| Project/Area Number |
17K06077
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Production engineering/Processing studies
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| Research Institution | Toyohashi University of Technology |
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Principal Investigator |
Sakaguchi Tatsuhiko 豊橋技術科学大学, 工学(系)研究科(研究院), 准教授 (00403303)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2017: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 多目的最適化 / スケジューリング / ネスティング / 遺伝的アルゴリズム / 進化型手法 / 精密板金加工 / 環境適応 / 生産工学 / システム工学 / 最適化 / 生産計画 |
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| Outline of Final Research Achievements |
In the sheet metal processing, it is required to reduce the waste of the metal-sheet and to satisfy the due-date of final products. To reduce waste, it is essential to optimize the processing layout of the metal sheets. On the other hand, the scheduling is necessary to satisfy the due-date of products. The optimization of cutting layout, i.e. nesting and scheduling is closely related with each other. Therefore, they should be considered simultaneously. In this study, to solve these two different optimization problems, we propose a co-evolutionary genetic algorithm based nesting scheduling method and an environment-adaptive genetic algorithm based method. We also analyzed the characteristics of each optimization method from the viewpoint of accuracy and diversity of multi-objective solutions so that the decision maker can select an appropriate optimization method.
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| Academic Significance and Societal Importance of the Research Achievements |
精密板金加工のネスティング問題およびスケジューリング問題のように，解くべき対象は異なるが関連性がある複数の問題を同時に求解することで全体最適化が可能となるまた，生産システムの意思決定者は，意思決定の際，多様な解の中から解を選択したい場合や，特定の領域で解を選択したい場合など，個々の選択基準のもとで解を算出しなければならない．これに対し，本研究で提案した共進化遺伝的アルゴリズムおよび異環境適応型遺伝的アルゴリズムでは，手法の使い分けや適切な初期解を与えることで，複数問題の多目的最適化において，解の探索領域を制御することができる．これは意思決定の効率化に寄与するものである．
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