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Use Of The Expected Value Solution In Linear Programming Under Uncertainty
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Book Synopsis Use of the 'expected Value Solution' in Linear Programming Under Uncertainty by : Albert Madansky
Download or read book Use of the 'expected Value Solution' in Linear Programming Under Uncertainty written by Albert Madansky and published by . This book was released on 1960 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of two methods in the one-stage stochastic linear program is discussed: (1) replacing the random elements by their expected values (the 'expected-value solution'); and (2) replacing the random elements by pessimistic estimates of their values (the 'fat' technique). The one-stage problem and the two-stage problem are described, and the relation between the 'fat' techniques used in the one-stage problem and the so-called 'slack' techniques useful in the two-stage problem is demonstrated.
Book Synopsis Planning Under Uncertainty by : Gerd Infanger
Download or read book Planning Under Uncertainty written by Gerd Infanger and published by Boyd & Fraser Publishing Company. This book was released on 1994 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Methods of Solution of Linear Programs Under Uncertainty. Notes on Linear Programming and Extensions by : ALBERT. MADANSKY
Download or read book Methods of Solution of Linear Programs Under Uncertainty. Notes on Linear Programming and Extensions written by ALBERT. MADANSKY and published by . This book was released on 1961 with total page 1 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most applied linear-programming problems involve uncertainty in either the technology matrix, the requirement vector, or the cost. Some of the more usual methods of reducing the effects of uncertainty are (1) replacing the random elements by their expected values, (2) replacing the random elements by pessimistic estimates of their values, and (3) recasting the problem into a two-stage program so that, in the second stage, one can compensate for inaccuracies in the activities of the first stage. These methods are called the expected-value solution, the fat solution, and the slack solution, respectively. The one-stage linear program is examined under uncertainty in some detail, pointing out the relation between these various solutions. (Author).
Book Synopsis Stochastic Programming by : Willem K. Klein Haneveld
Download or read book Stochastic Programming written by Willem K. Klein Haneveld and published by Springer Nature. This book was released on 2019-10-24 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an essential introduction to Stochastic Programming, especially intended for graduate students. The book begins by exploring a linear programming problem with random parameters, representing a decision problem under uncertainty. Several models for this problem are presented, including the main ones used in Stochastic Programming: recourse models and chance constraint models. The book not only discusses the theoretical properties of these models and algorithms for solving them, but also explains the intrinsic differences between the models. In the book’s closing section, several case studies are presented, helping students apply the theory covered to practical problems. The book is based on lecture notes developed for an Econometrics and Operations Research course for master students at the University of Groningen, the Netherlands - the longest-standing Stochastic Programming course worldwide.
Book Synopsis On the Solution of Two-stage Linear Programs Under Uncertainty by : George B. Dantzig
Download or read book On the Solution of Two-stage Linear Programs Under Uncertainty written by George B. Dantzig and published by . This book was released on 1961 with total page 1 pages. Available in PDF, EPUB and Kindle. Book excerpt: A possible method for compensating for uncertainty in linear-programming problems is to replace the random elements by expected values or by pessimistic estimates of these values, or to recast the problem into a two-stage program so that, in the second stage, one can compensate for inaccuracies in the first stage. The purpose of this analysis is to examine the last of these methods in detail. More precisely, it investigates the conditions under which the first-stage decisions are optimal. In addition, formulas for using various existing computational algorithms to obtain an optimal solution are given. (Author).
