Results 291 to 300 of about 55,839,926 (326)
Some of the next articles are maybe not open access.
Operations Research, 1995
In applications of operations research models, decision makers must assess the sensitivity of outputs to imprecise values for some of the model's parameters. Existing analytic approaches for classic optimization models rely heavily on duality properties for assessing the impact of local parameter variations, parametric programming for examining ...
openaire +3 more sources
In applications of operations research models, decision makers must assess the sensitivity of outputs to imprecise values for some of the model's parameters. Existing analytic approaches for classic optimization models rely heavily on duality properties for assessing the impact of local parameter variations, parametric programming for examining ...
openaire +3 more sources
Sensitivity analysis of scheduling algorithms
European Journal of Operational Research, 2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Penz, Bernard +2 more
openaire +4 more sources
Sensitivity analysis in economics
Computers & Operations Research, 1984The paper studies the local dependence on a parameter of a unique solution to a general inequality-equality constrained nonlinear programming problem. A theorem due to \textit{A. V. Fiacco} [Math. Program. 10, 287-311 (1976; Zbl 0357.90064)] is extended to deal with the case where the objective and constraint functions are definded only over the ...
openaire +3 more sources
A Bayesian approach to sensitivity analysis
Health Economics, 1999Sensitivity analysis has traditionally been applied to decision models to quantify the stability of a preferred alternative to parametric variation. In the health literature, sensitivity measures have traditionally been based upon distance metrics, payoff variations, and probability measures.
Felli, James C., Hazen, Gordon B.
openaire +3 more sources
Sensitivity Analysis for Biomedical Models
IEEE Transactions on Medical Imaging, 2010This article discusses the application of sensitivity analysis (SA) in biomedical models. Sensitivity analysis is widely applied in physics, chemistry, economics, social sciences and other areas where models are developed. By assigning a prior probability distribution to each model variable, the SA framework appeals to the posterior probabilities of ...
Zhenghui Hu, Pengcheng Shi
openaire +3 more sources
Uncertainty and Sensitivity Analysis
Working with Dynamic Crop Models, 2019Uncertainty analysis consists of quantitatively evaluating uncertainty in model components (input variables, parameters, equations) for a given situation, and deducing an uncertainty distribution for each output variable rather than a single value ...
D. Wallach +3 more
semanticscholar +1 more source
Sensitivity Analysis of the OWA Operator
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008The successful design and application of the ordered weighted averaging (OWA) method as a decision-making tool depend on the efficient computation of its order weights. The most popular methods for determining the order weights are the fuzzy linguistic quantifiers approach and the minimal variability method, which give different behavior patterns for ...
Mahdi Zarghami +2 more
openaire +3 more sources
Sensitivity Analysis of Nonlinear Eigenproblems
SIAM Journal on Matrix Analysis and Applications, 2019Summary: Let \(P : \Omega \subset\mathbb{C} \rightarrow\mathbb{C}^{n\times n}\) be given by \(P(\lambda) :=\sum^m_{j=0}A_j\phi_j(\lambda)\), where \( \phi_j : \Omega \rightarrow\mathbb{C}\) for \(j=0, 1, \ldots, m\) are suitable functions. We present an eigenvector-free framework for the sensitivity analysis of eigenvalues of \(P\).
Rafikul Alam, Sk. Safique Ahmad
openaire +2 more sources
No inconsistencies in sensitivity analysis
Trends in Ecology & Evolution, 2000We thank Joel Cohen, Jim Cushing and Roger A. Horn for helpful comments. Space limitations do not permit it, but if they did, the following would be co-authors to this letter: Solange Brault, Jean-Dominique Lebreton, Michael Neubert, Richard Sibly, Takenori Takada and Shripad Tuljapurkar.
openaire +2 more sources
2003
When modeling real-world problems, we cannot always be sure about the nature of our problem, nor be certain that it is immune to change. In job-shop scheduling, machines can break down, tasks may take longer in reality than in our model, and the market prices for the various commodities may change. Clearly, it would be beneficial if our CSP model could
openaire +1 more source
When modeling real-world problems, we cannot always be sure about the nature of our problem, nor be certain that it is immune to change. In job-shop scheduling, machines can break down, tasks may take longer in reality than in our model, and the market prices for the various commodities may change. Clearly, it would be beneficial if our CSP model could
openaire +1 more source