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Data envelopment analysis (DEA) is a mathematical programming method in Operations Research that can be used to distinguish between efficient and inefficient decision making units (DMUs). However, the conventional DEA models do not have the ability to rank the efficient DMUs. The super-efficiency models in DEA are used for ranking the efficient DMUs. This paper proposes a non-radial super-efficiency...
To provide a broader education for Operational Research PhD students in the UK, the Engineering and Physical Sciences Research Council funds the National Taught Course Centre for Operational Research (NATCOR). This is an initiative led by six UK universities and includes a one-week, residential simulation module taught for the first time in July 2009. We describe the background to NATCOR, summarize...
We discuss N-Skart, a nonsequential procedure designed to deliver a confidence interval (CI) for the steady-state mean of a simulation output process when the user supplies a single simulation-generated time series of arbitrary size and specifies the required coverage probability for a CI based on that data set. N-Skart is a variant of the method of batch means that exploits separate adjustments to...
Product family-based configuration with respect to mass customization has been well recognized. In product configuration, a Product Data Model (PDM) is widely used to capture various semantic and structural information in product configuration tasks. However, PDM is not suitable for computational purpose. Constraint Satisfaction Problem (CSP)-based product configuration is a promising approach for...
This paper sets up a new comprehensive evaluation method based on extenics and rough set, combining the advantages of indicator system screening method and objective weight method based on rough set into traditional extension evaluation, and the new method is of strict process, strong feasibility and objectivity. Through the application in the evaluation of company's core competence, we testify the...
This paper addresses parameter estimation of superimposed signals jointly with their number within the Bayesian framework. We combine sparse Bayesian machine learning methods with the state of the art SAGE-based parameter estimation algorithm. Existing sparse Bayesian methods allow to assess model order through priors over model parameters, but do not consider models nonlinear in parameters. SAGE-based...
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