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Author: Publisher: ISBN: Category : Languages : en Pages : 182
Book Description
In this thesis, several concepts of handling uncertainties in the formulation of mathematical optimization problems are presented. Some of these concepts are extensions of classical concepts of robustness for single objective optimization problems, others are newly introduced concepts particularly developed for the multi-objective setting. Properties of these concepts and algorithms for computing the respective solutions are analyzed. Connections between the concepts are investigated and the connection between multi-objective and set-valued optimization is pointed out and used to develop ne...
Author: Publisher: ISBN: Category : Languages : en Pages : 182
Book Description
In this thesis, several concepts of handling uncertainties in the formulation of mathematical optimization problems are presented. Some of these concepts are extensions of classical concepts of robustness for single objective optimization problems, others are newly introduced concepts particularly developed for the multi-objective setting. Properties of these concepts and algorithms for computing the respective solutions are analyzed. Connections between the concepts are investigated and the connection between multi-objective and set-valued optimization is pointed out and used to develop ne...
Author: Lisa Thom Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
This thesis addresses combinatorial optimization problems with several objectives containing uncertain parameters. A variety of robustness concepts for multi-objective optimization problems have been developed during the last years. This thesis provides methods to find so-called robust efficient solutions with respect to several of these concepts, assuming the uncertain parameters to be given via common uncertainty sets. Several solution approaches are presented, including extensions and combinations of algorithms from both robust and multi-objective optimization, using properties of partic...
Author: Aharon Ben-Tal Publisher: Princeton University Press ISBN: 1400831059 Category : Mathematics Languages : en Pages : 565
Book Description
Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject. Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution. The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems. The theory is supported by numerous examples and computational illustrations. An essential book for anyone working on optimization and decision making under uncertainty, Robust Optimization also makes an ideal graduate textbook on the subject.
Author: Corinna Krüger Publisher: ISBN: Category : Languages : en Pages :
Book Description
Multiobjective optimization problems (MOPs) are problems with two or more objective functions. Two types of uncertainty in MOPs are distinguished, namely decision uncertainty and parameter uncertainty. Decision uncertainty means that solutions cannot be implemented exactly as targeted and parameter uncertainty means that a part of the problem data is unknown. In the first publication of this cumulative thesis, a minmax robustness concept for MOPs with decision uncertainty is introduced and decision robust efficient solutions are defined as the solutions to a set-valued optimization problem....
Author: Chi-Keong Goh Publisher: Springer ISBN: 3540959769 Category : Computers Languages : en Pages : 273
Book Description
Evolutionary algorithms are sophisticated search methods that have been found to be very efficient and effective in solving complex real-world multi-objective problems where conventional optimization tools fail to work well. Despite the tremendous amount of work done in the development of these algorithms in the past decade, many researchers assume that the optimization problems are deterministic and uncertainties are rarely examined. The primary motivation of this book is to provide a comprehensive introduction on the design and application of evolutionary algorithms for multi-objective optimization in the presence of uncertainties. In this book, we hope to expose the readers to a range of optimization issues and concepts, and to encourage a greater degree of appreciation of evolutionary computation techniques and the exploration of new ideas that can better handle uncertainties. "Evolutionary Multi-Objective Optimization in Uncertain Environments: Issues and Algorithms" is intended for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of evolutionary multi-objective optimization and uncertainties.
Author: Chi-Keong Goh Publisher: Springer Science & Business Media ISBN: 3540959750 Category : Computers Languages : en Pages : 273
Book Description
Evolutionary algorithms are sophisticated search methods that have been found to be very efficient and effective in solving complex real-world multi-objective problems where conventional optimization tools fail to work well. Despite the tremendous amount of work done in the development of these algorithms in the past decade, many researchers assume that the optimization problems are deterministic and uncertainties are rarely examined. The primary motivation of this book is to provide a comprehensive introduction on the design and application of evolutionary algorithms for multi-objective optimization in the presence of uncertainties. In this book, we hope to expose the readers to a range of optimization issues and concepts, and to encourage a greater degree of appreciation of evolutionary computation techniques and the exploration of new ideas that can better handle uncertainties. "Evolutionary Multi-Objective Optimization in Uncertain Environments: Issues and Algorithms" is intended for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of evolutionary multi-objective optimization and uncertainties.
Author: Ritu Arora Publisher: CRC Press ISBN: 1000859851 Category : Business & Economics Languages : en Pages : 184
Book Description
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.
Author: Javier Del Ser Lorente Publisher: BoD – Books on Demand ISBN: 1789233283 Category : Mathematics Languages : en Pages : 71
Book Description
Nature-inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in hundreds of research fields and problem instances. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, or memetic methods, with empirical, innovative findings when used in different subfields of mathematical optimization, such as stochastic, dynamic, multimodal and robust optimization, as well as noisy optimization and dynamic and constraint satisfaction problems.
Author: Geldermann, Jutta Publisher: Universitätsverlag Göttingen ISBN: 3863951425 Category : Business & Economics Languages : en Pages : 504
Book Description
Renewable raw materials are becoming increasingly important as an alternative resource base in industrial networks. Consequently, research for methods improving the efficient use of renewable resources in production processes with by-products is crucial. The aim is cascade utilization, thus the multiple utilization of a raw material before its conversion into energy. The International Conference on Resource Efficiency in Interorganizational Networks (ResEff) brings together interdisciplinary researchers developing strategies and solution concepts for efficient resource utilization. It is therefore a platform for scientific exchange both between experts as well as interdisciplinary groups from agricultural and forestry science, mathematical optimization, operations research, marketing, business informatics, production and logistics. The following facets of the challenging topic of resource efficiency in interorganizational networks are covered: Materials, technologies, planning of production and value-added networks for renewable resources as well as governance, coordination and sale of products from renewable resources.
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Author: 
Author: Lisa Thom
Author: Aharon Ben-Tal
Author: Corinna Krüger
Author: Chi-Keong Goh
Author: Chi-Keong Goh
Author: Ritu Arora
Author: Marc Goerigk
Author: Javier Del Ser Lorente
Author: Geldermann, Jutta