小生境遗传算法效果评价及程序测试

目的 评价小生境遗传算法（niched pareto genetic algorithm,NPGA）进行多目标优化的效果,测试其程序的可靠性.方法 应用两目标简单测试函数、复杂测试函数、三目标测试函数对NPGA进行模拟测试,利用课题组成员英国Glasgow大学软件工程师陈益编写的Matlab2009a外挂SGALAB工具箱beta5008完成遗传算法寻优.结果 NPGA得到的两目标简单测试函数Pareto非劣解集的95％可信区间包含交叉点值,前沿呈一条光滑的曲线分布;两目标复杂测试函数Pareto非劣解前沿呈带状分布;三目标测试函数的Pareto非劣解前沿呈非线性、非对称的曲面分布.NPGA可得到测试函数的Pareto非劣解集,供决策者合理选择.结论NPGA多目标优化可以给出合理的Pareto非劣解集,效果理想、程序可靠,可用于在实际问题的分析.     

改进非劣分类遗传算法多目标优化效果评价及程序测试

目的 评价改进非劣分类遗传算法( NSGA-Ⅱ)进行多目标优化的效果,测试其程序的可靠性.方法 应用两目标简单测试函数、两目标复杂测试函数、三目标复杂测试函数对NSGA-Ⅱ进行模拟测试,利用课题组成员英国Glasgow大学软件工程师陈益编写的Matlab 2009a外挂SGALAB工具箱beta5008完成遗传算法寻优.结果 NSGA-Ⅱ得到的两目标简单测试函数95％ Pareto非劣解分布范围包含交叉点值1,前沿呈一条光滑的曲线分布;两目标复杂测试函数Pareto非劣解前沿在小于l的范围内呈下降的带状分布;三目标测试函数的Pareto非劣解前沿呈非线性、非对称的曲面分布.认为NSGA-Ⅱ可得到测试函数的Pareto非劣解集,供决策者合理选择.结论NSGA-Ⅱ多目标优化可以给出合理的Pareto非劣解集,效果理想、程序可靠,可用于实际问题的分析.     

基于非支配排序遗传算法的效果评价及程序测试

目的 研究非支配排序遗传算法的多目标优化问题,并对其效果进行评价.方法 采用4个标准测试函数对NSGA进行模拟测试.结果 通过NSGA对测试函数的多目标优化,说明NSGA程序可靠,可推广应用到实际问题中.结论 在保证多个目标都最优的前提下,NSGA搜索的Pareto非劣解比较理想,提供了可供研究者选择的Pareto非劣解方案.     

向量评估遗传算法优化珍珠菜抗肿瘤有效成分的研究

目的研究向量评估遗传算法(VEGA)在珍珠菜抗肿瘤有效成分提取工艺条件中的应用,并对其效果进行评价。方法对珍珠菜抗肿瘤有效成分提取工艺正交设计试验数据建立以总黄酮含量和总皂苷含量的子目标模型,采用VEGA对其进行多目标优化,搜索最优提取条件。利用课题组成员电子科技大学软件工程师陈益提供的Matlab2009a外挂SGALAB工具箱beta5008完成遗传算法的寻优;利用SPSS13.0软件进行统计分析。结果得到的最优工艺条件为80%乙醇,8倍用量,提取1次,提取时间根据实际情况选择的条件下的搜索总黄酮含量为10.36%、总皂苷含量25.79%。结论 VEGA收索的Pareto非劣解在保证多目标都最优的前提下是比较理想的,为珍珠菜抗肿瘤有效成分的最优条件选择提供了合理的方法,可推广到其他药物的最优条件选择。     

改进非劣分类遗传算法对盐酸雷尼替丁脉冲片多目标区间优化策略及效果评价

目的研究区间均值代替区间目标后,再利用改进非劣分类遗传算法(NSGA-Ⅱ)对盐酸雷尼替丁进行多目标区间优化的策略及效果。方法利用二次多项式模型建立目标函数,采用区间均值代替区间目标,使用改进非劣分类遗传算法(NSGA-Ⅱ)对转换后的目标函数进行多目标优化,搜索Pareto非劣解,并与原文的结果进行比较。结果盐酸雷尼替丁脉冲片处方优化中,随机搜索的30个Pareto最优解中,23个达到了规定优化效果,其中3个效果满意。30号方案释药时滞及累积释放度分别达到了211.62分钟和91.11%,效果最为理想。结论区间均值代替区间目标后,再利用改进非劣分类遗传算法(NSGA-Ⅱ)进行多目标区间优化的策略可行、有效。     

