Title | Decision Making Under Uncertainty [electronic resource] : Energy and Power / edited by Claude Greengard, Andrzej Ruszczynski |
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Imprint | New York, NY : Springer New York, 2002 |

Connect to | http://dx.doi.org/10.1007/978-1-4684-9256-9 |

Descript | X, 164 p. online resource |

SUMMARY

In the ideal world, major decisions would be made based on complete and reliable information available to the decision maker. We live in a world of uncertainties, and decisions must be made from information which may be incomplete and may contain uncertainty. The key mathematical question addressed in this volume is "how to make decision in the presence of quantifiable uncertainty." The volume contains articles on model problems of decision making process in the energy and power industry when the available information is noisy and/or incomplete. The major tools used in studying these problems are mathematical modeling and optimization techniques; especially stochastic optimization. These articles are meant to provide an insight into this rapidly developing field, which lies in the intersection of applied statistics, probability, operations research, and economic theory. It is hoped that the present volume will provide entry to newcomers into the field, and stimulation for further research

CONTENT

Stochastic programming models: wait-and-see versus here-and-now -- Optimal stimulation of oil production -- Power management in a hydro-thermal system under uncertainty by Lagrangian relaxation -- Hedging electricity portfolios via stochastic programming -- Opportunities for stochastic and probabilistic modeling in the deregulated electricity industry -- On supply function bidding in electricity markets -- Qualitative implications of uncertainty in economic equilibrium models -- List of workshop participants

Mathematics
Operations research
Decision making
Functions of real variables
Mathematical models
Statistics
Computational intelligence
Environmental management
Mathematics
Mathematical Modeling and Industrial Mathematics
Environmental Management
Operation Research/Decision Theory
Real Functions
Statistics for Engineering Physics Computer Science Chemistry and Earth Sciences
Computational Intelligence