 1.1 AN OVERVIEW OF MARKOV DECISION PROCESSES The theory of Markov Decision Processes-also known under several other names including sequential stochastic optimization, discrete-time stochastic control, and stochastic dynamic programming-studiessequential optimization ofdiscrete time stochastic systems. The basic object is a discrete-time stochas tic system whose transition mechanism can be controlled over time. Each control policy defines the stochastic process and values of objective functions associated with this process. The goal is to select a "good" control policy. In real life, decisions that humans and computers make on all levels usually have two types ofimpacts: (i) they cost orsavetime, money, or other resources, or they bring revenues, as well as (ii) they have an impact on the future, by influencing the dynamics. In many situations, decisions with the largest immediate profit may not be good in view offuture events. MDPs model this paradigm and provide results on the structure and existence of good policies and on methods for their calculation.
Book Synopsis Operations Research in Space and Air by : Tito A. Ciriani
Download or read book Operations Research in Space and Air written by Tito A. Ciriani and published by Springer Science & Business Media. This book was released on 2013-04-18 with total page 453 pages. Available in PDF, EPUB and Kindle. Book excerpt: Operations Research in Space and Air is a selection of papers reflecting the experience and expertise of international OR consulting companies and academic groups. The global market and competition play a crucial part in the decision making processes within the Space and Air industries and this book gives practical examples of how advanced applications can be used by Space and Air industry management. The material within the book provides both the basic background for the novice modeler and a useful reference for experienced modelers. Students, researchers and OR practitioners will appreciate the details of the modeling techniques, the processes that have been implemented and the computational results that demonstrate the benefits in applying OR in the Space and Airline industries. Advances in PC and Workstations technology, in optimiza tion engines and in modeling techniques now enable solving problems, never before attained by Operations Research. In recent years the Ital ian OR Society (AfRO, www. airo. org) has organized annual forums for researchers and practitioners to meet together to present and dis cuss the various scientific and technical OR achievements. The OR in Space 8 Air session of AfR02001 and AfR02002 Conferences, together with optimization tools' applications, presented recent results achieved by Alenia Spazio S. p. A. (Turin), Alitalia, Milan Polytechnic and Turin Polytechinc. With additional contributions from academia and indus try they have enabled us to capture, in print, today's 'state-of-the-art' optimization and data mining solutions.
Book Synopsis Reinforcement Learning and Stochastic Optimization by : Warren B. Powell
Download or read book Reinforcement Learning and Stochastic Optimization written by Warren B. Powell and published by John Wiley & Sons. This book was released on 2022-04-25 with total page 1090 pages. Available in PDF, EPUB and Kindle. Book excerpt: REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of “decision, information, decision, information,” are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a "diary problem" that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.
Author :Louis Anthony Cox Jr. Publisher :Springer Science & Business Media ISBN 13 :1461508479 Total Pages :564 pages Book Rating :4.4/5 (615 download)
Book Synopsis Risk Analysis Foundations, Models, and Methods by : Louis Anthony Cox Jr.
Download or read book Risk Analysis Foundations, Models, and Methods written by Louis Anthony Cox Jr. and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: Risk Analysis: Foundations, Models, and Methods fully addresses the questions of "What is health risk analysis?" and "How can its potentialities be developed to be most valuable to public health decision-makers and other health risk managers?" Risk analysis provides methods and principles for answering these questions. It is divided into methods for assessing, communicating, and managing health risks. Risk assessment quantitatively estimates the health risks to individuals and to groups from hazardous exposures and from the decisions or activities that create them. It applies specialized models and methods to quantify likely exposures and their resulting health risks. Its goal is to produce information to improve decisions. It does this by relating alternative decisions to their probable consequences and by identifying those decisions that make preferred outcomes more likely. Health risk assessment draws on explicit engineering, biomathematical, and statistical consequence models to describe or simulate the causal relations between actions and their probable effects on health. Risk communication characterizes and presents information about health risks and uncertainties to decision-makers and stakeholders. Risk management applies principles for choosing among alternative decision alternatives or actions that affect exposure, health risks, or their consequences.
