Introduction To Optimization Based Decision Making

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Introduction to Optimization-Based Decision-Making

Joao Luis de Miranda
Introduction to Optimization Based Decision Making Book PDF

Author : Joao Luis de Miranda
Publisher : CRC Press
Page : 263 pages
File Size : 44,8 Mb
Release : 2021-12-24
Category : Business & Economics
ISBN : 9781351778725

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Introduction to Optimization-Based Decision-Making by Joao Luis de Miranda Pdf

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

João Luis de Miranda
Introduction to Optimization Based Decision Making Book PDF

Author : João Luis de Miranda
Publisher : Chapman & Hall/CRC
Page : 241 pages
File Size : 48,9 Mb
Release : 2021-12-19
Category : Business & Economics
ISBN : 1351778714

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Introduction to Optimization-Based Decision-Making by João Luis de Miranda Pdf

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

Multiple Criteria Decision Making by Multiobjective Optimization

Ignacy Kaliszewski,Janusz Miroforidis,Dmitry Podkopaev
Multiple Criteria Decision Making by Multiobjective Optimization Book PDF

Author : Ignacy Kaliszewski,Janusz Miroforidis,Dmitry Podkopaev
Publisher : Springer
Page : 118 pages
File Size : 47,5 Mb
Release : 2016-08-02
Category : Business & Economics
ISBN : 9783319327563

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Multiple Criteria Decision Making by Multiobjective Optimization by Ignacy Kaliszewski,Janusz Miroforidis,Dmitry Podkopaev Pdf

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.

The Multi-Criteria Approach for Decision Support

Lotfi Azzabi,Dorra Azzabi,Abdessamad Kobi
The Multi Criteria Approach for Decision Support Book PDF

Author : Lotfi Azzabi,Dorra Azzabi,Abdessamad Kobi
Publisher : Springer Nature
Page : 92 pages
File Size : 47,8 Mb
Release : 2020-09-11
Category : Business & Economics
ISBN : 9783030572624

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The Multi-Criteria Approach for Decision Support by Lotfi Azzabi,Dorra Azzabi,Abdessamad Kobi Pdf

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.

Simulation-Based Optimization

Abhijit Gosavi
Simulation Based Optimization Book PDF

Author : Abhijit Gosavi
Publisher : Springer Science & Business Media
Page : 592 pages
File Size : 46,7 Mb
Release : 2003-06-30
Category : Science
ISBN : 1402074549

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Simulation-Based Optimization by Abhijit Gosavi Pdf

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.

Anticipatory Optimization for Dynamic Decision Making

Stephan Meisel
Anticipatory Optimization for Dynamic Decision Making Book PDF

Author : Stephan Meisel
Publisher : Springer Science & Business Media
Page : 192 pages
File Size : 50,6 Mb
Release : 2011-06-23
Category : Business & Economics
ISBN : 9781461405054

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Anticipatory Optimization for Dynamic Decision Making by Stephan Meisel Pdf

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.

Water Resource Systems Planning and Management

Daniel P. Loucks,Eelco van Beek
Water Resource Systems Planning and Management Book PDF

Author : Daniel P. Loucks,Eelco van Beek
Publisher : Springer
Page : 624 pages
File Size : 50,7 Mb
Release : 2017-03-02
Category : Technology & Engineering
ISBN : 9783319442341

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Water Resource Systems Planning and Management by Daniel P. Loucks,Eelco van Beek Pdf

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

Martin Gavalec,Jaroslav Ramík,Karel Zimmermann
Decision Making and Optimization Book PDF

Author : Martin Gavalec,Jaroslav Ramík,Karel Zimmermann
Publisher : Springer
Page : 231 pages
File Size : 49,7 Mb
Release : 2014-10-08
Category : Business & Economics
ISBN : 9783319083230

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Decision Making and Optimization by Martin Gavalec,Jaroslav Ramík,Karel Zimmermann Pdf

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.

