Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
7,841
result(s) for
"Optimierung"
Sort by:
Credibilistic programming : an introduction to models and applications
It provides fuzzy programming approach to solve real-life decision problems in fuzzy environment. Within the framework of credibility theory, it provides a self-contained, comprehensive and up-to-date presentation of fuzzy programming models, algorithms and applications in portfolio analysis.
BOOK
A Value-Function-Based Exact Approach for the Bilevel Mixed-Integer Programming Problem
We examine bilevel mixed-integer programs whose constraints and objective functions depend on both upper- and lower-level variables. The class of problems we consider allows for nonlinear terms to appear in both the constraints and the objective functions, requires all upper-level variables to be integer, and allows a subset of the lower-level variables to be integer. This class of bilevel problems is difficult to solve because the upper-level feasible region is defined in part by optimality conditions governing the lower-level variables, which are difficult to characterize because of the nonconvexity of the follower problem. We propose an exact finite algorithm for these problems based on an optimal-value-function reformulation. We demonstrate how this algorithm can be tailored to accommodate either optimistic or pessimistic assumptions on the follower behavior. Computational experiments demonstrate that our approach outperforms a state-of-the-art algorithm for solving bilevel mixed-integer linear programs.
Journal Article
Multi-criteria decision analysis : methods and software
This book presents an introduction to MCDA followed by more detailed chapters about each of the leading methods used in this field. Comparison of methods and software is also featured to enable readers to choose the most appropriate method needed in their research.
Worked examples as well as the software featured in the book are available on an accompanying website.
eBook
Discrete Nonlinear Optimization by State-Space Decompositions
This paper investigates a decomposition approach for binary optimization problems with nonlinear objectives and linear constraints. Our methodology relies on the partition of the objective function into separate low-dimensional dynamic programming (DP) models, each of which can be equivalently represented as a shortest-path problem in an underlying state-transition graph. We show that the associated transition graphs can be related by a mixed-integer linear program (MILP) so as to produce exact solutions to the original nonlinear problem. To address DPs with large state spaces, we present a general relaxation mechanism that dynamically aggregates states during the construction of the transition graphs. The resulting MILP provides both lower and upper bounds to the nonlinear function, and it may be embedded in branch-and-bound procedures to find provably optimal solutions. We describe how to specialize our technique for structured objectives (e.g., submodular functions) and consider three problems arising in revenue management, portfolio optimization, and healthcare. Numerical studies indicate that the proposed technique often outperforms state-of-the-art approaches by orders of magnitude in these applications.
Data and the online appendix are available at
https://doi.org/10.1287/mnsc.2017.2849
.
This paper was accepted by Yinyu Ye, optimization.
Journal Article
Robust Optimization
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 Optimizationalso makes an ideal graduate textbook on the subject.
eBook
Research and analysis on the development of intelligent toilet
Intelligent toilet, also known as \"electronic toilet\" or \"smart toilet\", was first invented by the Americans in 1964, and then introduced to Japan by Japanese businessmen. After the product is optimized in design, it began to be popularized in Japan in the 1980s and 1990s.In the early 1990s, Japanese businessmen has introduced intelligent toilet into China, so far, opened the domestic. The development of domestic intelligent toilet can be roughly divided into three stages: the first stage of domestic intelligent toilet development is 1990-1995, also known as the birth period of Chinese intelligent toilet, domestic intelligent toilet experienced the first stage of the cradle period, began to enter its growth period 1995-2015;2015 is a special and unusual year in the history of China's intelligent toilet. After 2015, domestic intelligent toilet enters its third stage of development.
Journal Article
Robust Solutions of Optimization Problems Affected by Uncertain Probabilities
In this paper we focus on robust linear optimization problems with uncertainty regions defined by
φ
-divergences (for example, chi-squared, Hellinger, Kullback-Leibler). We show how uncertainty regions based on
φ
-divergences arise in a natural way as confidence sets if the uncertain parameters contain elements of a probability vector. Such problems frequently occur in, for example, optimization problems in inventory control or finance that involve terms containing moments of random variables, expected utility, etc. We show that the robust counterpart of a linear optimization problem with
φ
-divergence uncertainty is tractable for most of the choices of
φ
typically considered in the literature. We extend the results to problems that are nonlinear in the optimization variables. Several applications, including an asset pricing example and a numerical multi-item newsvendor example, illustrate the relevance of the proposed approach.
This paper was accepted by Gérard P. Cachon, optimization.
Journal Article