考虑次关键路线的基于粒子群算法工期-费用优化研究

工期-费用优化是网络优化技术的核心内容。传统的工期-费用优化研究忽略了次关键路线、资源约束条件对工期压缩的影响。本文研究了次关键路线对工期压缩的影响,描述了求解最低压缩成本的特征路线法,并以此为基础建立了有资源约束的工期-费用优化数学模型。对解进行编码处理后,采用粒子群算法对工期费用优化模型进行求解,并根据求得的最优解来调整工序工期,最终实现工期-费用的最优化。最后,经过工程实例的仿真,证明了模型的合理性和有效性。     

大规模定制生产调度优化模型及其仿真研究——以汽轮机生产为例

针对大规模定制企业复杂的生产计划调度问题,以汽轮机生产为例,构建了同一时间窗口内的生产调度优化模型,用粒子群算法进行求解。并采用汽轮机的生产数据,进行了仿真论证。     

粒子干涉理论在客运专线路基基床表层级配碎石层的应用研究

通过对粒子干涉理论在客运专线路基基床表层级配碎石层的应用研究,提出填料施工中级配选择的一个合理方向———间断级配。     

论宇宙五大力的计算公式

通过三联数字宇宙模型提出两条基本假设:普朗克力为宇宙的大统一力,引力子运动的速度为反速度,在此两条假设的基础上推导出宇宙中普朗克力、强力、弱力、引力及电磁力的计算公式。     

Freight transport network design using particle swarm optimisation in supply chain–transport supernetwork equilibrium

This paper presents a discrete network design problem for optimally designing freight transport network in terms of the efficiency of supply chain. Modelling is undertaken within the framework of mathematical programmes with equilibrium constraints, which first incorporates both supply chain and transport networks explicitly. The upper level determines the best set of actions for transport network improvement, while the lower-level decision is based on a supply chain–multimodal transport supernetwork equilibrium. New variants of particle swarm optimisation are developed to approximately solve the upper level. Numerical tests reveal their superior performance and the effective freight transport-related actions.     

基于免疫粒子群优化神经网络的经济预测算法研究

在分析宏观经济系统特点之后,针对现有的采用人工神经网络对经济系统进行建模和预测的方法存在的问题,文章提出了一种改进的免疫粒子群优化神经网络算法。仿真结果表明：这种算法能够增强神经网络的泛化能力,提高系统模型的预测精度,使预测误差从原来BP神经元网络的15％下降到改进后的5％.     

Should governments fund basic science? Evidence from a willingness-to-pay experiment in five universities

ABSTRACT

Tax-payers are usually the ultimate funders of large-scale research infrastructures (RIs), but the expected discoveries of such projects often do not have any known use-value. By interviewing 1,022 undergraduates, we study the drivers of preferences for paying for basic research, which are still little known. We focus on the LHC at CERN, where the Higgs boson was discovered. Income, awareness, and positive attitudes towards science drive a positive willingness-to-pay for science. Students in social sciences and the humanities are willing to contribute to scientific curricula at least as much as their peers. Findings offer support to government funding of basic research as a public good.     

基于Adaboost-DPSO-SVM模型的供应链金融信用风险评价研究

数字化赋能供应链金融创新是推动我国产业链、供应链持续稳定优化升级的重要内容,在全球金融风险急剧增加的背景下,供应链金融与大数据、区块链、物联网、人工智能等技术相融合,成为解决我国中小企业融资授信问题的有效方式之一。本文在对供应链金融信用风险评价指标进行特征选择的基础上,采用一种动态变异的粒子群算法(DPSO)和AdaBoost算法对SVM进行协同优化和集成,建立了Adaboost-DPSO-SVM模型,并将该模型应用于我国新能源汽车行业供应链金融信用风险评价中,实验结果表明所建立的模型相对其他评价模型具有更好的分类识别性能。     

电镀污泥的基本理化特性研究

分析了12种来源不同的电镀污泥试样的含水率、灰分、pH值等基本理化特性，讨论了电镀污泥的化学组成、矿物组成、重金属含量、粒度分布，以及重金属含量与粒度分布之间关系，为电镀污泥的处理处置提供了大量有用的基本数据。     

Protectionism, Trade, and Measures of Damage from Exotic Species Introductions

Unintentional introductions of nonindigenous plants, animals, and microbes cause significant ecological and agricultural crop damage worldwide. Trade in both manufactured and agricultural goods is a primary vector for such introductions. Fusing simple models of trade and biological introductions, we explore the links between trade, protectionism, and damage arising from exotic species introductions. We show that it is possible for freer trade to reduce damage arising from exotic species invasions. We also show how current measures of damage—heavily weighted toward agricultural damage—serve as misleading indicators of how restrictions to trade affect total losses arising from exotic species introductions.