改进非劣分类遗传算法在金莲花醇提取条件优化中的应用

目的研究改进非劣分类遗传算法(NSGA-Ⅱ)在正交试验设计的多目标药物提取条件优化中的应用。方法对金莲花总黄酮醇提取工艺研究的正交试验数据建立以浸膏得率、总黄酮含量为子目标的数学模型,采用遗传算法对其进行单目标优化,NSGA-Ⅱ对其进行多目标优化,搜索最优提取条件,比较搜索结果;利用课题组编写的Matlab2009a外挂SGALAB工具箱beta5008完成遗传算法寻优。结果单目标遗传算法优化分别得到各子目标最大时的最优提取条件,NSGA-Ⅱ进行两目标优化时,确定的最优提取条件的效果以及由此得到的浸膏得率和总黄酮含量高于正交试验中的任何一个方案。结论 NSGA-Ⅱ搜索的Pareto非劣解是合理的,达到了较好的效果,为正交试验设计最优条件选择提供了合理的方法,可推广到均匀试验设计和析因试验设计。     

基于小生境遗传算法的多目标药物提取条件优化分析应用

目的 研究小生境遗传算法在均匀试验设计多目标药物提取条件优化中的应用.方法 对微萃取五味子的均匀试验数据建立以浸膏得率、五味子醇甲、五味子总木脂素的子目标模型,采用遗传算法分别对其进行单目标优化,NPGA对其进行多目标优化,搜索最优提取条件,比较搜索结果;利用课题组成员英国Glasgow大学软件工程师陈益编写的Matlab2009a外挂SGALAB工具箱beta5008完成遗传算法寻优.结果 单目标遗传算法优化可以得到各目标最大时的最优提取条件,NPGA进行三目标优化时,对各子目标进行了折衷处理使各子目标尽可能获得最大的解,在主要目标上达到了单目标最大函数值的76%以上,确定的最优提取条件的效果高于均匀试验中的任何一个方案.结论 NPGA搜索的Pareto非劣解是合理的,达到了较好的效果,为均匀试验设计最优条件选择提供了合理的方法,可推广到正交试验设计、析因试验设计的最优条件选择.     

基于微遗传算法的多目标Box-Behnken设计试验条件优化分析

目的比较微遗传算法与传统优化方法确定最优试验条件的效果。方法以琥珀酸维生素E、泊洛沙姆-188、0.1molNaOH作为影响因素;以粒径、ζ电位、克拉霉素(clarithromycin,CLA)乳剂相分布率作为评价指标,在Box-Behnken试验设计给定的条件下,通过最小二乘法拟合二阶响应面方程试验模型,分别运用Matlab2009a软件的函数模块、图形模块编制的遗传算法程序和ExpertDesign软件(Version7.1.6.0)的Box-Behnken设计的传统优化方法,筛选出最优试验条件,比较两种优化方法的效果。结果当评价指标和因素之间呈非线性回归关系时,Box-Behnken设计充分考虑到各影响因素间的交互作用,同时在中心点进行重复试验,提高了实验的准确性。经微遗传算法优化后试验因素琥珀酸维生素E、泊洛沙姆-188、0.1MNaOH最佳条件分别为:68.25%、0.52%、15.26%,评价指标粒径、ζ电位、CLA乳剂相分布率分别为:135.75nm、33.67mV、98.12%;经传统优化方法优化后试验因素琥珀酸维生素E、泊洛沙姆-188、0.1MNaOH最佳条件分别为:76.0%、0.5%、15.4%,评价指标粒径、ζ电位、CLA乳剂相分布率分别为:135.75nm、31.04mV、97.33%。结论对于三水平多因素的试验,采用Box-Behnken设计试验设计可以大大减少设计所花费的时间和成本,提高试验效率。微遗传算法相对于传统优化方法,可以从全局的角度搜索试验条件的最优组合,进行多个评价指标优化效果理想、程序可行、计算时间短、可以应用于解决多维解空间的实际问题。     

双种群遗传算法优化射野方向及权重的研究

目的:研究双种群遗传算法在同时优化射野方向和射野权重中的应用。方法:建立基于三维光子笔射束的IMRT剂量计算模型,用VisualC#.Net编写剂量计算并实现双种群遗传算法优化射野方向和权重的算法,分析优化结果。结果:在射野较少的情况下射野方向的微小变化可以使靶区剂量分布更加适形,且高剂量区域更大,用双种群遗传算法同时优化射野方向和权重,能够在一个临床可接受的计算时间内得到较高适形度的剂量分布。结论:在射野较小的情况下,射野方向的改变对剂量的分布有很大的影响。双种群遗传算法是一种有效的随机全局优化方法,它的并行性、全局性、鲁棒性使得方向和权重两个具有复杂耦合关系的量能够同时优化。双种群遗传算法在IMRT计划优化中具有广阔的应用前景。     

