This paper considers the Rotating Workforce Scheduling Problem, and shows how the strengths and weaknesses of various solution methods can be understood by the in-depth evaluation offered by a recently developed methodology known as Instance Space Analysis. We first present a set of features aiming to describe hardness of test instances. We create a new, more diverse set of instances based on an initial instance space analysis that reveals gaps in the instance space, and offers the opportunity to generate additional instances to add diversity to the test suite. The results of three algorithms on our extended instance set reveal insights based on this visual methodology. We observe different regions of strength and weakness in the instance space for each algorithm, as well as a phase transition from feasible to infeasible instances with more challenging instances at the phase transition boundary. This rigorous and insightful approach to analyzing algorithm performance highlights the critical role played by the choice of test instances, and the importance of ensuring diversity and unbiasedness of test instances to support valid conclusions.

中文翻译：

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人员调度问题的实例空间分析

本文考虑了轮换劳动力调度问题，并展示了如何通过最近开发的称为实例空间分析的方法提供的深入评估来理解各种解决方法的优缺点。我们首先提出一组旨在描述测试实例硬度的特征。我们基于初始实例空间分析创建了一组新的、更多样化的实例，该分析揭示了实例空间中的差距，并提供了生成额外实例以增加测试套件多样性的机会。我们扩展实例集上的三种算法的结果揭示了基于这种视觉方法的见解。我们在每个算法的实例空间中观察不同的优势和劣势区域，以及从可行实例到不可行实例的相变，在相变边界处具有更具挑战性的实例。这种分析算法性能的严谨而有见地的方法突出了测试实例选择所起的关键作用，以及确保测试实例的多样性和无偏见以支持有效结论的重要性。