Introduction To Optimization Based Decision Making

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Introduction to Optimization-Based Decision-Making

Author : Joao Luis de Miranda
Publisher : CRC Press
ISBN 13 : 1351778722
Total Pages : 263 pages
Book Rating : 4.3/5 (517 download)

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Book Synopsis Introduction to Optimization-Based Decision-Making by : Joao Luis de Miranda

Download or read book Introduction to Optimization-Based Decision-Making written by Joao Luis de Miranda and published by CRC Press. This book was released on 2021-12-24 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Author : João Luis de Miranda
Publisher : Chapman & Hall/CRC
ISBN 13 : 9781351778718
Total Pages : 241 pages
Book Rating : 4.7/5 (787 download)

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Book Synopsis Introduction to Optimization-Based Decision-Making by : João Luis de Miranda

Download or read book Introduction to Optimization-Based Decision-Making written by João Luis de Miranda and published by Chapman & Hall/CRC. This book was released on 2021-12-19 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Optimization for Decision Making

Author : Katta G. Murty
Publisher : Springer Science & Business Media
ISBN 13 : 1441912916
Total Pages : 502 pages
Book Rating : 4.4/5 (419 download)

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Book Synopsis Optimization for Decision Making by : Katta G. Murty

Download or read book Optimization for Decision Making written by Katta G. Murty and published by Springer Science & Business Media. This book was released on 2010-03-14 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear programming (LP), modeling, and optimization are very much the fundamentals of OR, and no academic program is complete without them. No matter how highly developed one’s LP skills are, however, if a fine appreciation for modeling isn’t developed to make the best use of those skills, then the truly ‘best solutions’ are often not realized, and efforts go wasted. Katta Murty studied LP with George Dantzig, the father of linear programming, and has written the graduate-level solution to that problem. While maintaining the rigorous LP instruction required, Murty's new book is unique in his focus on developing modeling skills to support valid decision making for complex real world problems. He describes the approach as 'intelligent modeling and decision making' to emphasize the importance of employing the best expression of actual problems and then applying the most computationally effective and efficient solution technique for that model.

Water Resource Systems Planning and Management

Author : Daniel P. Loucks
Publisher : Springer
ISBN 13 : 3319442341
Total Pages : 635 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Water Resource Systems Planning and Management by : Daniel P. Loucks

Download or read book Water Resource Systems Planning and Management written by Daniel P. Loucks and published by Springer. This book was released on 2017-03-02 with total page 635 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Decision Making and Optimization

Author : Martin Gavalec
Publisher : Springer
ISBN 13 : 3319083236
Total Pages : 231 pages
Book Rating : 4.3/5 (19 download)

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Book Synopsis Decision Making and Optimization by : Martin Gavalec

Download or read book Decision Making and Optimization written by Martin Gavalec and published by Springer. This book was released on 2014-10-08 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is a benefit for graduate and postgraduate students in the areas of operations research, decision theory, optimization theory, linear algebra, interval analysis and fuzzy sets. The book will also be useful for the researchers in the respective areas. The first part of the book deals with decision making problems and procedures that have been established to combine opinions about alternatives related to different points of view. Procedures based on pairwise comparisons are thoroughly investigated. In the second part we investigate optimization problems where objective functions and constraints are characterized by extremal operators such as maximum, minimum or various triangular norms (t-norms). Matrices in max-min algebra are useful in applications such as automata theory, design of switching circuits, logic of binary relations, medical diagnosis, Markov chains, social choice, models of organizations, information systems, political systems and clustering. The input data in real problems are usually not exact and can be characterized by interval values.