率的非劣效试验中样本含量的估计——SAS和PASS实现

目的 提供二分类定性资料平行设计非劣效临床试验样本含量最常用的计算公式及其 SAS和PASS过程,并为相关参数的设置提供参考。方法 基于二项分布的正态近似理论推导样本含量的估计公式,通过SAS程序和PASS过程探讨各重要参数（样本率、非劣效界值）变化时样本含量及检验效能的变化情况。结果 对率的非劣效试验样本含量的计算,公式、SAS程序和PASS过程能得到一致结果;当检验水准和对照组样本率确定时,试验组样本率越大、检验效能越小、界值越大,所需样本含量越小。结论 利用本文提供的公式、SAS程序和PASS过程,可以帮助研究者系统快速得到二分类资料2组平行非劣效设计时的样本含量。试验组样本率、检验效能和非劣效界值是非劣效临床试验估计样本含量必须认真考虑的参数。     

Constrained Optimization Methods in Health Services Research—An Introduction: Report 1 of the ISPOR Optimization Methods Emerging Good Practices Task Force

Providing health services with the greatest possible value to patients and society given the constraints imposed by patient characteristics, health care system characteristics, budgets, and so forth relies heavily on the design of structures and processes. Such problems are complex and require a rigorous and systematic approach to identify the best solution. Constrained optimization is a set of methods designed to identify efficiently and systematically the best solution (the optimal solution) to a problem characterized by a number of potential solutions in the presence of identified constraints. This report identifies 1) key concepts and the main steps in building an optimization model; 2) the types of problems for which optimal solutions can be determined in real-world health applications; and 3) the appropriate optimization methods for these problems. We first present a simple graphical model based on the treatment of “regular” and “severe” patients, which maximizes the overall health benefit subject to time and budget constraints. We then relate it back to how optimization is relevant in health services research for addressing present day challenges. We also explain how these mathematical optimization methods relate to simulation methods, to standard health economic analysis techniques, and to the emergent fields of analytics and machine learning.     

Dynamic optimization of chemotherapy outpatient scheduling with uncertainty

Chemotherapy outpatient scheduling is a complex, dynamic, uncertain problem. Chemotherapy centres are facing increasing demands and they need to increase their efficiency; however there are very few studies looking at using optimization technology on the chemotherapy scheduling problem. We address dynamic uncertainty that arises from requests for appointments that arrive in real time and uncertainty due to last minute scheduling changes. We propose dynamic template scheduling, a novel technique that combines proactive and online optimization and we apply it to the chemotherapy outpatient scheduling problem. We create a proactive template of an expected day in the chemotherapy centre using a deterministic optimization model and a sample of appointments. As requests for appointments arrive, we use the template to schedule them. When a request arrives that does not fit the template, we update the template online using the optimization model and a revised set of appointments. To accommodate last minute additions and cancellations to the schedule, we propose a shuffling algorithm that moves appointment start times within a predefined time limit. We test the use of dynamic template scheduling against the optimal offline solution and the actual performance of the cancer centre. We find improvements in makespan of up to 20 % when using dynamic template scheduling compared to current practice.     

Stochastic programming for outpatient scheduling with flexible inpatient exam accommodation

This study is concerned with the determination of an optimal appointment schedule in an outpatient-inpatient hospital system where the inpatient exams can be cancelled based on certain rules while the outpatient exams cannot be cancelled. Stochastic programming models were formulated and solved to tackle the stochasticity in the procedure durations and patient arrival patterns. The first model, a two-stage stochastic programming model, is formulated to optimize the slot size. The second model further optimizes the inpatient block (IPB) placement and slot size simultaneously. A computational method is developed to solve the second optimization problem. A case study is conducted using the data from Magnetic Resonance Imaging (MRI) centers of Lahey Hospital and Medical Center (LHMC). The current schedule and the schedules obtained from the optimization models are evaluated and compared using simulation based on FlexSim Healthcare. Results indicate that the overall weighted cost can be reduced by 11.6% by optimizing the slot size and can be further reduced by an additional 12.6% by optimizing slot size and IPB placement simultaneously. Three commonly used sequencing rules (IPBEG, OPBEG, and a variant of ALTER rule) were also evaluated. The results showed that when optimization tools are not available, ALTER variant which evenly distributes the IPBs across the day has the best performance. Sensitivity analysis of weights for patient waiting time, machine idle time and exam cancellations further supports the superiority of ALTER variant sequencing rules compared to the other sequencing methods. A Pareto frontier was also developed and presented between patient waiting time and machine idle time to enable medical centers with different priorities to obtain solutions that accurately reflect their respective optimal tradeoffs. An extended optimization model was also developed to incorporate the emergency patient arrivals. The optimal schedules from the extended model show only minor differences compared to those from the original model, thus proving the robustness of the scheduling solutions obtained from our optimal models against the impacts of emergency patient arrivals.

