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Introduction To Optimization Based Decision Making

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Introduction To Optimization Based Decision Making

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Author : Joao Luis de Miranda
language : en
Publisher: CRC Press
Release Date : 2021-12-19

Introduction To Optimization Based Decision Making written by Joao Luis de Miranda and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-19 with Business & Economics categories.

The large and complex challenges the world is facing, the growing prevalence of huge data sets, and the new and developing ways for addressing them (artificial intelligence, data science, machine learning, etc.), means it is increasingly vital that academics and professionals from across disciplines have a basic understanding of the mathematical underpinnings of effective, optimized decision-making. Without it, decision makers risk being overtaken by those who better understand the models and methods, that can best inform strategic and tactical decisions. Introduction to Optimization-Based Decision-Making provides an elementary and self-contained introduction to the basic concepts involved in making decisions in an optimization-based environment. The mathematical level of the text is directed to the post-secondary reader, or university students in the initial years. The prerequisites are therefore minimal, and necessary mathematical tools are provided as needed. This lean approach is complemented with a problem-based orientation and a methodology of generalization/reduction. In this way, the book can be useful for students from STEM fields, economics and enterprise sciences, social sciences and humanities, as well as for the general reader interested in multi/trans-disciplinary approaches. Features Collects and discusses the ideas underpinning decision-making through optimization tools in a simple and straightforward manner Suitable for an undergraduate course in optimization-based decision-making, or as a supplementary resource for courses in operations research and management science Self-contained coverage of traditional and more modern optimization models, while not requiring a previous background in decision theory

Introduction To Optimization Based Decision Making

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Author : João Luis de Miranda
language : en
Publisher: Chapman & Hall/CRC
Release Date : 2021-12-19

Introduction To Optimization Based Decision Making written by João Luis de Miranda and has been published by Chapman & Hall/CRC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-19 with Business & Economics categories.

An Introduction To Optimization

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Author : Edwin K. P. Chong
language : en
Publisher: John Wiley & Sons
Release Date : 2004-03-22

An Introduction To Optimization written by Edwin K. P. Chong and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-03-22 with Mathematics categories.

A modern, up-to-date introduction to optimization theory and methods This authoritative book serves as an introductory text to optimization at the senior undergraduate and beginning graduate levels. With consistently accessible and elementary treatment of all topics, An Introduction to Optimization, Second Edition helps students build a solid working knowledge of the field, including unconstrained optimization, linear programming, and constrained optimization. Supplemented with more than one hundred tables and illustrations, an extensive bibliography, and numerous worked examples to illustrate both theory and algorithms, this book also provides: * A review of the required mathematical background material * A mathematical discussion at a level accessible to MBA and business students * A treatment of both linear and nonlinear programming * An introduction to recent developments, including neural networks, genetic algorithms, and interior-point methods * A chapter on the use of descent algorithms for the training of feedforward neural networks * Exercise problems after every chapter, many new to this edition * MATLAB(r) exercises and examples * Accompanying Instructor's Solutions Manual available on request An Introduction to Optimization, Second Edition helps students prepare for the advanced topics and technological developments that lie ahead. It is also a useful book for researchers and professionals in mathematics, electrical engineering, economics, statistics, and business. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

Advanced Optimization And Decision Making Techniques In Textile Manufacturing

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Author : Anindya Ghosh
language : en
Publisher: CRC Press
Release Date : 2019-03-18

Advanced Optimization And Decision Making Techniques In Textile Manufacturing written by Anindya Ghosh and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-18 with Technology & Engineering categories.

Optimization and decision making are integral parts of any manufacturing process and management system. The objective of this book is to demonstrate the confluence of theory and applications of various types of multi-criteria decision making and optimization techniques with reference to textile manufacturing and management. Divided into twelve chapters, it discusses various multi-criteria decision-making methods such as AHP, TOPSIS, ELECTRE, and optimization techniques like linear programming, fuzzy linear programming, quadratic programming, in textile domain. Multi-objective optimization problems have been dealt with two approaches, namely desirability function and evolutionary algorithm. Key Features Exclusive title covering textiles and soft computing fields including optimization and decision making Discusses concepts of traditional and non-traditional optimization methods with textile examples Explores pertinent single-objective and multi-objective optimizations Provides MATLAB coding in the Appendix to solve various types of multi-criteria decision making and optimization problems Includes examples and case studies related to textile engineering and management

Water Resource Systems Planning And Management

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Author : Daniel P. Loucks
language : en
Publisher: Springer
Release Date : 2017-03-02

Water Resource Systems Planning And Management written by Daniel P. Loucks and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-02 with Technology & Engineering categories.

This book is open access under a CC BY-NC 4.0 license. This revised, updated textbook presents a systems approach to the planning, management, and operation of water resources infrastructure in the environment. Previously published in 2005 by UNESCO and Deltares (Delft Hydraulics at the time), this new edition, written again with contributions from Jery R. Stedinger, Jozef P. M. Dijkman, and Monique T. Villars, is aimed equally at students and professionals. It introduces readers to the concept of viewing issues involving water resources as a system of multiple interacting components and scales. It offers guidelines for initiating and carrying out water resource system planning and management projects. It introduces alternative optimization, simulation, and statistical methods useful for project identification, design, siting, operation and evaluation and for studying post-planning issues. The authors cover both basin-wide and urban water issues and present ways of identifying and evaluating alternatives for addressing multiple-purpose and multi-objective water quantity and quality management challenges. Reinforced with cases studies, exercises, and media supplements throughout, the text is ideal for upper-level undergraduate and graduate courses in water resource planning and management as well as for practicing planners and engineers in the field.

