Nurse rostering problem (NRP) or nurse scheduling problem is a combinatorial optimization problem that involves the assignment of shifts to nurses while managing coverage constraints, expertise categories, labor legislation, contractual agreements, personal preferences, etc. The focus on this problem serves to improve service quality, nurse health and their satisfaction, and reduction of hospital costs. The existence of uncertainties and inaccurate estimates of the workload leads to a non-optimal or an infeasible solution. In this study, due to the importance of human resource management and crisis management in the health care system, a sustainable approach was developed with a robust scenario-based optimization method. Since NRP is a NP-hard problem, it is impossible to solve it in medium and large sizes in reasonable time. In this paper, a well-known metaheuristic algorithm, namely the differential evolution (DE) algorithm was proposed due to its sound structural features for searching in binary space. Then its performance was compared against the genetic algorithm. The results show that the DE algorithm has good performance.

护士名册问题（NRP）或护士日程安排问题是组合优化问题，涉及在管理覆盖范围限制，专业知识类别，劳工法规，合同协议，个人喜好等时分配给护士的轮班。对此问题的关注有助于改善服务质量，护士健康及其满意度，并降低医院成本。工作量的不确定性和估计不正确会导致解决方案不理想或不可行。在这项研究中，由于人力资源管理和危机管理在卫生保健系统中的重要性，因此开发了一种可持续的方法，并采用了基于情景的优化方法。由于NRP是一个NP难题，因此不可能在合理的时间内解决大中型的问题。在本文中，提出了一种著名的元启发式算法，即差分进化算法（DE），该算法具有在二元空间中进行搜索的良好结构特征。然后将其性能与遗传算法进行比较。结果表明，DE算法具有良好的性能。