改进的细菌觅食优化算法在护士排班问题中的应用研究

发布时间：2018-05-25 14:25

本文选题：护士排班 + 护士倾向　；参考：《深圳大学》2017年硕士论文

【摘要】：护士作为维护医院日常运营的重要组成人员,由于医院工作的特殊性经常需要护士人员承担相比较普通职业更大的工作压力,尤其表现在护士岗位需要全天24小时在岗制。因此如何合理安排护士人员的工作时间对于规模大且客流量多的医院来讲并不是一件容易的事情,尤其是需要综合考虑医院成本、劳动法规、护士技能、人为倾向等等多方面因素的影响,因此本篇论文将群体智能算法引入该领域来求解护士排班问题。全文针对护士排班问题和细菌觅食优化算法进行了总结,详细介绍了研究的目的、意义及相关的理论基础、问题背景,对基于护士倾向的护士排班模型进行了完善,同时对细菌觅食优化算法提出了改进方案,最后将改进的智能优化算法应用到加入了个人倾向的护士排班问题中,本文在国家自然科学基金(71571120,71271140,71471158)及广东省自然科学基金(2016A030310074)的资助下从以下三个方面展开研究:(1)实际调查医院病房科室护士排班情况,以减少护士总成本为目标建立基于劳动法规的基本护士排班模型,并且在此基础上综合考虑护士对工作班次以及工作伙伴的个性化需求,将护士个人倾向约束添加到模型中,使问题更加贴近医院实际排班要求。(2)针对传统细菌觅食优化算法的三层嵌套结构,结合粒子之间信息交流行为,本文引入了分区信息交流机制,提出了改进的SRBFO-RC算法来求解护士排班问题。为了考察改进算法的求解质量,并用在标准测试函数上进行测试。结果表明,改进SRBFO-RC算法在求解质量上有很大的提高。(3)在提出的SRBFO-RC算法的基础上,给出了解决基于护士倾向的护士排班模型解决方案。探讨了细菌个体初始化、编码、算法的主要流程等问题。另外结合实际医院护士人数场景与传统的BFO、PSO进行了对比实验,验证了所提算法在解决该问题中具有较好的应用潜力。
[Abstract]:As an important component of the daily operation of the hospital, nurses often need nurses to take on more work pressure than ordinary occupations because of the particularity of the hospital work. Especially, the nurses' post needs 24 hours in the job. So how to arrange the working hours of the nurses is more large and more passenger flow. The hospital is not an easy thing, especially the need to consider the influence of many factors such as hospital costs, labor regulations, nurses' skills, human tendency and so on. Therefore, this paper introduces the swarm intelligence algorithm into the field to solve the nurse scheduling problem. The full text is aimed at the nurse scheduling problem and the bacterial foraging optimization algorithm. In this paper, the purpose, significance and theoretical basis of the study, the background of the study, the background of the problem, the nurse scheduling model based on the nurses' tendency are perfected, and the improved algorithm for the optimization of the bacterial foraging algorithm is put forward. Finally, the improved intelligent optimization algorithm is applied to the nurse scheduling problem which has joined the individual tendency. Under the support of the National Natural Science Foundation (715711207127114071471158) and the Guangdong Natural Science Foundation (2016A030310074), the study is carried out from the following three aspects: (1) the actual investigation of the nurse scheduling in the hospital ward section and the establishment of a basic nurse scheduling model based on labor laws and regulations in order to reduce the total cost of nurses and the basis of this basis On the basis of the comprehensive consideration of the nurses' individualized demand for work shifts and working partners, the personal tendencies of nurses are added to the model to make the problems close to the actual scheduling requirements of the hospital. (2) in view of the three layer nested structure of the traditional bacterial foraging optimization algorithm and the information exchange behavior between the particles, this paper introduces the regional information exchange. The improved SRBFO-RC algorithm is proposed to solve the nurse scheduling problem. In order to investigate the quality of the improved algorithm, the improved algorithm is tested on the standard test function. The results show that the improved SRBFO-RC algorithm has a great improvement in the solution quality. (3) on the basis of the proposed SRBFO-RC algorithm, a solution based on the nurses' tendency is given. The nurse scheduling model solved the problem of individual initialization, coding, and the main process of the algorithm. In addition, a comparison experiment with the traditional BFO and PSO was carried out with the actual number of nurses in the hospital, which proved that the proposed algorithm has a good application potential in solving the problem.
【学位授予单位】：深圳大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R47;TP18

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