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Hybrid offline/online methods for optimization under uncertainty /
Balancing the solution-quality/time trade-off and optimizing problems which feature offline and online phases can deliver significant improvements in efficiency and budget control. Offline/online integration yields benefits by achieving high quality solutions while reducing online computation time....
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam, Netherlands :
IOS Press,
2022.
|
| Colección: | Frontiers in artificial intelligence and applications ;
v. 349. Frontiers in artificial intelligence and applications. Dissertations in artificial intelligence. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
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| 020 | |a 9781643682631 |q (electronic bk.) | ||
| 020 | |a 1643682636 |q (electronic bk.) | ||
| 020 | |z 9781643682624 |q (print) | ||
| 035 | |a (OCoLC)1317842369 | ||
| 037 | |a 9781643682631 |b IOS Press |n http://www.iospress.nl | ||
| 050 | 4 | |a QA402.5 | |
| 082 | 0 | 4 | |a 519.6 |2 23 |
| 049 | |a UAMI | ||
| 100 | 1 | |a De Filippo, Allegra, |e author. | |
| 245 | 1 | 0 | |a Hybrid offline/online methods for optimization under uncertainty / |c Allegra De Filippo. |
| 264 | 1 | |a Amsterdam, Netherlands : |b IOS Press, |c 2022. | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Frontiers in artificial intelligence and applications ; |v volume 349 | |
| 490 | 1 | |a Dissertations in artificial intelligence | |
| 504 | |a Includes bibliographical references. | ||
| 588 | 0 | |a Online resource; title from PDF title page (IOS Press, viewed May 18, 2022). | |
| 505 | 0 | |a Intro -- Title Page -- Abstract -- Contents -- Introduction -- Context -- Contribution -- Outline -- Related Work -- Optimization Under Uncertainty -- Robust Optimization -- Stochastic Optimization and Sequential Decision Problems -- Sampling and Sample Average Approximation -- Two-Stage Stochastic Programming -- Multistage Stochastic Programming -- Stochastic Dynamic Programming -- Markov Decision Processes -- Towards Online Stochastic Optimization -- Online Stochastic Optimization -- Online Anticipatory Algorithms -- Integrated Offline/Online Decision-Making in Complex Systems | |
| 505 | 8 | |a Motivating Examples -- Offline/Online Models -- Optimization Models under Uncertainty for EMS -- Distributed Generation and Virtual Power Plants -- Optimization Techniques -- Offline/Online Integration in Optimization under Uncertainty -- Introduction -- Strategic and Operational Decisions -- Model Description and Motivations -- Baseline Model: Formal Description -- Flattened Problem -- Offline Problem -- Online Heuristic -- Improving Offline/Online Integration Methods -- ANTICIPATE -- TUNING -- ACKNOWLEDGE -- ACTIVE -- Method Comparison -- Instantiating the Integrated Offline/Online Methods | |
| 505 | 8 | |a Distributed Energy System: the Virtual Power Plant Case Study -- Instantiating the Baseline Model -- Instantiating ANTICIPATE -- Instantiating TUNING -- Instantiating ACKNOWLEDGE -- Instantiating ACTIVE -- Results for the VPP -- Experimental Setup -- Discussion -- The Vehicle Routing Problem Case Study -- Instantiating the Baseline Model -- Instantiating ANTICIPATE -- Instantiating TUNING -- Instantiating ACKNOWLEDGE -- Instantiating ACTIVE -- Results for the VRP -- Experimental Setup -- Discussion -- Trade-Offs of Online Anticipatory Algorithms -- Introduction | |
| 505 | 8 | |a Motivations of ``Taming"" an Online Anticipatory Algorithm -- Offline Information Availability -- Building Block Techniques -- Probability Estimation for Scenario Sampling -- Building a Contingency Table -- Efficient Online Fixing Heuristic -- Deriving the FIXING Heuristic -- Formal Method Description -- ANTICIPATE-D -- CONTINGENCY -- CONTINGENCY-D -- Instantiating the Methods -- Instantiating the Methods for the VPP Energy Problem -- Instantiating the Baseline Model -- The Models of Uncertainty -- Instantiating ANTICIPATE -- Instantiating ANTICIPATE-D -- Instantiating CONTINGENCY | |
| 505 | 8 | |a Instantiating CONTINGENCY-D -- Results for the VPP -- Experimental Setup -- Discussion -- The Traveling Salesman Problem Case Study -- Instantiating the Baseline Model -- The Models of Uncertainty -- Instantiating ANTICIPATE -- Results for the TSP -- Experimental Setup -- Discussion -- Concluding Remarks & Future Works -- Bibliography | |
| 520 | |a Balancing the solution-quality/time trade-off and optimizing problems which feature offline and online phases can deliver significant improvements in efficiency and budget control. Offline/online integration yields benefits by achieving high quality solutions while reducing online computation time. This book considers multi-stage optimization problems under uncertainty and proposes various methods that have broad applicability. Due to the complexity of the task, the most popular approaches depend on the temporal granularity of the decisions to be made and are, in general, sampling-based methods and heuristics. Long-term strategic decisions that may have a major impact are typically solved using these more accurate, but expensive, sampling-based approaches. Short-term operational decisions often need to be made over multiple steps within a short time frame and are commonly addressed via polynomial-time heuristics, with the more advanced sampling-based methods only being applicable if their computational cost can be carefully managed. Despite being strongly interconnected, these 2 phases are typically solved in isolation. In the first part of the book, general methods based on a tighter integration between the two phases are proposed and their applicability explored, and these may lead to significant improvements. The second part of the book focuses on how to manage the cost/quality trade-off of online stochastic anticipatory algorithms, taking advantage of some offline information. All the methods proposed here provide multiple options to balance the quality/time trade-off in optimization problems that involve offline and online phases, and are suitable for a variety of practical application scenarios.-- |c Provided by publisher. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 0 | |a Uncertainty (Information theory) | |
| 650 | 7 | |a Mathematical optimization. |2 fast |0 (OCoLC)fst01012099 | |
| 650 | 7 | |a Uncertainty (Information theory) |2 fast |0 (OCoLC)fst01160838 | |
| 710 | 2 | |a IOS Press. | |
| 830 | 0 | |a Frontiers in artificial intelligence and applications ; |v v. 349. | |
| 830 | 0 | |a Frontiers in artificial intelligence and applications. |p Dissertations in artificial intelligence. | |
| 856 | 4 | 0 | |u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3283623 |z Texto completo |
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL29238885 | ||
| 938 | |a EBSCOhost |b EBSC |n 3283623 | ||
| 994 | |a 92 |b IZTAP | ||