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....
Gespeichert in:
| 1. Verfasser: | |
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| Körperschaft: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam, Netherlands :
IOS Press,
2022.
|
| Schriftenreihe: | Frontiers in artificial intelligence and applications ;
v. 349. Frontiers in artificial intelligence and applications. Dissertations in artificial intelligence. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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.-- |
| Beschreibung: | 1 online resource |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 9781643682631 1643682636 |
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| 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. | |
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| 490 | 1 | |a Frontiers in artificial intelligence and applications ; |v volume 349 | |
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| 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. | ||
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| contents | 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 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 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 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 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 |
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| dewey-sort | 3519.6 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:30:34Z |
| institution | BVB |
| institution_GND | http://id.loc.gov/authorities/names/no2015091156 |
| isbn | 9781643682631 1643682636 |
| language | English |
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| series | Frontiers in artificial intelligence and applications ; Frontiers in artificial intelligence and applications. Dissertations in artificial intelligence. |
| series2 | Frontiers in artificial intelligence and applications ; Dissertations in artificial intelligence |
| spelling | De Filippo, Allegra, author. Hybrid offline/online methods for optimization under uncertainty / Allegra De Filippo. Amsterdam, Netherlands : IOS Press, 2022. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier Frontiers in artificial intelligence and applications ; volume 349 Dissertations in artificial intelligence Includes bibliographical references. Online resource; title from PDF title page (IOS Press, viewed May 18, 2022). 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 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 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 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 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 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.-- Provided by publisher. Mathematical optimization. http://id.loc.gov/authorities/subjects/sh85082127 Uncertainty (Information theory) http://id.loc.gov/authorities/subjects/sh85139564 Optimisation mathématique. Incertitude (Théorie de l'information) Mathematical optimization fast Uncertainty (Information theory) fast IOS Press. http://id.loc.gov/authorities/names/no2015091156 Frontiers in artificial intelligence and applications ; v. 349. http://id.loc.gov/authorities/names/n92019289 Frontiers in artificial intelligence and applications. Dissertations in artificial intelligence. http://id.loc.gov/authorities/names/no2003047925 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3283623 Volltext |
| spellingShingle | De Filippo, Allegra Hybrid offline/online methods for optimization under uncertainty / Frontiers in artificial intelligence and applications ; Frontiers in artificial intelligence and applications. Dissertations in artificial intelligence. 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 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 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 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 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 Mathematical optimization. http://id.loc.gov/authorities/subjects/sh85082127 Uncertainty (Information theory) http://id.loc.gov/authorities/subjects/sh85139564 Optimisation mathématique. Incertitude (Théorie de l'information) Mathematical optimization fast Uncertainty (Information theory) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85082127 http://id.loc.gov/authorities/subjects/sh85139564 |
| title | Hybrid offline/online methods for optimization under uncertainty / |
| title_auth | Hybrid offline/online methods for optimization under uncertainty / |
| title_exact_search | Hybrid offline/online methods for optimization under uncertainty / |
| title_full | Hybrid offline/online methods for optimization under uncertainty / Allegra De Filippo. |
| title_fullStr | Hybrid offline/online methods for optimization under uncertainty / Allegra De Filippo. |
| title_full_unstemmed | Hybrid offline/online methods for optimization under uncertainty / Allegra De Filippo. |
| title_short | Hybrid offline/online methods for optimization under uncertainty / |
| title_sort | hybrid offline online methods for optimization under uncertainty |
| topic | Mathematical optimization. http://id.loc.gov/authorities/subjects/sh85082127 Uncertainty (Information theory) http://id.loc.gov/authorities/subjects/sh85139564 Optimisation mathématique. Incertitude (Théorie de l'information) Mathematical optimization fast Uncertainty (Information theory) fast |
| topic_facet | Mathematical optimization. Uncertainty (Information theory) Optimisation mathématique. Incertitude (Théorie de l'information) Mathematical optimization |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3283623 |
| work_keys_str_mv | AT defilippoallegra hybridofflineonlinemethodsforoptimizationunderuncertainty AT iospress hybridofflineonlinemethodsforoptimizationunderuncertainty |