An Introduction to Robust Combinatorial Optimization

Author : Marc Goerigk
Publisher : Springer Nature
ISBN 13 : 3031612612
Total Pages : 316 pages
Book Rating : 4.0/5 (316 download)

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Book Synopsis An Introduction to Robust Combinatorial Optimization by : Marc Goerigk

Download or read book An Introduction to Robust Combinatorial Optimization written by Marc Goerigk and published by Springer Nature. This book was released on with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Multiple Criteria Decision Making by Multiobjective Optimization

Author : Ignacy Kaliszewski
Publisher : Springer
ISBN 13 : 3319327569
Total Pages : 134 pages
Book Rating : 4.3/5 (193 download)

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Book Synopsis Multiple Criteria Decision Making by Multiobjective Optimization by : Ignacy Kaliszewski

Download or read book Multiple Criteria Decision Making by Multiobjective Optimization written by Ignacy Kaliszewski and published by Springer. This book was released on 2016-08-02 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook approaches optimization from a multi-aspect, multi-criteria perspective. By using a Multiple Criteria Decision Making (MCDM) approach, it avoids the limits and oversimplifications that can come with optimization models with one criterion. The book is presented in a concise form, addressing how to solve decision problems in sequences of intelligence, modelling, choice and review phases, often iterated, to identify the most preferred decision variant. The approach taken is human-centric, with the user taking the final decision is a sole and sovereign actor in the decision making process. To ensure generality, no assumption about the Decision Maker preferences or behavior is made. The presentation of these concepts is illustrated by numerous examples, figures, and problems to be solved with the help of downloadable spreadsheets. This electronic companion contains models of problems to be solved built in Excel spreadsheet files. Optimization models are too often oversimplifications of decision problems met in practice. For instance, modeling company performance by an optimization model in which the criterion function is short-term profit to be maximized, does not fully reflect the essence of business management. The company’s managing staff is accountable not only for operational decisions, but also for actions which shall result in the company ability to generate a decent profit in the future. This calls for management decisions and actions which ensure short-term profitability, but also maintaining long-term relations with clients, introducing innovative products, financing long-term investments, etc. Each of those additional, though indispensable actions and their effects can be modeled separately, case by case, by an optimization model with a criterion function adequately selected. However, in each case the same set of constraints represents the range of company admissible actions. The aim and the scope of this textbook is to present methodologies and methods enabling modeling of such actions jointly.

Introduction to Applied Optimization

Author : Urmila Diwekar
Publisher : Springer Science & Business Media
ISBN 13 : 1475737459
Total Pages : 342 pages
Book Rating : 4.4/5 (757 download)

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Book Synopsis Introduction to Applied Optimization by : Urmila Diwekar

Download or read book Introduction to Applied Optimization written by Urmila Diwekar and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text presents a multi-disciplined view of optimization, providing students and researchers with a thorough examination of algorithms, methods, and tools from diverse areas of optimization without introducing excessive theoretical detail. This second edition includes additional topics, including global optimization and a real-world case study using important concepts from each chapter. Introduction to Applied Optimization is intended for advanced undergraduate and graduate students and will benefit scientists from diverse areas, including engineers.

Simulation-Based Optimization

Author : Abhijit Gosavi
Publisher : Springer Science & Business Media
ISBN 13 : 9781402074547
Total Pages : 592 pages
Book Rating : 4.0/5 (745 download)

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Book Synopsis Simulation-Based Optimization by : Abhijit Gosavi

Download or read book Simulation-Based Optimization written by Abhijit Gosavi and published by Springer Science & Business Media. This book was released on 2003-06-30 with total page 592 pages. Available in PDF, EPUB and Kindle. Book excerpt: Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization. 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.

An Introduction to Optimization

Author : Edwin K. P. Chong
Publisher : John Wiley & Sons
ISBN 13 : 0471654000
Total Pages : 497 pages
Book Rating : 4.4/5 (716 download)

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Book Synopsis An Introduction to Optimization by : Edwin K. P. Chong

Download or read book An Introduction to Optimization written by Edwin K. P. Chong and published by John Wiley & Sons. This book was released on 2004-04-05 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Optimization-based Analysis and Training of Human Decision Making

Author : Michael Engelhart
Publisher :
ISBN 13 :
Total Pages : 210 pages
Book Rating : 4.:/5 (922 download)

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Book Synopsis Optimization-based Analysis and Training of Human Decision Making by : Michael Engelhart

Download or read book Optimization-based Analysis and Training of Human Decision Making written by Michael Engelhart and published by . This book was released on 2015 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt:

