A Stochastic Programming Approach for Production Planning with Uncertainty in the Quality of Raw Materials PDF Download
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Languages : en
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Languages : fr
Pages : 20
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Languages : en
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Languages : fr
Pages : 22
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Author: Horand Gassmann
Publisher: World Scientific
ISBN: 9814407518
Category : Business & Economics
Languages : en
Pages : 549
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Book Description
This book shows the breadth and depth of stochastic programming applications. All the papers presented here involve optimization over the scenarios that represent possible future outcomes of the uncertainty problems. The applications, which were presented at the 12th International Conference on Stochastic Programming held in Halifax, Nova Scotia in August 2010, span the rich field of uses of these models. The finance papers discuss such diverse problems as longevity risk management of individual investors, personal financial planning, intertemporal surplus management, asset management with benchmarks, dynamic portfolio management, fixed income immunization and racetrack betting. The production and logistics papers discuss natural gas infrastructure design, farming Atlantic salmon, prevention of nuclear smuggling and sawmill planning. The energy papers involve electricity production planning, hydroelectric reservoir operations and power generation planning for liquid natural gas plants. Finally, two telecommunication papers discuss mobile network design and frequency assignment problems.
Author: Stacy Dawn Lewis
Publisher:
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Category : Production planning
Languages : en
Pages : 82
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Author: Robert Lincoln Clay
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Languages : en
Pages : 0
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Category :
Languages : en
Pages :
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Author: Masoumeh Kazemi Zanjani
Publisher:
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Category : Combinatorial optimization
Languages : en
Pages : 0
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Author: Chao Jiang
Publisher: Springer Nature
ISBN: 9811585466
Category : Mathematics
Languages : en
Pages : 291
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Book Description
This book systematically discusses nonlinear interval optimization design theory and methods. Firstly, adopting a mathematical programming theory perspective, it develops an innovative mathematical transformation model to deal with general nonlinear interval uncertain optimization problems, which is able to equivalently convert complex interval uncertain optimization problems to simple deterministic optimization problems. This model is then used as the basis for various interval uncertain optimization algorithms for engineering applications, which address the low efficiency caused by double-layer nested optimization. Further, the book extends the nonlinear interval optimization theory to design problems associated with multiple optimization objectives, multiple disciplines, and parameter dependence, and establishes the corresponding interval optimization models and solution algorithms. Lastly, it uses the proposed interval uncertain optimization models and methods to deal with practical problems in mechanical engineering and related fields, demonstrating the effectiveness of the models and methods.
