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A Study on an Evaluation Model for Robust Nurse Rostering Based on Heuristics

机译:基于启发式的鲁棒护士名册评估模型研究

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Staff scheduling problem has been researched for decades and dozens of approaches have been proposed. Since in the hospital ward, an optimal solution could be changed for the uncertain causes, such as sick leave or other unforeseen events. If these occur, the roster that has been settled as an optimal solution often needs to make changes such as shift moves and others, some of which could have impact on the rosters fitness value. We first investigate the sensitive of an optimal solution under several operations of those types and the result shows that the solutions which are optimal obtained with the searching technique could indeed be affected by those disturbance. Secondly, the evaluation method is used to construct new evaluation function to improve the robustness of a roster. The model could apply to any method such as population-based evolutionary approaches and metaheuristics. Experiments show that it could help generate more robust solutions.
机译:员工调度问题已经研究了数十年,并且已经提出了数十种方法。由于在医院病房中,可以针对不确定的原因(例如病假或其他意外事件)更改最佳解决方案。如果发生这些情况,已经确定为最佳解决方案的名册通常需要进行更改,例如换档和其他更改,其中一些可能会影响名册适应性值。我们首先研究了在这些类型的几种操作下最优解的敏感性,结果表明,使用搜索技术获得的最优解的确会受到那些干扰的影响。其次,采用评估方法构造新的评估函数,以提高名册的鲁棒性。该模型可以应用于任何方法,例如基于人口的进化方法和元启发式方法。实验表明,它可以帮助产生更强大的解决方案。

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