1,721,054 research outputs found

    Combining simulation experiments and analytical models with area-based accuracy for performance evaluation of manufacturing systems

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    Simulation is considered as one of the most practical tools to estimate manufacturing system performance, but it is slow in its execution. Analytical models are generally available to provide fast, but biased, estimates of the system performance. These two approaches are commonly used distinctly in a sequential approach, or one as alternative to the other, for assessing manufacturing system performance. This article proposes a method to combine simulation experiments with analytical results in a single performance evaluation model. The method is based on kernel regression and allows considering more than one analytical methods. A high-fidelity model is combined with low-fidelity models for manufacturing system performance evaluation. Multiple area-based low-fidelity models can be considered for the prediction. The numerical results show that the proposed method is able to identify the reliability of low-fidelity models in different areas and provide estimates with higher accuracy. Comparison with alternative approaches shows that the method is more accurate in a studied manufacturing application

    Dynamic Programming for Energy State Control of Machine Tools in Manufacturing with Part Admission Control

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    Energy efficiency in manufacturing is becoming a challenging goal due to the demand of this sector in the world- wide scenario. In the last years there has been a growing interest on technical solutions to reduce the energy consumption in manufacturing and one of the measures for saving energy is the implementation of controls that reduce machine energy consumption. Thus, controlling the machine state towards the optimum trade-off between cycle time and power demand can improve machine sustainability in manufacturing. Further, the integration of machine state control with part admission allows the representation of limited capacity systems

    Dynamic programming for energy control of machine tools in manufacturing

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    Controlling the machine state towards the optimum trade-off between cycle time and power demand improves machine sustainability in manufacturing. This paper presents a dynamic programming approach for optimally controlling a single server manufacturing system with finite capacity and Poisson arrivals (M/M/1/K). The admission of parts into the system is controlled together with the service rate of the machine tool. Further, the machine can be triggered in a low power consumption state when the service is interrupted. The structural properties of the optimal control are analyzed when the transition off/on is instantaneous and when a transitory - i.e., warm-up - is necessary to resume the service. The transitory length is considered exponentially distributed. Numerical results are based on value iteration approximation

    Extended kernel regression: A multi-resolution method to combine simulation experiments with analytical methods

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    Simulation is widely used to predict the performance of complex systems. The main drawback of simulation is that it is slow in execution and the related compute experiments can be very expensive. On the other hand, analytical methods are used to rapidly provide performance estimates, but they are often approximate because of their restrictive assumptions. Recently, Extended Kernel Regression (EKR) has been proposed to combine simulation with analytical methods for reducing the computational effort. This paper has different purposes. Firstly, EKR is tested on different cases and compared with other techniques. Secondly, two different methods for calculation of confidence band are proposed. Numerical results show that the EKR method provides accurate predictions, particularly when the computational effort is low. Results also show that the performance of the two confidence band methods depends on the case analyzed. Thus, further studies are necessary to develop a robust method for confidence band calculation

    Going Beyond Counting First Authors in Author Co-citation Analysis

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    The present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed

    Variations on the Author

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    “Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship

    Appropriate Similarity Measures for Author Cocitation Analysis

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    We provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis

    Dispelling the Myths Behind First-author Citation Counts

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    We conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more sophisticated methods
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