An Introduction to Robust Combinatorial Optimization

Author : Marc Goerigk
Publisher : Springer
ISBN 13 : 9783031612602
Total Pages : 0 pages
Book Rating : 4.6/5 (126 download)

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Book Synopsis An Introduction to Robust Combinatorial Optimization by : Marc Goerigk

Download or read book An Introduction to Robust Combinatorial Optimization written by Marc Goerigk and published by Springer. This book was released on 2024-08-03 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a self-contained introduction to the world of robust combinatorial optimization. It explores decision-making using the min-max and min-max regret criteria, while also delving into the two-stage and recoverable robust optimization paradigms. It begins by introducing readers to general results for interval, discrete, and budgeted uncertainty sets, and subsequently provides a comprehensive examination of specific combinatorial problems, including the selection, shortest path, spanning tree, assignment, knapsack, and traveling salesperson problems. The book equips both students and newcomers to the field with a grasp of the fundamental questions and ongoing advancements in robust optimization. Based on the authors’ years of teaching and refining numerous courses, it not only offers essential tools but also highlights the open questions that define this subject area.

An Introduction to Optimization

Author : Edwin K. P. Chong
Publisher : John Wiley & Sons
ISBN 13 : 111821160X
Total Pages : 428 pages
Book Rating : 4.1/5 (182 download)

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Book Synopsis An Introduction to Optimization by : Edwin K. P. Chong

Download or read book An Introduction to Optimization written by Edwin K. P. Chong and published by John Wiley & Sons. This book was released on 2011-09-23 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise from the Second Edition "...an excellent introduction to optimization theory..." (Journal of Mathematical Psychology, 2002) "A textbook for a one-semester course on optimization theory and methods at the senior undergraduate or beginning graduate level." (SciTech Book News, Vol. 26, No. 2, June 2002) Explore the latest applications of optimization theory and methods Optimization is central to any problem involving decision making in many disciplines, such as engineering, mathematics, statistics, economics, and computer science. Now, more than ever, it is increasingly vital to have a firm grasp of the topic due to the rapid progress in computer technology, including the development and availability of user-friendly software, high-speed and parallel processors, and networks. Fully updated to reflect modern developments in the field, An Introduction to Optimization, Third Edition fills the need for an accessible, yet rigorous, introduction to optimization theory and methods. The book begins with a review of basic definitions and notations and also provides the related fundamental background of linear algebra, geometry, and calculus. With this foundation, the authors explore the essential topics of unconstrained optimization problems, linear programming problems, and nonlinear constrained optimization. An optimization perspective on global search methods is featured and includes discussions on genetic algorithms, particle swarm optimization, and the simulated annealing algorithm. In addition, the book includes an elementary introduction to artificial neural networks, convex optimization, and multi-objective optimization, all of which are of tremendous interest to students, researchers, and practitioners. Additional features of the Third Edition include: New discussions of semidefinite programming and Lagrangian algorithms A new chapter on global search methods A new chapter on multipleobjective optimization New and modified examples and exercises in each chapter as well as an updated bibliography containing new references An updated Instructor's Manual with fully worked-out solutions to the exercises Numerous diagrams and figures found throughout the text complement the written presentation of key concepts, and each chapter is followed by MATLAB exercises and drill problems that reinforce the discussed theory and algorithms. With innovative coverage and a straightforward approach, An Introduction to Optimization, Third Edition is an excellent book for courses in optimization theory and methods at the upper-undergraduate and graduate levels. It also serves as a useful, self-contained reference for researchers and professionals in a wide array of fields.

Algorithms for Decision Making

Author : Mykel J. Kochenderfer
Publisher : MIT Press
ISBN 13 : 0262370239
Total Pages : 701 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Algorithms for Decision Making by : Mykel J. Kochenderfer

Download or read book Algorithms for Decision Making written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2022-08-16 with total page 701 pages. Available in PDF, EPUB and Kindle. Book excerpt: A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.

