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Please use this identifier to cite or link to this item: http://hdl.handle.net/10928/1431

Title: 分枝限定法とDeep Learningを組み合わせた並列タスクスケジューリング解法の開発
Other Titles: Development of A Parallel Task Scheduling Solver that Combines A Branch-and-bound Method And Deep Learning
Authors: 小納, 惇平
甲斐, 宗徳
KONO, Jumpei
KAI, Munenori
Keywords: Task Scheduling
Deep Learning
Branch and bound
Convolutional Neural Network
Issue Date: 1-Jun-2021
Publisher: 成蹊大学理工学部
Abstract: 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.
URI: http://hdl.handle.net/10928/1431
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