Book Synopsis Statistics-based Approaches to Stochastic Optimal Control Problems by : Aihong Wen
Download or read book Statistics-based Approaches to Stochastic Optimal Control Problems written by Aihong Wen and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation develops practical approaches to stochastic optimal control problems in the absence of Linear system transition functions, Quadratic cost functions, and/or Gaussian random disturbances (LQG hypotheses). In such type of problems, an analytical solution is impossible and numerical synthesis techniques have to be applied. The "classic" algorithms existing in the literature suffer from the problem of the " curse of dimensionality," an exponential increase of computation time and memory requirements as the dimension of the problem grows. The statistics-based numerical approaches are presented as the main tools for mitigating the "curse of dimensionality" phenomenon. Two approaches are explored: (A) stochastic dynamic programming (SDP), which approximates the future value functions and solves the recursion relation backwards in stages, and (B) stochastic gradient (SG), which approximates the control functions by linear combinations of certain basis functions containing free parameters, and optimizes the parameters through iterations over a sequence of realizations of the system's random variables. The research presented in this dissertation views the approximation of future value functions in SDP and the approximation of control functions in SG via a computer experiments perspective, and integrates statistical methods from the area of design and analysis of computer experiments (DACE) into SDP and SG approaches to enable numerical solution to large SOC problems. Recent developments in DACE make it possible to approximate high-dimensional, complex input-output relationships with moderate computation time and memory requirements. A statistical perspective of future value function approximation in high-dimensional, continuous-state SDP was first presented using orthogonal array (OA) experimental designs and multivariate adaptive regression splines (MARS) statistical models. This work utilizes number theoretic methods (NTMs) and artificial neural networks (ANNs) as alternatives to OAs and MARS respectively, and introduces the statistical perspective to SG approach. Comparisons consider the differences in methodological objectives, model accuracy and numerical solutions are presented. Three problems are tested: a nine-dimensional inventory forecasting problem, an eight-dimensional water reservoir network management problem, and a thirty-dimensional water reservoir network management problem. This last application is the largest water reservoir problem solved in the literature.
Book Synopsis Operations Research Proceedings 2003 by : Dino Ahr
Download or read book Operations Research Proceedings 2003 written by Dino Ahr and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains a selection of papers referring to lectures presented at the symposium "Operations Research 2003" (OR03) held at the Ruprecht Karls-Universitiit Heidelberg, September 3 - 5, 2003. This international con ference took place under the auspices of the German Operations Research So ciety (GOR) and of Dr. Erwin Teufel, prime minister of Baden-Wurttemberg. The symposium had about 500 participants from countries all over the world. It attracted academians and practitioners working in various field of Opera tions Research and provided them with the most recent advances in Opera tions Research and related areas in Economics, Mathematics, and Computer Science. The program consisted of 4 plenary and 13 semi-plenary talks and more than 300 contributed papers selected by the program committee to be presented in 17 sections. Due to a limited number of pages available for the proceedings volume, the length of each article as well as the total number of accepted contributions had to be restricted. Submitted manuscripts have therefore been reviewed and 62 of them have been selected for publication. This refereeing procedure has been strongly supported by the section chairmen and we would like to express our gratitude to them. Finally, we also would like to thank Dr. Werner Muller from Springer-Verlag for his support in publishing this proceedings volume.
