金融冲击与中央银行的流动性支持政策研究

本文在DSGE框架下引入金融冲击,比较完整地刻画了包含货币政策、宏观审慎政策以及流动性支持政策的央行“政策工具箱”,并对中央银行的流动性支持政策在不同外生冲击下的经济金融稳定效应进行了系统考察。本文分析得到了三个基本结论:一是在各种典型的金融冲击下,中央银行的流动性支持政策均具有良好的宏观稳定效应;二是从政策实施的角度,中央银行的流动性支持政策可考虑盯住银行存款的变动,并在政策操作上体现“及时反应、力度合理”的原则;三是从社会福利增进的角度,中央银行流动性支持政策的效果受到冲击来源的影响,总体上应对金融冲击的效果较好。分析结果初步显示了流动性支持政策作为中央银行“政策工具箱”一种辅助性金融稳定工具的可行性、针对性和有效性。     

基于行政大数据的失业率估计:以某四百万人口城市为例

我国城镇登记失业率指标稳定在4%左右,难以较为准确反映就业动态;而劳动力调查样本量有限,城镇调查失业率对省以下各级行政区域代表性不足。本文将针对大数据的机器学习算法与针对传统统计数据的核算思想结合起来,基于某四百万人口城市2016—2018年的全样本行政大数据,利用机器学习算法,对每个城镇居民每个月的就业状态进行预测,再利用统计核算方法,估计出该城市的失业率。在个人层面,本文的模型在样本外测试集上的准确率达到96.7%。经过统计核算加总,本文估计的当地失业率在合理区间范围内,并表现出明显的周期性特征,对就业形势动态变化的刻画明显优于当地一年发布一次的登记失业率数据。本文基于个人层面的预测结果,进一步探讨了当地失业人口 的性别与文化程度特征,以及再就业的时间规律。本文针对如何使用行政大数据辅助经济决策提出了新的范式,对大数据时代如何理解经济与制定政策具有参考意义。     

一种改进的TSP问题启发式算法

旅行推销商问题(TSP)属于组合优化领域中一个典型的NP Hard问题。本文在最近城市搜索法的基础上,提出一种改进的启发式算法———两端延伸最近城市搜索法,这种方法能够很快得到最优解(近优解),且大大降低了计算复杂度。同时,对TSP问题进行了分类,并给出相应的启发式解法。     

A heuristic approach for component scheduling on a high-speed PCB assembly machine

The collect-and-place machine is one of the most widely used placement machines for assembling electronic components on the printed circuit boards (PCBs). Nevertheless, the number of researches concerning the optimisation of the machine performance is very few. This motivates us to study the component scheduling problem for this type of machine with the objective of minimising the total assembly time. The component scheduling problem is an integration of the component sequencing problem, that is, the sequencing of component placements; and the feeder arrangement problem, that is, the assignment of component types to feeders. To solve the component scheduling problem efficiently, a hybrid genetic algorithm is developed in this paper. A numerical example is used to compare the performance of the algorithm with different component grouping approaches and different population sizes.     

Decision approach to workload distribution

Abstract. This paper deals with decisions of workload temporal distribution in scheduling discrete and diversified productions. A new way of formulating the scheduling problem is proposed, from which some concepts and tools are presented. The notion of time resource interval objects, TRIs, allows the management of technical (time and resource) aspects at the different levels of a hierarchical structuring of the set of decisions taken in the workshop, from ‘load distribution’ types, to ‘effective realization of the operations’ types. Constraint-based reasoning handles different TRIs corresponding to given kinds of decisions. It helps to highlight the bounds or limits to be respected while deciding, to remain consistent with an initial set of constraints, issued for example from an upper level of decisions. Decisions of load temporal distribution consist in readjustments of some time constraints on a set of planned operations, by taking into account the (or some more detailed) constraints on the resource(s) on which they have been planned, such as finite capacity and/or minimal profitability. The analysis on temporal proximities of the planned operations involves some particular structuring of the time axis into successive time intervals: these structures are associated with sets of temporal bounds, and are called adjacent decompositions of the time axis. Such a decomposition introduces some specific TRIs, associated with load constraints (coming from the planned operations), and resource constraints (coming from limited quantities of resource, or profitability concerns). By respecting the given time and resource constraints, they can ‘exchange’ some quantities of load according to communicating vessels processes. These phenomena have been modelled as bounded flows in a temporal network, and offer new flexible curves of load with finite capacities, to help the decision.     

