In real-life applications problems can frequently change or require small adaptations. Manually creating and tuning algorithms for different problem domains or different versions of a problem can be cumbersome and time-consuming. In this paper we consider several important problems with high practical relevance, which are Rotating Workforce Scheduling, Minimum Shift Design, and Bus Driver Scheduling. Instead of designing very specific solution methods, we propose to use the more general approach based on hyper-heuristics which take a set of simpler low-level heuristics and combine them to automatically create a fitting heuristic for the problem at hand. This paper presents a major study on applying hyper-heuristics to these domains, which contributes in four different ways: First, it defines new low-level heuristics for these scheduling domains, allowing to apply hyper-heuristics to them for the first time. Second, it provides a comparison of several state-of-the-art hyper-heuristics on those domains. Third, new best solutions for several instances of the different problem domains are found. Finally, a detailed investigation of the use of low-level heuristics by the hyper-heuristics gives insights in the way hyper-heuristics apply to different domains and the importance of different low-level heuristics. The results show that hyper-heuristics are able to perform well even on very complex practical problem domains in the area of scheduling and, while being more general and requiring less problem-specific adaptation, can in several cases compete with specialized algorithms for the specific problems. Several hyper-heuristics with very good performance across different real-life domains are identified. They can efficiently select low-level heuristics to apply for each domain, but for repeated application they benefit from evaluating and selecting the most useful subset of these heuristics. These results help to improve industrial systems in use for solving different scheduling scenarios by allowing faster and easier adaptation to new problem variants.

中文翻译：

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人员调度领域的超启发式


在现实生活中的应用中，问题可能会经常发生变化或需要进行小的调整。针对不同问题域或问题的不同版本手动创建和调整算法可能既麻烦又耗时。在本文中，我们考虑了几个具有高度实际相关性的重要问题，即轮班劳动力调度、最小班次设计和公交车司机调度。我们建议使用基于超启发式的更通用的方法，而不是设计非常具体的解决方法，该方法采用一组更简单的低级启发式，并将它们组合起来，自动为当前的问题创建一个合适的启发式。本文提出了一项关于将超启发式应用到这些领域的主要研究，它以四种不同的方式做出了贡献：首先，它为这些调度领域定义了新的低级启发式，允许首次将超启发式应用于它们。其次，它对这些领域的几种最先进的超启发式方法进行了比较。第三，针对不同问题领域的多个实例找到了新的最佳解决方案。最后，对超启发式对低级启发式的使用进行了详细研究，深入了解了超启发式应用于不同领域的方式以及不同低级启发式的重要性。结果表明，即使在调度领域中非常复杂的实际问题领域，超启发式算法也能够表现良好，并且虽然更通用并且需要较少的针对特定问题的适应，但在某些情况下可以与针对特定问题的专用算法竞争。确定了几种在不同现实生活领域中具有非常好的性能的超启发式方法。 他们可以有效地选择低级启发式方法来应用于每个领域，但对于重复应用，他们可以从评估和选择这些启发式方法中最有用的子集中受益。这些结果有助于更快、更轻松地适应新的问题变体，从而改善用于解决不同调度场景的工业系统。