Optimization for Decision Making
Optimization for Decision Making

Author: Katta G. Murty

Publisher: Springer Science & Business Media

Published: 2010-03-14

Total Pages: 502

ISBN-13: 1441912916

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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.

Decision Making and Programming
Decision Making and Programming

Author: V. V. Kolbin

Publisher: World Scientific

Published: 2003

Total Pages: 757

ISBN-13: 9812775463

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The problem of selection of alternatives or the problem of decision making in the modern world has become the most important class of problems constantly faced by business people, researchers, doctors and engineers. The fields that are almost entirely focused on conflicts, where applied mathematics is successfully used, are law, military science, many branches of economics, sociology, political science, and psychology. There are good grounds to believe that medicine and some branches of biology and ethics can also be included in this list. Modern applied mathematics can produce solutions to many tens of classes of conflicts differing by the composition and structure of the participants, specific features of the set of their objectives or interests, and various characteristics of the set of their actions, strategies, behaviors, controls, and decisions as applied to various principles of selection or notions of decision optimization. The current issues of social and economic systems involve the necessity to coordinate and jointly optimize various lines of development and activities of modern society. For this reason, the decision problems arising in investigation of such systems are versatile, which shows up not only in the multiplicity of participants, their interests and complexity of reciprocal effects, but also in the laborious development of social utility criteria for a variety of indices and versatile objectives. The efficient decision methods for such complex systems can be developed only the basis of specially developed mathematical tools. Contents: Social Choice Problems; Vector Optimization; Infinite-Valued Programming Problems; Stochastic Programming; Discrete Programming; Fundamentals of Decision Making; Multicriterion Optimization Problems; Decision Making Under Incomplete Information; Multicriterion Elements of Optimization Theory; Decision Models; Decision Models Under Fuzzy Information; The Applied Mathematical Model for Conflict Management. Readership: Undergraduates, graduate students, professionals and researchers in applied mathematics.

Optimization for Profit
Optimization for Profit

Author: Filmore E. Bender

Publisher: Psychology Press

Published: 1992

Total Pages: 568

ISBN-13: 9781560220145

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This major new volume provides business decisionmakers and analysts with a tool that provides a logical structure for understanding problems as well as a mathematical technique for solving them. The primary tool presented throughout Optimization for Profit is linear programming (LP)--a medium that can be mastered by any individual who seeks to improve his/her analytical and decisionmaking skills. One of the special features of Optimization for Profit is the illustration of activity analysis as the technique used to formulate problems. By using activity analysis as the problem structure, linear programming become a natural extension of the way decision makers approach problems. As a result, linear programming becomes an integral part of the thinking process of the individual. Consequently, students or practitioners can readily create a linear programming model of an entire business or any part of a business. Several chapters are devoted to describing this technique and illustrating its application to many different types of companies, including an oil refinery, a marmalade production company, and a chicken processing plant. A thorough study of Optimization for Profit will enable you to work with any manufacturer or service industry and model all or part of the operation, and then solve the model to determine how best to minimize costs or maximize profits. Many firms save hundreds of thousands of dollars each year through the application of linear programming. The authors have presented the material in this vital book so clearly and thoroughly that an individual could master the material through self-study. The inclusion of problems at the end of each chapter makes this book suitable as a textbook at the advanced undergraduate or beginning graduate level at most colleges or universities for students of management science, operations research personnel, and applied mathematicians working in industry, government, or academia. Notable features of the book include: the practical aspects of modeling a business or any part of a business using linear programming a unique approach to explain the simplex method for solving linear programming problems real life, practical problems that are presented and solved in detail detailed instructions for those interested in solving linear programming problems on all types of computers from mainframes to PCs numerous problems provided for the benefit of the student and all of the linear programming models described in these problems as well as in the text itself are available on a diskette

Advances in Optimization and Linear Programming
Advances in Optimization and Linear Programming

Author: Ivan Stanimirović

Publisher: Apple Academic Press

Published: 2024-07-08

Total Pages: 0

ISBN-13: 9781774637418

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This new volume provides the information needed to understand the simplex method, the revised simplex method, dual simplex method, and more for solving linear programming problems.

Multi-Level Decision Making
Multi-Level Decision Making

Author: Guangquan Zhang

Publisher: Springer

Published: 2015-02-07

Total Pages: 385

ISBN-13: 3662460599

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This monograph presents new developments in multi-level decision-making theory, technique and method in both modeling and solution issues. It especially presents how a decision support system can support managers in reaching a solution to a multi-level decision problem in practice. This monograph combines decision theories, methods, algorithms and applications effectively. It discusses in detail the models and solution algorithms of each issue of bi-level and tri-level decision-making, such as multi-leaders, multi-followers, multi-objectives, rule-set-based, and fuzzy parameters. Potential readers include organizational managers and practicing professionals, who can use the methods and software provided to solve their real decision problems; PhD students and researchers in the areas of bi-level and multi-level decision-making and decision support systems; students at an advanced undergraduate, master’s level in information systems, business administration, or the application of computer science.

Algorithms for Decision Making
Algorithms for Decision Making

Author: Mykel J. Kochenderfer

Publisher: MIT Press

Published: 2022-08-16

Total Pages: 701

ISBN-13: 0262047012

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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.