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Operation of the Collaborative Composite Manufacturing CCM System
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処理 - 輸送複合ロボットのエネルギー効率のよい柔軟な作業場スケジューリング問題のための二重自己学習共同進化アルゴリズム
Meizhou Zhang1,2, Min Zhou1,2, Liping Zhang3,4
1Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, 430081, China.
Scientific reports
|September 5, 2025
まとめ
この研究は,製造における処理・輸送ロボットのエネルギー効率のスケジュールを導入します. 新しいアルゴリズムは 柔軟な作業場でのエネルギー消費と生産時間を最小限にします
科学分野:
- 製造システム工学
- 運用研究
- 人工知能
背景:
- 複合ロボットは製造業でますます使用され,エネルギー需要が高まっています.
- 効率的なスケジュールは,これらの複雑なシステムにおけるエネルギー消費の管理に不可欠です.
- 柔軟な作業場環境は ロボットの統合スケジュールに 独特の課題をもたらします
研究 の 目的:
- 柔軟な作業場におけるロボットと機械の統合のためのエネルギー効率的なスケジュールを調査する.
- 総エネルギー消費とマックスパンの両方を最小限に抑える
- 先進的な製造ロボットと関連したエネルギー消費の増加に対処するためです.
主な方法:
- 新しい混合整数線形プログラミング (MILP) モデルの開発
- オプティマイゼーションのための二重自己学習共同進化アルゴリズムの提案.
- 立体的な解像度の表現と 貪欲な解読戦略を用いて
- 適応的なランダム選択とカオスマッピングによるハイブリッド初期化方法を使用します.
- 進化的な操作者選択と集団の相互作用のための二重の自己学習メカニズムを実装する.
主要な成果:
- 提案されたMILPモデルはエネルギー効率のスケジューリング問題を効果的に策定しています.
- 双重自己学習の共進化アルゴリズムは エネルギー消費と寿命を最小限に抑える優れた性能を示しています
- 実験分析により,提案されたアルゴリズムとその構成要素の有効性が確認されました.
- 貪欲な解読戦略は空いた時間とエネルギー消費を 減少させるのに成功しました
結論:
- 開発されたアプローチは,ロボットと機械を統合した柔軟な作業場でのエネルギー効率の良いスケジューリングのための効果的な解決策を提供します.
- 新しいアルゴリズムとそのコンポーネントは,最適化結果を大幅に改善します.
- この研究はエネルギー消費を減らすことで持続可能な製造方法に貢献します.
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