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Reliability evaluation has a vital importance at all stages of processing and controlling modern engineering systems. In most of these modern systems the components have different contributions to the system, which are defined as weights. Among the other weighted systems, a linear consecutive-weighted-k-out-of-n:F system consists of n components, where each component has its own weight and reliability. The system fails if and only if the total weight of the failed consecutive components is at least k. The aim of this paper is to study the reliability of linear consecutive-weighted-k-out-of-n:F system consisting of independent & nonidentical and nonhomogeneous Markov dependent components. Exact formulae are provided for computing the reliability for above mentioned cases. Approximation formulae for the reliability are also presented.
In this paper, an exact formulation for computing reliability of weighted k-out-of-n:G systems using system signature is presented. Some component importance measures are computed. Moreover, reliability and some important reliability indices of repairable weighted k-out-of-n:G system are derived via system signature. A real life case of weighted k-out-of-n:G is analyzed using the proposed formulations to show the practicability of the given model.
Journal Article A genetic algorithm approach to the nurse scheduling problem with fuzzy preferences Get access Alejandra Duenas, Alejandra Duenas † Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield S1 4DA, UK †Corresponding author. Email: a.duenas@sheffield.ac.uk Search for other works by this author on: Oxford Academic Google Scholar G. Yazgı Tütüncü, G. Yazgı Tütüncü IESEG, School of Management, CNRS, LEM, UMR 8179, 3 Rue Digue, F-59000, Lille, France Search for other works by this author on: Oxford Academic Google Scholar James B. Chilcott James B. Chilcott Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield S1 4DA, UK Search for other works by this author on: Oxford Academic Google Scholar IMA Journal of Management Mathematics, Volume 20, Issue 4, October 2009, Pages 369–383, https://doi.org/10.1093/imaman/dpn033 Published: 20 November 2008 Article history Received: 01 May 2007 Accepted: 01 May 2008 Published: 20 November 2008
In this article, the influence of a cold standby component to the reliability of weighted k-out-of-n: G systems consisting of two different types of components is studied. Weighted k-out-of-n: G systems are generalization of k-out-of-n systems that has attracted substantial interest in reliability theory because of their various applications in engineering. A method based on residual lifetimes of mixed components is presented for computing reliability of weighted k-out-of-n: G systems with two types of components and a cold standby component. Reliability and mean time to failure of different structured systems have been computed. Moreover, obtained results are used for defining optimal system configurations that can minimize the overall system costs.
Classification on the basis of gene expression data derived from RNA-seq promises to become an important part of modern medicine. We propose a new classification method based on a model where the data is marginally negative binomial but dependent, thereby incorporating the dependence known to be present between measurements from different genes. The method, called qtQDA, works by first performing a quantile transformation (qt) then applying Gaussian quadratic discriminant analysis (QDA) using regularized covariance matrix estimates. We show that qtQDA has excellent performance when applied to real data sets and has advantages over some existing approaches. An R package implementing the method is also available on https://github.com/goknurginer/qtQDA .
Abstract Background and Objective Recent developments in the next-generation sequencing (NGS) based on RNA-sequencing (RNA-Seq) allow researchers to measure the expression levels of thousands of genes for multiple samples simultaneously. In order to analyze these kind of data sets, many classification models have been proposed in the literature. Most of the existing classifiers assume that genes are independent; however, this is not a realistic approach for real RNA-Seq classification problems. For this reason, some other classification methods, which incorporates the dependence structure between genes into a model, are proposed. qtQDA proposed by Koçhan et al. [1] is one of those classifiers, which estimates covariance matrix by Maximum Likelihood Estimator. Methods In this study, we use a another approach based on local dependence function to estimate the covariance matrix to be used in the qtQDA classification model. We investigate the impact of different covariance estimates on RNA-Seq data classification. Results The performances of qtQDA classifier based on two different covariance matrix estimates are compared over two real RNA-Seq data sets, in terms of classification error rates. The results show that using local dependence function approach yields a better estimate of covariance matrix and increases the performance of qtQDA classifier. Conclusion Incorporating the true/accurate covariance matrix into the classification model is an important and crucial step particularly for cancer prediction. The local covariance matrix estimate allows researchers to classify cancer patients based on gene expression profiles more accurately. R code for local dependence function is available at https://github.com/Necla/LocalDependence .
Abstract Classification on the basis of gene expression data derived from RNA-seq promises to become an important part of modern medicine. We propose a new classification method based on a model where the data is marginally negative binomial but dependent, thereby incorporating the dependence known to be present between measurements from different genes. The method, called qtQDA, works by first performing a quantile transformation (qt) then applying Gaussian Quadratic Discriminant Analysis (QDA) using regularized covariance matrix estimates. We show that qtQDA has excellent performance when applied to real data sets and has advantages over some existing approaches. An R package implementing the method is also available.
This paper studies drivers for RFID (Radio Fre quency IDentification) adoption. The mindlessne ss/mindfulness theory is applied to the context of RFID implementation decisions. Several type s of mindless and mindful decision making dri vers are put forward. Hypotheses are tested usi ng a questionnaire that was answered by 122 Chinese companies. The data shows mixed sup port for the applicability of the mindlessness/mi -ndfulness theory. Companies which notice othe r companies adopt RFID technology are motiva ted to adopt the technology as well. Late RFID implementers seem to take decisions more mi ndlessly than early RFID implementers. Still, ea rly RFID implementers also take decisions min dlessly. Neither late implementers nor early imp lementers can be qualified as being fully mindl ess: both groups also take decisions mindfully.
