摘要翻译：
护士排班是一个复杂的排班问题，影响着世界各地的医院人员的日常工作。本文针对英国某大医院的护士排班问题，提出了一种新的基于组件的进化消除方法。该技术的主要思想是将一个时间表分解为各个组成部分（即每个护士分配的轮班模式），然后在这些组成部分上分别实施两种模拟自然选择和自然突变过程的进化淘汰策略，以迭代地提供更好的时间表。时间表中所有组件的价值必须不断得到证明，以便它们保持在那里。这个演示采用了一个评价函数，它评价每个组成部分对最终目标的贡献程度。然后应用两个消除步骤：第一个消除消除一些被认为不值得留在当前时间表中的组件；第二次淘汰也可能以低水平的概率抛出一些有价值的成分。利用一组基于局部最优性准则的构造性启发式方法，对剔除后的构件进行新的补充。52个实例的计算结果表明了该方法在解决实际问题中的适用性。
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英文标题：
《A Component Based Heuristic Search Method with Evolutionary Eliminations》
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作者：
Jingpeng Li, Uwe Aickelin, Edmund Burke
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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英文摘要：
  Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with evolutionary eliminations, for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decompose a schedule into its components (i.e. the allocated shift pattern of each nurse), and then to implement two evolutionary elimination strategies mimicking natural selection and natural mutation process on these components respectively to iteratively deliver better schedules. The worthiness of all components in the schedule has to be continuously demonstrated in order for them to remain there. This demonstration employs an evaluation function which evaluates how well each component contributes towards the final objective. Two elimination steps are then applied: the first elimination eliminates a number of components that are deemed not worthy to stay in the current schedule; the second elimination may also throw out, with a low level of probability, some worthy components. The eliminated components are replenished with new ones using a set of constructive heuristics using local optimality criteria. Computational results using 52 data instances demonstrate the applicability of the proposed approach in solving real-world problems.
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PDF链接：
https://arxiv.org/pdf/0910.2593