Book Synopsis Combinatorial Optimization Under Uncertainty by : Ritu Arora
Download or read book Combinatorial Optimization Under Uncertainty written by Ritu Arora and published by CRC Press. This book was released on 2023-05-12 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the basic ideas, underlying principles, mathematical formulations, analysis and applications of the different combinatorial problems under uncertainty and attempts to provide solutions for the same. Uncertainty influences the behaviour of the market to a great extent. Global pandemics and calamities are other factors which affect and augment unpredictability in the market. The intent of this book is to develop mathematical structures for different aspects of allocation problems depicting real life scenarios. The novel methods which are incorporated in practical scenarios under uncertain circumstances include the STAR heuristic approach, Matrix geometric method, Ranking function and Pythagorean fuzzy numbers, to name a few. Distinct problems which are considered in this book under uncertainty include scheduling, cyclic bottleneck assignment problem, bilevel transportation problem, multi-index transportation problem, retrial queuing, uncertain matrix games, optimal production evaluation of cotton in different soil and water conditions, the healthcare sector, intuitionistic fuzzy quadratic programming problem, and multi-objective optimization problem. This book may serve as a valuable reference for researchers working in the domain of optimization for solving combinatorial problems under uncertainty. The contributions of this book may further help to explore new avenues leading toward multidisciplinary research discussions.
Book Synopsis Applications of Stochastic Programming by : Stein W. Wallace
Download or read book Applications of Stochastic Programming written by Stein W. Wallace and published by SIAM. This book was released on 2005-01-01 with total page 724 pages. Available in PDF, EPUB and Kindle. Book excerpt: Consisting of two parts, this book presents papers describing publicly available stochastic programming systems that are operational. It presents a diverse collection of application papers in areas such as production, supply chain and scheduling, gaming, environmental and pollution control, financial modeling, telecommunications, and electricity.
Book Synopsis Methods of Solution of Linear Programs Under Uncertainty by : Albert Madansky
Download or read book Methods of Solution of Linear Programs Under Uncertainty written by Albert Madansky and published by . This book was released on 1960 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three most usual methods of reducing the effects of uncertainty in the technology matrix, requirement vector, or cost vector of a linear programming problem are the expected-value solution, the 'fat' solution, and the 'slack' solution. These methods are explained in some detail, and the relation between these various methods is pointed out. (Author).
Download or read book Technical Abstract Bulletin written by and published by . This book was released on with total page 684 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Methods of Solution of Linear Programs Under Uncertainty by :
Download or read book Methods of Solution of Linear Programs Under Uncertainty written by and published by . This book was released on 1961 with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three most usual methods of reducing the effects of uncertainty in the technology matrix, requirement vector, or cost vector of a linear programming problem are the expected-value solution, the 'fat' solution, and the 'slack' solution. These methods are explained in some detail, and the relation between these various methods is pointed out. (Author).
Book Synopsis Sensitivity Analysis in Linear Systems by : Assem Deif
Download or read book Sensitivity Analysis in Linear Systems written by Assem Deif and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: A text surveying perturbation techniques and sensitivity analysis of linear systems is an ambitious undertaking, considering the lack of basic comprehensive texts on the subject. A wide-ranging and global coverage of the topic is as yet missing, despite the existence of numerous monographs dealing with specific topics but generally of use to only a narrow category of people. In fact, most works approach this subject from the numerical analysis point of view. Indeed, researchers in this field have been most concerned with this topic, although engineers and scholars in all fields may find it equally interesting. One can state, without great exaggeration, that a great deal of engineering work is devoted to testing systems' sensitivity to changes in design parameters. As a rule, high-sensitivity elements are those which should be designed with utmost care. On the other hand, as the mathematical modelling serving for the design process is usually idealized and often inaccurately formulated, some unforeseen alterations may cause the system to behave in a slightly different manner. Sensitivity analysis can help the engineer innovate ways to minimize such system discrepancy, since it starts from the assumption of such a discrepancy between the ideal and the actual system.
Book Synopsis Operations Research by : Charles M. Harvey
Download or read book Operations Research written by Charles M. Harvey and published by North-Holland. This book was released on 1979 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear optimization. Formulation of linear optimization models. The simplex algorithm. The simplex algorithm: further topics. Further topics in linear optimization. Postoptimal analysis and duality theory. Transportation models and related types of models. Multiperiod models for production and inventory; Integer programming models. Decision analysis. Probability: the quantification of uncertainty. Decision making under uncertainty. Value and utility: the quantification of preferences. Statistical decision theory.
