By Raquel J. Fonseca, Gerhard-Wilhelm Weber, João Telhada
ISBN-10: 3319204297
ISBN-13: 9783319204291
ISBN-10: 3319204300
ISBN-13: 9783319204307
This quantity includes contributions from the eleventh overseas convention on administration technology (CMS 2014), held at Lisbon, Portugal, on may perhaps 29-31, 2014. Its contents mirror the extensive scope of administration technology, overlaying diversified theoretical facets for a relatively different set of purposes. Computational administration technology offers a special standpoint in proper decision-making tactics via targeting all its computational elements. those comprise computational economics, finance and records; power; scheduling; offer chains; layout, research and functions of optimization algorithms; deterministic, dynamic, stochastic, powerful and combinatorial optimization types; resolution algorithms, studying and forecasting reminiscent of neural networks and genetic algorithms; versions and instruments of information acquisition, resembling information mining; and all different issues in administration technology with the emphasis on computational paradigms.
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Published online: 20 December 2013. 1007/s12,667-013-0110-4 (2013) A Computational Method for Predicting the Entropy of Energy Market Time Series Francesco Benedetto, Gaetano Giunta, and Loretta Mastroeni 1 Introduction The understanding of the dynamic behaviour of energy market time series (EMTS) is of great and crucial interest for the analysis of energy commodities. In particular, the observation of historical data as well as the analysis of their volatility (and price fluctuations) can be useful indicators of the dynamic characteristics of the series, in order to effectively perform forecasting procedures.
36(4), 450–455 (2008) 9. : What you should know about approximate dynamic programming. Nav. Res. Logist. 56(3), 239–249 (2009) 10. : Analysis of stochastic dual dynamic programming method. Eur. J. Oper. Res. 209(1), 63–72 (2011) 11. : Adaptive monitoring of the progressive hedging penalty for reservoir systems management. Energy Syst. 1–16. Published online: 20 December 2013. 1007/s12,667-013-0110-4 (2013) A Computational Method for Predicting the Entropy of Energy Market Time Series Francesco Benedetto, Gaetano Giunta, and Loretta Mastroeni 1 Introduction The understanding of the dynamic behaviour of energy market time series (EMTS) is of great and crucial interest for the analysis of energy commodities.
Interruptible electricity contracts from an electricity retailer’s point of view: valuation and optimal interruption. Oper. Res. 54(4), 627–642 (2006) 2. : Probabilistic characterisation of the aggregated residential load patterns. Gener. Transm. Distrib. IET 2(3), 373–382 (2008) 3. : Mean-risk efficient portfolio analysis of demand response and supply resources. Energy 34(10), 1523–1529 (2009) 4. : Flexible demand in smart grids-modeling and coordination. D. thesis, Karlsruhe, Karlsruher Institut für Technologie (KIT) (2013) 5.
Computational Management Science: State of the Art 2014 by Raquel J. Fonseca, Gerhard-Wilhelm Weber, João Telhada
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