Most available (r, Q) inventory models assume that the actual inventory records are the same as these in the computer systems. With this unrealistic assumption, these models may, thus, distort the inventory decision making in practice. In this study, we develop new (r, Q) models considering the misplaced items to provide inventory managers with realistic decision-making support. In developing the new models, we characterize two cases: backorders and lost-sales. In both cases, we consider stochastic demand and introduce parameters to represent misplaced items. We also propose solution algorithms for model solving. Numerical examples are conducted to demonstrate the applicability and potential of the new (r, Q) models and solution algorithms in making realistic inventory decisions. We further obtain managerial implications.
A survey of 245 French e-grocery customers reveals their views on the unattended delivery model, including statistically significant differences across age groups but not between genders in terms of interest in unattended grocery delivery and intentions to buy groceries online. Some customer groups expect to adopt e-grocery if home delivery becomes possible, but their willingness to pay for delivery is low. Moreover, the analysis reveals that willingness to pay is not related to distance from the store, shopping duration, or shopping pleasure, such that could help grocers cover the costs. Thus French grocers will have difficulty moving to a complete e-commerce model. (This abstract was borrowed from another version of this item.)
This paper proposes a hybrid compromise programming local search approach with two main characteristics: a capacity to generate non-dominated solutions and the ability to interact with the decision maker. Compromise programming is an approach where it is not necessary to determine the entire set of Pareto-optimal solutions but only some of them. These solutions are called compromise solutions and represent a good tradeoff between conflicting objectives. Another advantage of this type of method is that it allows the inclusion of the decision maker's preferences through the definition of weights included in the different metrics used by the method. This approach is tested on an operating room planning process. This process incorporates the operating rooms and the nurse planning simultaneously. Three different objectives were considered: to minimize operating room costs, to minimize the maximum number of nurses needed to participate in surgeries and to minimize the number of open operating rooms. The results show that it is a powerful decision tool that enables the decision makers to apply compromise alongside optimal solutions during an operating room planning process.
Classification on the basis of gene expression data derived from RNA-seq promises to become an important part of modern medicine.We propose a new classification method based on a model where the data is marginally negative binomial but dependent, thereby incorporating the dependence known to be present between measurements from different genes.The method, called qtQDA, works by first performing a quantile transformation (qt) then applying Gaussian Quadratic Discriminant Analysis (QDA) using regularized covariance matrix estimates.We show that qtQDA has excellent performance when applied to real data sets and has advantages over some existing approaches.An R package implementing the method is also available on https://github.com/goknurginer/qtQDA.
Classification on the basis of gene expression data derived from RNA-seq promises to become an important part of modern medicine.We propose a new classification method based on a model where the data is marginally negative binomial but dependent, thereby incorporating the dependence known to be present between measurements from different genes.The method, called qtQDA, works by first performing a quantile transformation (qt) then applying Gaussian Quadratic Discriminant Analysis (QDA) using regularized covariance matrix estimates.We show that qtQDA has excellent performance when applied to real data sets and has advantages over some existing approaches.An R package implementing the method is also available on https://github.com/goknurginer/qtQDA.
Classification on the basis of gene expression data derived from RNA-seq promises to become an important part of modern medicine.We propose a new classification method based on a model where the data is marginally negative binomial but dependent, thereby incorporating the dependence known to be present between measurements from different genes.The method, called qtQDA, works by first performing a quantile transformation (qt) then applying Gaussian Quadratic Discriminant Analysis (QDA) using regularized covariance matrix estimates.We show that qtQDA has excellent performance when applied to real data sets and has advantages over some existing approaches.An R package implementing the method is also available on https://github.com/goknurginer/qtQDA.
Classification on the basis of gene expression data derived from RNA-seq promises to become an important part of modern medicine.We propose a new classification method based on a model where the data is marginally negative binomial but dependent, thereby incorporating the dependence known to be present between measurements from different genes.The method, called qtQDA, works by first performing a quantile transformation (qt) then applying Gaussian Quadratic Discriminant Analysis (QDA) using regularized covariance matrix estimates.We show that qtQDA has excellent performance when applied to real data sets and has advantages over some existing approaches.An R package implementing the method is also available on https://github.com/goknurginer/qtQDA.
Fuzzy concepts have been widely used to treat imprecision in many fields of natural and social sciences. In most of the natural science fields such as applied mathematics, physics, chemistry, and engineering, triangular and trapezoidal fuzzy numbers are commonly used and arithmetic operations on those numbers are studied in detail. On the other hand, in engineering and social science fields such as sociology and psychology, while treating the uncertainties, these numbers are not applicable and fuzzy numbers with more parameters and clear definitions of their arithmetic operations are needed. In order to fill this gap in the literature, in this study we propose the generalized pentagonal fuzzy numbers, and we define fuzzy arithmetic operations based on both extension and the function principle.