     

Testing the equality of two survival functions with right truncated data

To compare the survival functions based on right-truncated data, Lagakos et al. proposed a weighted logrank test based on a reverse time scale. This is in contrast to Bilker and Wang, who suggested a semi-parametric version of the Mann-Whitney test by assuming that the distribution of truncation times is known or can be estimated parametrically. The approach of Lagakos et al. is simple and elegant, but the weight function in their method depends on the underlying cumulative hazard functions even under proportional hazards models. On the other hand, a semi-parametric test may have better efficiency, but it may be sensitive to misspecification of the distribution of truncation times. Therefore, this paper proposes a non-parametric test statistic based on the integrated weighted difference between two estimated survival functions in forward time. The comparative results from a simulation study are presented and the implementation of these methods to a real data set is demonstrated.     

35 498例伤害住院患者伤害发生类型和外部原因帕累托图分析

目的分析某院伤害住院患者伤害发生类型和伤害发生的外部原因按ICD-10分类汇总情况,为合理配置医疗资源,加强该地区的伤害性疾病防治工作提供可靠依据。方法运用帕累托图法,对该院10年间35498例伤害住院患者伤害发生类型和伤害发生的外部原因进行分析。结果10年间该院伤害住院患者人数逐年上升。利用帕累托图分析发现伤害住院患者伤害发生类型主要有头部损伤,下肢损伤,肩及上肢损伤,各类中毒;伤害发生的外部原因主要有交通事故,跌倒,砸伤、玻璃和刀刺割伤、机器事故。结论采用帕累托图对伤害住院患者伤害发生类型和伤害发生的外部原因构成情况进行分析,找出了该地区伤害患者疾病谱,为医院和相关部门进一步完善该地区急救体系,优化资源配置,进一步做好伤害预防和控制工作提供数据支持。     

四害密度监测数据管理上报系统的建立

目的 :建立四害密度监测数据管理及数据上报系统。方法 :编制四害密度监测数据库及其数据处理、管理程序 ,以电子邮件附件方式及其数据接口实现监测点与监测数据处理中心之间的数据交换传输。结果 :形成监测数据处理中心的四害密度监测数据管理系统以及具备数据上报功能的监测点的管理系统。结论 :系统的建立解决了因常规邮件造成的迟报、漏报现象 ,提高了监测数据管理水平。同时表明 ,使用电子邮件附件方式足以解决监测点与监测数据处理中心的监测数据交换问题 ,且便于基层推广使用     

C语言自动命题系统的设计与开发

本文分析了传统考试命题方式的弊端,提出了利用计算机进行辅助命题的可行性和必要性,详细介绍了"C语言自动命题系统"的试题库和系统功能的实现方法。本系统的开发可辅助教师出具试卷,在一定程度上减轻教师工作负担。     

病案ABC分类管理统计分析

目的 研究病案ABC分类管理的原理,评价病案ABC分类管理的效率和效益。解决病案管理中使用人员多和库房面积小的实际难题。方法 使用一所大型综合性医院病案管理统计数据,采用帕莱托图(V.F.D.Pareto)ABC和卡方检验两种方法进行分析。结果 占借阅人数13.25％的科研人员借阅病案卷数占84.05％,而占借阅人数61.14％的社会人员只占借阅卷数的4.62％。使用范围在保管年限一年内的病案占76.92％,而10年左右者仅占2.5％,呈明显的帕莱托比例规律,卡方检验P<0.00001。结论 病案借阅的人员分布和时期分布符合帕莱托定律,按帕莱托定律管理病案节省人力和库房,值得推广。     

Analysis of a pharmaceutical risk sharing agreement based on the purchaser's total budget

Many public and private healthcare payers use formularies as a tool for controlling drug costs and quality. Although the price per dose is often negotiated as part of the formulary listing, payers may still face unlimited financial risk if demand is much greater than expected at the time of listing. The requirement for drug manufacturers to submit a budget impact analysis as part of the drug approval process suggests that payers are concerned not only with the cost effectiveness of a proposed drug but also with the potential increase in total expenditures that may result from new formulary listings. In this paper we define and analyze a model for financial risk sharing based on the total budget. Our analysis focuses on optimal decision making by manufacturers in the presence of a specific risk sharing agreement. We derive a manufacturer's optimal statement of budget impact and discuss several properties of the optimal solution.