The role of stress and social support in Sick Building Syndrome

Sick Building Syndrome (SBS) involves seemingly healthy individuals experiencing long-lasting symptoms of physical distress in their work settings and is estimated to exist in 20 to 30% of all work settings in the USA. We examined differences in stress, social support and both physical and psychological symptoms reported by hospital personnel working in known SBS sites in Halifax, Nova Scotia (n = 297) with control employees working in relatively SBS-free settings (n = 228). We found that employees reporting that their health had been affected by the building in which they worked and those complaining of poor air quality were more likely to be found in SBS locations. In addition, those people with higher levels of organizational support and marginally higher levels of union support were also more likely to be found in SBS locations. Further analyses revealed that employees with higher role overload and greater family support, but lower levels of organizational support were more likely to report that their own health had been adversely affected by their place of work. Perceptions of poor air quality were predicted by higher levels of role conflict, role overload, and organizational stress and lower levels of organizational support. These results suggest that SBS may not be solely dependent on environmental factors.     

双旋转货架拣货作业优化设计

本文讨论了由一个拣货员负责的双旋转货架系统,目标是寻找一种最优拣货顺序使得总拣货时间。该问题被证明为强NP-难问题,文章提出了启发式算法寻找最优拣货顺序,并对相关模型及算法进行模拟测试以证明其有效性,为方法选择与应用提供了依据。     

多元随机风险传染模型及沪铜场内外风险传染实证

为了更合适的度量金融市场间风险传染整体效果,本文通过理论分析构造了风险传染方程,其风险传染项相比已有的系数风险传染项与协方差时变风险传染项更具实际意义,并借鉴多元GARCH建模原理建立了结合均值溢出、波动溢出与风险传染项的多元随机风险传染模型,设计了模型的MCMC迭代求解算法,满足解的完整性。最后,运用模型对沪铜场内外风险传染现象进行了实证,实证结果不仅验证了一系列已有研究结论,同时还给出了一些符合期货实情的新结论,如金融市场间风险传染类似金融市场波动存在集聚效应、沪铜与沪铝市场存在风险传染交替变化现象、市场行情的变化能提前反映于风险传染效果中等。这也充分表明新模型的有效性、实用性以及优越性。     

基于多分类器动态选择与成本敏感优化集成的电信客户流失预测研究

针对不同样本在特征空间中具有不同的区域特性和不同分类算法之间的预测互补性,在电信客户流失预测理论基础上,融合多分类器动态集成理论和成本敏感学习理论,建立了电信客户流失多分类器集成预测的利润函数,并提出了一类新的基于多分类器动态选择与成本敏感优化集成的电信客户流失预测模型.首先使用K均值聚类法聚类训练样本成多个分区;接着使用NaiveBayes算法、多层感知机算法和J48算法在各分区样本上构建客户流失预测子分类器;最后使用改进人工鱼群算法分别对各分区的子分类器进行成本敏感优化集成.实验结果表明,所提出的基于多分类器动态选择与成本敏感优化集成模型的分类性能不仅优于由训练集全体样本所构建的3个单模型,也优于基于改进人工鱼群算法优化集成这3个单模型而得到的集成模型.     

Supply Chains in Sub‐Saharan Africa: A Decision Support System for Small‐Scale Seed Entrepreneurs*

It is necessary to infuse a consistent supply of improved seed varieties into local sub‐Saharan African crop production to improve low crop yields. The best distribution channel for the improved seed varieties may be small‐scale commercial seed companies, but local entrepreneurs struggle to determine whether such businesses are viable. Using a multi‐echelon supply chain approach, a decision support system (DSS) was designed to help African seed entrepreneurs make informed decisions about small‐scale seed chain businesses. Specifically, entrepreneurs make decisions about where to locate seed enterprises, with which farmers to contract, and where to store seed. Optimization and simulation modeling are used to evaluate infrastructure variables such as distance, transportation cost, and storage loss and cost in three development level areas. Currently, the decision tool is used in Mozambique, Malawi, Kenya, and Tanzania. The model has supported the start‐up of at least 17 small seed companies that are now introducing improved seed varieties into villages and farms. The DSS applies decision science research in a humanitarian application and offers important managerial implications about supply chain infrastructure to nongovernmental organizations and humanitarian groups. Such applications are vital as groups such as USAID, the Gates Foundation, and the International Crops Research Institute for the Semi‐Arid Tropics (ICRISAT) continue to move toward micro‐enterprise, value chain, and market‐oriented development programs.