基于规则的护士排班分布估计智能局部搜索算法
An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering
摘要：
本文提出了一种新的模因进化算法，以实现明确的学习规则为基础的护士排班，其中包括应用一组启发式规则，每个护士的分配。该算法的主要框架是一个估计的分布算法，其中的蚂蚁矿工的方法，提高在每一代产生的个人解决方案。与我们以前的工作（其中学习是隐式的）不同，分布算法的模因估计中的学习是显式的，即我们能够直接识别构建块。整体方法通过构建概率模型来学习，即用于构建时间表的个体护理规则对的概率分布的估计。本地搜索处理器（即蚂蚁挖掘器）加强获得更高奖励的护士规则对。一个具有挑战性的现实世界护士排班问题被用作测试问题。计算结果表明，所提出的方法优于大多数现有的方法。本文所提出的学习方法可应用于其它按特定规则系统地建立时间表的排序问题
英文摘要：
This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse's assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-miner methodology improves the individual solutions produced in each generation. Unlike our previous work (where learning is implicit), the learning in the memetic estimation of distribution algorithm is explicit, i.e. we are able to identify building blocks directly. The overall approach learns by building a probabilistic model, i.e. an estimation of the probability distribution of individual nurse-rule pairs that are used to construct schedules. The local search processor (i.e. the ant-miner) reinforces nurse-rule pairs that receive higher rewards. A challenging real world nurse rostering problem is used as the test problem. Computational results show that the proposed approach outperforms most existing approaches. It is suggested that the learning methodologies suggested in this paper may be applied to other scheduling problems where schedules are built systematically according to specific rules
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