Book Synopsis High-dimensional Adaptive Dynamic Programming with Mixed Integer Linear Programming by : Zirun Zhang
Download or read book High-dimensional Adaptive Dynamic Programming with Mixed Integer Linear Programming written by Zirun Zhang and published by . This book was released on 2015 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dynamic programming (DP, Bellman 1957) is a classic mathematical programming approach to solve multistage decision problems. The "Bellman equation" uses a recursive concept that includes both the current contribution and future contribution in the objective function of an optimization. The method has potential to represent dynamic decision-making systems, but an exact DP solution algorithm is limited to small problems with restrictions, such as problems with linear transitions and problems without uncertainty. Approximate dynamic programming (ADP) is a modern branch of DP that seeks to achieve numerical solutions via approximation. It is can be applied to real-world DP problems, but there are still challenges for high dimensions. This dissertation focuses on ADP value function approximation for a continuous-state space using the statistical perspective (Chen et al. 1999). Two directions of ADP methodology are developed: a sequential algorithm to explore the state space, and a sequential algorithm using mixed integer linear programming (MILP) and regression trees. The first component addresses exploration of the state space. A sequential state space exploration (SSSE) algorithm (Fan 2008) was developed using neural networks. Here it is considered the use of multivariate adaptive regression splines (Friedman 1991) in place of neural networks. In ADP, the value function approximation is defined over a specified state space region. In the real world, the relevant state space region is unknown. In particular, the ADP approach employed in this dissertation uses a statistical perspective that is analogous to design and analysis of computer experiments (DACE, Chen et al. 2006). In DACE, an experimental design is used to discretize the input space region, which is the state space region in ADP. Since the ADP state space region is unknown, SSSE uses the stochastic trajectories to sample future states and identify the potential range of system state. By reducing iterations without impacting solution quality, SSSE using MARS demonstrates improved efficiency over SSSE with neural networks. The second component of this dissertation addresses the optimization of a real world, complex, dynamic system. This work is motivated by a case study on the environmental impact of aircraft deicing activities at the Dallas-Fort Worth (D/FW) International Airport. For this case study, the state transitions are nonlinear, the objective function is nonconvex, and the decision (action) space is high-dimensional and discrete. To overcome these complexities, an ADP method is introduced using a piecewise linear value function approximation and MILP. The piecewise linear structure can be transformed for MILP by introducing binary variables. The treed regression value function approximation is flexible enough to approximate the nonlinear structure of the data and structured to be easily formulated in MILP. The proposed ADP approach is compared with a reinforcement learning (Murphy 2005) approach.
Book Synopsis Dynamic Programming Based Operation of Reservoirs by : K. D. W. Nandalal
Download or read book Dynamic Programming Based Operation of Reservoirs written by K. D. W. Nandalal and published by Cambridge University Press. This book was released on 2007-05-10 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dynamic programming is a method of solving multi-stage problems in which decisions at one stage become the conditions governing the succeeding stages. It can be applied to the management of water reservoirs, allowing them to be operated more efficiently. This is one of the few books dedicated solely to dynamic programming techniques used in reservoir management. It presents the applicability of these techniques and their limits on the operational analysis of reservoir systems. The dynamic programming models presented in this book have been applied to reservoir systems all over the world, helping the reader to appreciate the applicability and limits of these models. The book also includes a model for the operation of a reservoir during an emergency situation. This volume will be a valuable reference to researchers in hydrology, water resources and engineering, as well as professionals in reservoir management.
Download or read book INFOR. written by and published by . This book was released on 2003 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Adaptation and Learning in Control and Signal Processing 2001 by : S. Bittanti
Download or read book Adaptation and Learning in Control and Signal Processing 2001 written by S. Bittanti and published by Pergamon. This book was released on 2002-09-19 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: In control and signal processing, adaptation is a natural tool to cope with real-time changes in the dynamical behaviour of signals and systems. In this area, strongly connected with prediction and identification, there has been an increasing interest in switching and supervising methods. Moreover in recent years, special attention has been paid to the ideas evolving round the theory of statistical learning as a potential tool of improved adaptation. The IFAC workshop on Adaptation and Learning in Control and Signal Processing in 2001 gathered together experts in the field and interested researchers from universities and industry to present a full picture of the area. This proceedings volume presents papers covering the following subjects: Model reference and predictive control; Multiple model control; Adaptive control I/II; Adaptive control and learning; Learning; Adaptive control of nonlinear systems I/II; Supervisory control; Neural networks for control; PID design methods; Sliding mode; Adaptive filtering and estimation; Identification methods I/II.
