通过增量搜索和重新启动大规模单机调度以发布日期和截止日期来增强最佳首次搜索F
Yacine Laalaoui1, Rym M'Hallah2
1Information Technology Department, Taif University, Taif, Kingdom of Saudi Arabia.
概括
一个新的启发式搜索,FIR,在单一机器上高效地安排工作. 对于大规模的工作安排问题,FIR显著优于现有的解决者,在不到一分钟的时间内找到解决方案.
科学领域:
- 运营研究 运营研究
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 工作安排问题在运营研究和计算机科学中至关重要.
- 现有的方法难以处理大型实例,其特点是发布日期,处理时间和截止日期.
研究的目的:
- 引入一种新的启发式搜索算法,FIR,用于单机工作安排.
- 在确定调度可行性方面提高可扩展性和效率.
主要方法:
- 开发FIR (使用增量搜索和重新启动的可行性) 启发式搜索.
- 将增量搜索和重新启动策略纳入F heuristic.
- 与约束编程和混合整数编程解决方案进行比较分析.
主要成果:
- 在大规模的工作安排实例中,FIR表现出卓越的性能.
- 在不到60秒的时间内,FIR成功地为高达10万个工作的实例找到可行的解决方案.
- FIR的表现优于IBM ILOG CP,CPLEX和其他现有启发式.
结论:
- FIR是一种高效和可扩展的启发式方法,用于单机工作安排.
- 该算法在有效地解决复杂的调度问题方面取得了重大进展.
- 对于现实世界,大规模的调度挑战,FIR提供了一个实用的解决方案.
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