The importance of the nurse rostering problem in complex healthcare environments should not be understated. The nurses in a hospital should be assigned to the most appropriate shifts and days so as to meet the demands of the hospital as well as to satisfy the requirements and requests of the nurses as much as possible. Nurse rostering represents a challenging and demanding combinatorial optimisation problem. To address it, general and efficient methodologies, such as selection hyper-heuristics, have emerged. In this paper, we will consider the multi-stage nurse rostering formulation, posed by the second international nurse rostering competition’s problem. We introduce a sequence-based selection hyper-heuristic that utilises a statistical Markov model. The proposed methodology incorporates a dedicated algorithm for building feasible initial solutions and a series of low-level heuristics with different dynamics that respect the characteristics of the competition’s problem formulation. Empirical results and analysis suggest that the proposed approach has significant potential for difficult problem instances.

在复杂的医疗环境中护士名册问题的重要性不容小stat。应该为医院的护士分配最合适的轮班和工作日数，以便满足医院的需求，并尽可能满足护士的要求和要求。护士名册代表了一个充满挑战和要求的组合优化问题。为了解决这个问题，已经出现了一般有效的方法，例如选择超启发式方法。在本文中，我们将考虑由第二届国际护士名册竞赛的问题提出的多阶段护士名册制定。我们介绍了利用统计马尔可夫模型的基于序列的选择超启发式方法。所提出的方法论结合了用于构建可行的初始解决方案的专用算法，以及一系列具有不同动力学特性的低级启发式算法，这些启发式算法都尊重了比赛问题表述的特征。实证结果和分析表明，所提出的方法对于困难的问题实例具有巨大的潜力。