适应性强化学习用于飞机维护中的任务安排
Catarina Silva1, Pedro Andrade2, Bernardete Ribeiro2
1CISUC-Centre Informatics and Systems, Informatics Engineering Department, University of Coimbra, Polo II, Coimbra, 3004-531, Coimbra, Portugal. catarina@dei.uc.pt.
Scientific reports
|October 3, 2023
概括
本研究引入了航空公司维护计划的强化学习 (RL),降低了运营成本. RL算法优化飞机维护计划,平衡效率与适应新信息的能力.
科学领域:
- 运营研究 运营研究
- 人工智能的人工智能
- 航空航天工程 航空航天工程
背景情况:
- 航空公司的维护计划是复杂的,并影响运营成本.
- 现有的方法可能缺乏适应实时信息的适应性.
- 优化飞机停机时间对于航空公司的利能力至关重要.
研究的目的:
- 建议和评估用于飞机维护计划的强化学习 (RL) 方法.
- 开发静态 (长期) 和自适应 (重新安排) RL 算法.
- 通过使用关键绩效指标 (KPIs) 评估绩效,例如地面时间,时间松和变更得分.
主要方法:
- 实现用于初始维护计划的静态RL算法.
- 开发一种适应性RL算法,用于动态调度.
- 基于地面时间,时间松和变更得分指标的算法评估.
主要成果:
- 强化学习有效地产生高效的飞机维护时间表.
- 静态算法在地面时间和时间放松中显示了轻微的优势.
- 适应性算法在变化得分上显著表现出色,这表明其灵活性优越.
结论:
- 拟议的基于RL的系统提高了飞机维护效率和成本效益.
- 静态和自适应算法的组合提供了一个强大的调度解决方案.
- 这种方法为未来智能维护管理研究提供了基础.
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