Optimization for Decision Making

Katta G. Murty
Optimization for Decision Making Book PDF

Author : Katta G. Murty
Publisher : Springer Science & Business Media
Page : 502 pages
File Size : 46,7 Mb
Release : 2010-03-14
Category : Mathematics
ISBN : 9781441912916

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Optimization for Decision Making by Katta G. Murty Pdf

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.

An Introduction to Robust Combinatorial Optimization

Marc Goerigk,Michael Hartisch
An Introduction to Robust Combinatorial Optimization Book PDF

Author : Marc Goerigk,Michael Hartisch
Publisher : Springer
Page : 0 pages
File Size : 54,6 Mb
Release : 2024-08-03
Category : Business & Economics
ISBN : 3031612604

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An Introduction to Robust Combinatorial Optimization by Marc Goerigk,Michael Hartisch Pdf

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.

Business Analytics for Decision Making

Steven Orla Kimbrough,Hoong Chuin Lau
Business Analytics for Decision Making Book PDF

Author : Steven Orla Kimbrough,Hoong Chuin Lau
Publisher : CRC Press
Page : 308 pages
File Size : 50,5 Mb
Release : 2018-09-03
Category : Business & Economics
ISBN : 9781315362595

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Business Analytics for Decision Making by Steven Orla Kimbrough,Hoong Chuin Lau Pdf

Business Analytics for Decision Making, the first complete text suitable for use in introductory Business Analytics courses, establishes a national syllabus for an emerging first course at an MBA or upper undergraduate level. This timely text is mainly about model analytics, particularly analytics for constrained optimization. It uses implementations that allow students to explore models and data for the sake of discovery, understanding, and decision making. Business analytics is about using data and models to solve various kinds of decision problems. There are three aspects for those who want to make the most of their analytics: encoding, solution design, and post-solution analysis. This textbook addresses all three. Emphasizing the use of constrained optimization models for decision making, the book concentrates on post-solution analysis of models. The text focuses on computationally challenging problems that commonly arise in business environments. Unique among business analytics texts, it emphasizes using heuristics for solving difficult optimization problems important in business practice by making best use of methods from Computer Science and Operations Research. Furthermore, case studies and examples illustrate the real-world applications of these methods. The authors supply examples in Excel®, GAMS, MATLAB®, and OPL. The metaheuristics code is also made available at the book's website in a documented library of Python modules, along with data and material for homework exercises. From the beginning, the authors emphasize analytics and de-emphasize representation and encoding so students will have plenty to sink their teeth into regardless of their computer programming experience.

An Introduction to Optimization

Edwin K. P. Chong,Stanislaw H. Zak
An Introduction to Optimization Book PDF

Author : Edwin K. P. Chong,Stanislaw H. Zak
Publisher : John Wiley & Sons
Page : 428 pages
File Size : 44,9 Mb
Release : 2011-09-23
Category : Mathematics
ISBN : 9781118211601

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An Introduction to Optimization by Edwin K. P. Chong,Stanislaw H. Zak Pdf

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.

Multicriteria and Optimization Models for Risk, Reliability, and Maintenance Decision Analysis

Adiel Teixeira de Almeida,Love Ekenberg,Philip Scarf,Enrico Zio,Ming J. Zuo
Multicriteria and Optimization Models for Risk Reliability and Maintenance Decision Analysis Book PDF

Author : Adiel Teixeira de Almeida,Love Ekenberg,Philip Scarf,Enrico Zio,Ming J. Zuo
Publisher : Springer Nature
Page : 502 pages
File Size : 43,8 Mb
Release : 2022-06-28
Category : Business & Economics
ISBN : 9783030896478

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Multicriteria and Optimization Models for Risk, Reliability, and Maintenance Decision Analysis by Adiel Teixeira de Almeida,Love Ekenberg,Philip Scarf,Enrico Zio,Ming J. Zuo Pdf

