Pareto分布及其在可靠性设计资源分配等问题中的应用

ParetoⅠ型图是QC小组基本工具之一。介绍ParetoⅡ型图、Pareto分布、Pareto累积分布及连续型Pareto分布函数,推荐几种Pareto分布函数的数学模型,以可靠性设计资源分配实例说明Pareto分布在资源分配合理性分析中的应用,并提出了3种分配合理性指标。

成都市汽油车尾气遥感检测数据排放限值研究

为加强成都市移动源排放管控,采用2021年成都市4个遥感检测点位检测的汽油车尾气排放数据,根据检测环境条件、车辆行驶状态筛选出有效遥感检测数据集,采用广义Pareto分布(GPD)函数对筛选后的数据集进行拟合,使用Kolmogorov-Smirnov检验(简称K-S检验)确定拟合最优的阈值,将最终确定的阈值作为污染物排放限值。结果表明:(1)GPD函数对遥感检测数据集的超阈值样本拟合效果较好,理论GPD函数与实际GPD函数线性拟合的R 2大于0.999;(2)使用K-S检验定量确定CO、碳氢化合物(HC)、NO的排放限值(以体积分数计)分别为2.9%、430×10^(-6)、1400×10^(-6);(3)确定的污染物排放限值处于国内其他地方标准限值的中间水平,表明GPD函数设定的遥感检测数据排放限值比较合理,研究结果为成都市本地标准的制定提供了参考。

p指数和h指数学术影响力评价对比的理论和实证研究

[目的/意义]通过对p指数和h指数学术影响力评价理论和实证的对比研究,阐明两种指数的区别与联系,以及p指数高分辨率形成的科学性和合理性,为p指数在学术评价中的应用提供理论依据。[方法/过程]以吉林大学5个国家重点实验室和2个学院发表论文统计数据为样本,从引文分布函数入手,利用严格的数学推导,对比分析两种指数学术影响力评价特征、相互关联及评价结果异同产生的根源。[结果/结论]对于论文量大、被引次数多的团体,其引文分布函数遵从f(x)=2Tt2(x+t)-3,h指数与论文量(T),篇均引频(t)和p指数的关系满足:h=γT1/3t2/3=γp=0.893p,两种指数学术评价结果具有一致性;对于T少的个人,由于其引文分布偏离函数f(x),导致两种指数的学术评价结果不同;对于h指数相同的个人,其p指数随t增加而增加,使p指数具有比h指数更高的分辨率。

Multi-Objective Genetic Algorithm to Design Manufacturing Process Line Including Feasible and Infeasible Solutions in Neighborhood

This paper treats multi-objective problem for manufacturing process design. A purpose of the process design is to decide combinations of work elements assigned to different work centers. Multiple work elements are ordinarily assigned to each center. Here, infeasible solutions are easily generated by precedence relationship of work elements in process design. The number of infeasible solutions generated is ordinarily larger than that of feasible solutions generated in the process. Therefore, feasible and infeasible solutions are located in any neighborhood in solution space. It is difficult to seek high quality Pareto solutions in this problem by using conventional multi-objective evolutional algorithms. We consider that the problem includes difficulty to seek high quality solutions by the following characteristics： （1） Since infeasible solutions are resemble to good feasible solutions, many infeasible solutions which have good values of objective functions are easily sought in the search process, （2） Infeasible solutions are useful to select new variable conditions generating good feasible solutions in search process. In this study, a multi-objective genetic algorithm including local search is proposed using these characteristics. Maximum value of average operation times and maximum value of dispersion of operation time in all work centers are used as objective functions to promote productivity. The optimal weighted coefficient is introduced to control the ratio of feasible solutions to all solutions selected in crossover and selection process in the algorithm. This paper shows the effectiveness of the proposed algorithm on simple model.