| 書目名稱 | Dynamic Pricing and Automated Resource Allocation for Complex Information Services |
| 副標(biāo)題 | Reinforcement Learni |
| 編輯 | Michael Schwind |
| 視頻video | http://file.papertrans.cn/284/283711/283711.mp4 |
| 概述 | Includes supplementary material: |
| 叢書名稱 | Lecture Notes in Economics and Mathematical Systems |
| 圖書封面 |  |
| 描述 | .Many firms provide their customers with online information products which require limited resources such as server capacity. This book develops allocation mechanisms that aim to ensure an efficient resource allocation in modern IT-services. Recent methods of artificial intelligence, such as neural networks and reinforcement learning, and nature-oriented optimization methods, such as genetic algorithms and simulated annealing, are advanced and applied to allocation processes in distributed IT-infrastructures, e.g. grid systems. The author presents two methods, both of which using the users’ willingness-to-pay to control the allocation process: The first approach uses a yield management method that tries to learn an optimal acceptance strategy for resource requests. The second method is a combinatorial auction able to deal with resource complementarities. The author finally generates a method to calculate dynamic resource prices, marking an important step towards the industrialization of grid systems. . |
| 出版日期 | Book 2007 |
| 關(guān)鍵詞 | Optimization Methods; algorithms; artificial intelligence; genetic algorithms; intelligence; learning; opt |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-540-68003-1 |
| isbn_softcover | 978-3-540-68002-4 |
| isbn_ebook | 978-3-540-68003-1Series ISSN 0075-8442 Series E-ISSN 2196-9957 |
| issn_series | 0075-8442 |
| copyright | Springer-Verlag Berlin Heidelberg 2007 |
|Archiver|手機(jī)版|小黑屋|
派博傳思國(guó)際
( 京公網(wǎng)安備110108008328)
GMT+8, 2025-10-29 02:57
發(fā)表于 2025-3-21 20:07:51