Download or read book Optimal Decisions written by Oskar Lange and published by Elsevier. This book was released on 2014-05-17 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimal Decisions: Principles of Programming deals with all important problems related to programming. This book provides a general interpretation of the theory of programming based on the application of the Lagrange multipliers, followed by a presentation of the marginal and linear programming as special cases of this general theory. The praxeological interpretation of the method of Lagrange multipliers is also discussed. This text covers the Koopmans' model of transportation, geometric interpretation of the programming problem, and nature of activity analysis. The solution of the problem by marginal analysis, Hurwitz and the Bayes-Laplace principles, and planning of production under uncertainty are likewise deliberated. This publication is a good source for researchers and specialists intending to acquire knowledge of the principles of programming.
Book Synopsis Introduction to Stochastic Programming by : John R. Birge
Download or read book Introduction to Stochastic Programming written by John R. Birge and published by Springer Science & Business Media. This book was released on 2006-04-06 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: This rapidly developing field encompasses many disciplines including operations research, mathematics, and probability. Conversely, it is being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors present a broad overview of the main themes and methods of the subject, thus helping students develop an intuition for how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems. The early chapters introduce some worked examples of stochastic programming, demonstrate how a stochastic model is formally built, develop the properties of stochastic programs and the basic solution techniques used to solve them. The book then goes on to cover approximation and sampling techniques and is rounded off by an in-depth case study. A well-paced and wide-ranging introduction to this subject.
Book Synopsis Modeling with Stochastic Programming by : Alan J. King
Download or read book Modeling with Stochastic Programming written by Alan J. King and published by Springer Science & Business Media. This book was released on 2012-06-19 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: While there are several texts on how to solve and analyze stochastic programs, this is the first text to address basic questions about how to model uncertainty, and how to reformulate a deterministic model so that it can be analyzed in a stochastic setting. This text would be suitable as a stand-alone or supplement for a second course in OR/MS or in optimization-oriented engineering disciplines where the instructor wants to explain where models come from and what the fundamental issues are. The book is easy-to-read, highly illustrated with lots of examples and discussions. It will be suitable for graduate students and researchers working in operations research, mathematics, engineering and related departments where there is interest in learning how to model uncertainty. Alan King is a Research Staff Member at IBM's Thomas J. Watson Research Center in New York. Stein W. Wallace is a Professor of Operational Research at Lancaster University Management School in England.
Book Synopsis Optimization of Temporal Networks under Uncertainty by : Wolfram Wiesemann
Download or read book Optimization of Temporal Networks under Uncertainty written by Wolfram Wiesemann and published by Springer Science & Business Media. This book was released on 2012-01-04 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many decision problems in Operations Research are defined on temporal networks, that is, workflows of time-consuming tasks whose processing order is constrained by precedence relations. For example, temporal networks are used to model projects, computer applications, digital circuits and production processes. Optimization problems arise in temporal networks when a decision maker wishes to determine a temporal arrangement of the tasks and/or a resource assignment that optimizes some network characteristic (e.g. the time required to complete all tasks). The parameters of these optimization problems (e.g. the task durations) are typically unknown at the time the decision problem arises. This monograph investigates solution techniques for optimization problems in temporal networks that explicitly account for this parameter uncertainty. We study several formulations, each of which requires different information about the uncertain problem parameters.
Book Synopsis Linear Optimization Problems with Inexact Data by : Miroslav Fiedler
Download or read book Linear Optimization Problems with Inexact Data written by Miroslav Fiedler and published by Springer Science & Business Media. This book was released on 2006-07-18 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear programming has attracted the interest of mathematicians since World War II when the first computers were constructed. Early attempts to apply linear programming methods practical problems failed, in part because of the inexactness of the data used to create the models. This book presents a comprehensive treatment of linear optimization with inexact data, summarizing existing results and presenting new ones within a unifying framework.
