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Titlebook: Computational Management Science; State of the Art 201 Raquel J. Fonseca,Gerhard-Wilhelm Weber,João Telha Conference proceedings 2016 Sprin

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书目名称Computational Management Science
副标题State of the Art 201
编辑Raquel J. Fonseca,Gerhard-Wilhelm Weber,João Telha
视频videohttp://file.papertrans.cn/233/232652/232652.mp4
丛书名称Lecture Notes in Economics and Mathematical Systems
图书封面Titlebook: Computational Management Science; State of the Art 201 Raquel J. Fonseca,Gerhard-Wilhelm Weber,João Telha Conference proceedings 2016 Sprin
描述This volume contains contributions from the 11th International Conference on Management Science (CMS 2014), held at Lisbon, Portugal, on May 29-31, 2014. Its contents reflect the wide scope of Management Science, covering different theoretical aspects for a quite diverse set of applications. Computational Management Science provides a unique perspective in relevant decision-making processes by focusing on all its computational aspects. These include computational economics, finance and statistics; energy; scheduling; supply chains; design, analysis and applications of optimization algorithms; deterministic, dynamic, stochastic, robust and combinatorial optimization models; solution algorithms, learning and forecasting such as neural networks and genetic algorithms; models and tools of knowledge acquisition, such as data mining; and all other topics in management science with the emphasis on computational paradigms.
出版日期Conference proceedings 2016
关键词Computational models; Decision making; Optimization; Risk management; Robust optimization; Uncertainty; qu
版次1
doihttps://doi.org/10.1007/978-3-319-20430-7
isbn_softcover978-3-319-20429-1
isbn_ebook978-3-319-20430-7Series ISSN 0075-8442 Series E-ISSN 2196-9957
issn_series 0075-8442
copyrightSpringer International Publishing Switzerland 2016
The information of publication is updating

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Controlled Approximation of the Stochastic Dynamic Programming Value Function for Multi-Reservoir Sytices of such simplices form an irregular grid over which the value function is computed. Under convexity assumptions, lower and upper bounds are developed over the state space continuum. The partition is then refined where the gap between these bounds is largest. This process readily provides a con
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A Computational Method for Predicting the Entropy of Energy Market Time Seriess. According to conventional entropy-based analysis, high entropy values characterize unpredictable series, while more stable series exhibits lesser entropy values. Here, we predict the entropy regarding the future behavior of a series, based on the observation of historical data. Our prediction is
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