Bibliographic Information
- Other Title
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- ブンシ ゲンテイホウ ト Deep Learning オ クミアワセタ ヘイレツ タスクスケジューリングカイホウ ノ カイハツ
- Development of A Parallel Task Scheduling Solver that Combines A Branch-and-bound Method And Deep Learning
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Abstract
type:Article
Since the task scheduling problem belongs to the strong NP-hard combinatorial optimization problem, the search time for the optimum solution becomes enormous due to the increase in the scale of the problem. Deep Learning can be applied to this difficult problem. Deep Learning has the advantage that the required time to find a solution is short once learning is completed, but it has the disadvantage that the optimum solution is not always found. Therefore, in this paper, we prototype and evaluate a method for speeding up to find the optimal solution by scheduling that combines the search method based on branch-and-bound method and deep learning.
identifier:http://repository.seikei.ac.jp/dspace/handle/10928/1431
Journal
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- 成蹊大学理工学研究報告 = The journal of the Faculty of Science and Technology, Seikei University
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成蹊大学理工学研究報告 = The journal of the Faculty of Science and Technology, Seikei University 58 (1), 11-16, 2021-06-01
成蹊大学理工学部
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Details 詳細情報について
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- CRID
- 1390573242710565760
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- NII Article ID
- 120007165147
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- NII Book ID
- AA1203510X
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- ISSN
- 18802265
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- NDL BIB ID
- 031696885
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- Text Lang
- ja
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- Data Source
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- JaLC
- IRDB
- NDL
- CiNii Articles