Anticipatory Optimization for Dynamic Decision Making

Author : Stephan Meisel
Publisher : Springer Science & Business Media
ISBN 13 : 146140505X
Total Pages : 192 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis Anticipatory Optimization for Dynamic Decision Making by : Stephan Meisel

Download or read book Anticipatory Optimization for Dynamic Decision Making written by Stephan Meisel and published by Springer Science & Business Media. This book was released on 2011-06-23 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: The availability of today’s online information systems rapidly increases the relevance of dynamic decision making within a large number of operational contexts. Whenever a sequence of interdependent decisions occurs, making a single decision raises the need for anticipation of its future impact on the entire decision process. Anticipatory support is needed for a broad variety of dynamic and stochastic decision problems from different operational contexts such as finance, energy management, manufacturing and transportation. Example problems include asset allocation, feed-in of electricity produced by wind power as well as scheduling and routing. All these problems entail a sequence of decisions contributing to an overall goal and taking place in the course of a certain period of time. Each of the decisions is derived by solution of an optimization problem. As a consequence a stochastic and dynamic decision problem resolves into a series of optimization problems to be formulated and solved by anticipation of the remaining decision process. However, actually solving a dynamic decision problem by means of approximate dynamic programming still is a major scientific challenge. Most of the work done so far is devoted to problems allowing for formulation of the underlying optimization problems as linear programs. Problem domains like scheduling and routing, where linear programming typically does not produce a significant benefit for problem solving, have not been considered so far. Therefore, the industry demand for dynamic scheduling and routing is still predominantly satisfied by purely heuristic approaches to anticipatory decision making. Although this may work well for certain dynamic decision problems, these approaches lack transferability of findings to other, related problems. This book has serves two major purposes: ‐ It provides a comprehensive and unique view of anticipatory optimization for dynamic decision making. It fully integrates Markov decision processes, dynamic programming, data mining and optimization and introduces a new perspective on approximate dynamic programming. Moreover, the book identifies different degrees of anticipation, enabling an assessment of specific approaches to dynamic decision making. ‐ It shows for the first time how to successfully solve a dynamic vehicle routing problem by approximate dynamic programming. It elaborates on every building block required for this kind of approach to dynamic vehicle routing. Thereby the book has a pioneering character and is intended to provide a footing for the dynamic vehicle routing community.

The Multi-Criteria Approach for Decision Support

Author : Lotfi Azzabi
Publisher : Springer Nature
ISBN 13 : 3030572625
Total Pages : 92 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis The Multi-Criteria Approach for Decision Support by : Lotfi Azzabi

Download or read book The Multi-Criteria Approach for Decision Support written by Lotfi Azzabi and published by Springer Nature. This book was released on 2020-09-11 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the multi-criteria approach to decision support, as well as the various multi-criteria tools to help avoid multi-objective optimization. The book is intended as a tool for understanding the multi-criteria tools for decision support and modeling in mathematical programming. It helps to structure models, to easily model complex constraints, to have a basic modeling guide for any multi-criteria system and to better understand models already existing in the literature. The book is structured in the same order as components of the methodology, established in a multi-criteria optimization problem. It introduces the elements of the actors, the decision-making activity under criteria, calculations, specifications and objective criterion.

Intelligent Decision-making Models for Production and Retail Operations

Author : Zhaoxia Guo
Publisher : Springer
ISBN 13 : 3662526816
Total Pages : 330 pages
Book Rating : 4.6/5 (625 download)

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Book Synopsis Intelligent Decision-making Models for Production and Retail Operations by : Zhaoxia Guo

Download or read book Intelligent Decision-making Models for Production and Retail Operations written by Zhaoxia Guo and published by Springer. This book was released on 2016-06-27 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of intelligent decision-making techniques and discusses their application in production and retail operations. Manufacturing and retail enterprises have stringent standards for using advanced and reliable techniques to improve decision-making processes, since these processes have significant effects on the performance of relevant operations and the entire supply chain. In recent years, researchers have been increasingly focusing attention on using intelligent techniques to solve various decision-making problems. The opening chapters provide an introduction to several commonly used intelligent techniques, such as genetic algorithm, harmony search, neural network and extreme learning machine. The book then explores the use of these techniques for handling various production and retail decision-making problems, such as production planning and scheduling, assembly line balancing, and sales forecasting.