This book considers a broad range of areas from decision making methods applied in the contexts of Risk, Reliability and Maintenance (RRM). Intended primarily as an update of the 2015 book Multicriteria and Multiobjective Models for Risk, Reliability and Maintenance Decision Analysis, this edited work provides an integration of applied probability and decision making. Within applied probability, it primarily includes decision analysis and reliability theory, amongst other topics closely related to risk analysis and maintenance. In decision making, it includes multicriteria decision making/aiding (MCDM/A) methods and optimization models. Within MCDM, in addition to decision analysis, some of the topics related to mathematical programming areas are considered, such as multiobjective linear programming, multiobjective nonlinear programming, game theory and negotiations, and multiobjective optimization. Methods related to these topics have been applied to the context of RRM. In MCDA, several other methods are considered, such as outranking methods, rough sets and constructive approaches. The book addresses an innovative treatment of decision making in RRM, improving the integration of fundamental concepts from both areas of RRM and decision making. This is accomplished by presenting current research developments in decision making on RRM. Some pitfalls of decision models on practical applications on RRM are discussed and new approaches for overcoming those drawbacks are presented.

Algorithms for Decision Making

Mykel J. Kochenderfer,Tim A. Wheeler,Kyle H. Wray
Algorithms for Decision Making Book PDF

Author : Mykel J. Kochenderfer,Tim A. Wheeler,Kyle H. Wray
Publisher : MIT Press
Page : 701 pages
File Size : 47,6 Mb
Release : 2022-08-16
Category : Computers
ISBN : 9780262370233

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Algorithms for Decision Making by Mykel J. Kochenderfer,Tim A. Wheeler,Kyle H. Wray Pdf

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.

Optimization and Decision Support Design Guide: Using IBM ILOG Optimization Decision Manager

Axel Buecker,Yana Ageeva,Veronique Blanchard,Dr. Jeremy Bloom,Dr. Mehmet F. Candas,Joao Chaves,Guang Feng,Abhishek Raman,Dr. Hans Schlenker,IBM Redbooks
Optimization and Decision Support Design Guide Using IBM ILOG Optimization Decision Manager Book PDF

Author : Axel Buecker,Yana Ageeva,Veronique Blanchard,Dr. Jeremy Bloom,Dr. Mehmet F. Candas,Joao Chaves,Guang Feng,Abhishek Raman,Dr. Hans Schlenker,IBM Redbooks
Publisher : IBM Redbooks
Page : 368 pages
File Size : 48,5 Mb
Release : 2012-10-10
Category : Computers
ISBN : 9780738437361

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Optimization and Decision Support Design Guide: Using IBM ILOG Optimization Decision Manager by Axel Buecker,Yana Ageeva,Veronique Blanchard,Dr. Jeremy Bloom,Dr. Mehmet F. Candas,Joao Chaves,Guang Feng,Abhishek Raman,Dr. Hans Schlenker,IBM Redbooks Pdf

Today many organizations face challenges when developing a realistic plan or schedule that provides the best possible balance between customer service and revenue goals. Optimization technology has long been used to find the best solutions to complex planning and scheduling problems. A decision-support environment that enables the flexible exploration of all the trade-offs and sensitivities needs to provide the following capabilities: Flexibility to develop and compare realistic planning and scheduling scenarios Quality sensitivity analysis and explanations Collaborative planning and scenario sharing Decision recommendations This IBM® Redbooks® publication introduces you to the IBM ILOG® Optimization Decision Manager (ODM) Enterprise. This decision-support application provides the capabilities you need to take full advantage of optimization technology. Applications built with IBM ILOG ODM Enterprise can help users create, compare, and understand planning or scheduling scenarios. They can also adjust any of the model inputs or goals, and fully understanding the binding constraints, trade-offs, sensitivities, and business options. This book enables business analysts, architects, and administrators to design and use their own operational decision management solution.