Introduction To Applied Optimization

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Author : Urmila Diwekar
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09

Introduction To Applied Optimization written by Urmila Diwekar and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Mathematics categories.

Provides well-written self-contained chapters, including problem sets and exercises, making it ideal for the classroom setting; Introduces applied optimization to the hazardous waste blending problem; Explores linear programming, nonlinear programming, discrete optimization, global optimization, optimization under uncertainty, multi-objective optimization, optimal control and stochastic optimal control; Includes an extensive bibliography at the end of each chapter and an index; GAMS files of case studies for Chapters 2, 3, 4, 5, and 7 are linked to http://www.springer.com/math/book/978-0-387-76634-8; Solutions manual available upon adoptions. Introduction to Applied Optimization is intended for advanced undergraduate and graduate students and will benefit scientists from diverse areas, including engineers.

Introduction To Optimization

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Author : Pablo Pedregal
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-11-03

Introduction To Optimization written by Pablo Pedregal and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-11-03 with Mathematics categories.

This undergraduate textbook introduces students of science and engineering to the fascinating field of optimization. It is a unique book that brings together the subfields of mathematical programming, variational calculus, and optimal control, thus giving students an overall view of all aspects of optimization in a single reference. As a primer on optimization, its main goal is to provide a succinct and accessible introduction to linear programming, nonlinear programming, numerical optimization algorithms, variational problems, dynamic programming, and optimal control. Prerequisites have been kept to a minimum, although a basic knowledge of calculus, linear algebra, and differential equations is assumed.

Decision Making Management

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Author : Alberto Pliego Marugan
language : en
Publisher: Academic Press
Release Date : 2017-07-20

Decision Making Management written by Alberto Pliego Marugan and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-20 with Business & Economics categories.

Decision-Making Management: A Tutorial and Applications provides practical guidance for researchers seeking to optimizing business-critical decisions employing Logical Decision Trees thus saving time and money. The book focuses on decision-making and resource allocation across and between the manufacturing, product design and logistical functions. It demonstrates key results for each sector with diverse real-world case studies drawn primarily from EU projects. Theory is accompanied by relevant analysis techniques, with a progressional approach building from simple theory to complex and dynamic decisions with multiple data points, including big data and lot of data. Binary Decision Diagrams are presented as the operating approach for evaluating large Logical Decision Trees, helping readers identify Boolean equations for quantitative analysis of multifaceted problem sets. Computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques are expertly blended to support analysis of multi-criteria decision-making problems with defined constraints and requirements. The final objective is to optimize dynamic decisions with original approaches employing useful tools, including Big Data analysis. Extensive annexes provide useful supplementary information for readers to follow methods contained in the book. - Explores the use of logical decision trees to solve business problems - Uses mathematical optimization techniques to resolve 'big data' or other multi-criteria problems - Provides annexes showcasing application in manufacturing, product design and logistics - Shows case examples in telecommunications, renewable energy and aerospace - Supplies introduction by Benjamin Lev, Editor-in-Chief of Omega, the highest-ranked journal in management science (JCR)

Reinforcement Learning And Stochastic Optimization

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Author : Warren B. Powell
language : en
Publisher: John Wiley & Sons
Release Date : 2022-03-15

Reinforcement Learning And Stochastic Optimization written by Warren B. Powell and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-15 with Mathematics categories.

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.

An Introduction To Optimization

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Author : Edwin K. P. Chong
language : en
Publisher: John Wiley & Sons
Release Date : 2013-02-05

Praise for the Third Edition ". . . guides and leads the reader through the learning path . . . [e]xamples are stated very clearly and the results are presented with attention to detail." —MAA Reviews Fully updated to reflect new developments in the field, the Fourth Edition of Introduction to Optimization fills the need for accessible treatment of optimization theory and methods with an emphasis on engineering design. Basic definitions and notations are provided in addition to the related fundamental background for linear algebra, geometry, and calculus. This new edition explores the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. The authors also present an optimization perspective on global search methods and include discussions on genetic algorithms, particle swarm optimization, and the simulated annealing algorithm. Featuring an elementary introduction to artificial neural networks, convex optimization, and multi-objective optimization, the Fourth Edition also offers: A new chapter on integer programming Expanded coverage of one-dimensional methods Updated and expanded sections on linear matrix inequalities Numerous new exercises at the end of each chapter MATLAB exercises and drill problems to reinforce the discussed theory and algorithms Numerous diagrams and figures that complement the written presentation of key concepts MATLAB M-files for implementation of the discussed theory and algorithms (available via the book's website) Introduction to Optimization, Fourth Edition is an ideal textbook for courses on optimization theory and methods. In addition, the book is a useful reference for professionals in mathematics, operations research, electrical engineering, economics, statistics, and business.

Introduction To Optimization Based Decision Making

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