一个知识增强的进化多任务记忆算法,用于多模式多目标灵活的工作车间安排,考虑到速度
IEEE transactions on cybernetics
|February 18, 2026
概括
本研究引入了一种新的算法,用于节能工作车间调度,解决可变机器速度和多式联网解决方案. 拟议的方法提高了生产效率,同时促进了制造业的绿色发展.
科学领域:
- 运营研究 运营研究
- 制造业 工程 制造工程
- 人工智能的人工智能
背景情况:
- 传统的灵活工厂调度通常假定机器的速度是恒定的,忽视了现实世界的能源效率需求.
- 将生产效率与制造业的绿色发展相平衡,导致复杂的多模式优化问题.
研究的目的:
- 为考虑可变机器速度 (MMFJSP-S) 的多式多目标灵活工作车间调度问题开发一种新的算法.
- 为应对多式联运解决方案和负面知识转移在节能调度的挑战.
主要方法:
- 介绍了一种知识增强的进化多任务记忆算法 (KEMMA).
- 使用一个进化式多任务处理 (EMT) 框架,并以自动学习为灵感的辅助任务.
- 实施知识增强和明确转移战略,以减轻负面转移.
- 采用映射转换机制来处理决策空间的多模式性.
主要成果:
- 拟议的KEMMA在解决MMFJSP-S的过程中,与十个先进的算法相比,表现出了更高的性能.
- 实验结果证实了知识增强和绘制转型战略的有效性.
- 该研究强调了在调度中解决多式联运物业的关键重要性.
结论:
- 凯玛为节能和绿色制造调度提供了显著的进步.
- 进化的多任务处理和知识转移的整合有效地解决了复杂的调度挑战.
- 未来的研究应该继续探索优化问题的多模式特性.
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