流动性约束与消费行为关系的实证研究

流动性约束是指资本市场的资金流动具有单向性,流动性约束的存在可以提高储蓄水 平,降低当期累积消费. 通过三阶段生命期界的最优消费模型,证明了流动性约束的定量存在, 并通过实证检验发现,我国消费总量中受流动性约束影响的比例达到83. 46 %. 因此,增强信 贷市场的流动性,对于扩张有效需求将起重要作用.     

需求不确定下的补偿策略理论模型

在需求不确定的前提条件下就供应商对销售商的补偿策略进行了深入研究,建立了包 含风险偏好的补偿策略理论模型. 在该理论模型的基础上,运用数值分析的手段分析了风险偏 好对供应链中参与者决策行为的影响,为供应链决策者制定合理的决策提供了科学依据. 最 后,结合我国国情,重点讨论如何在我国正确实施补偿策略.     

Modeling the demand reduction input-output (I-O) inoperability due to terrorism of interconnected infrastructures.

Interdependency analysis in the context of this article is a process of assessing and managing risks inherent in a system of interconnected entities (e.g., infrastructures or industry sectors). Invoking the principles of input-output (I-O) and decomposition analysis, the article offers a framework for describing how terrorism-induced perturbations can propagate due to interconnectedness. Data published by the Bureau of Economic Analysis Division of the U.S. Department of Commerce is utilized to present applications to serve as test beds for the proposed framework. Specifically, a case study estimating the economic impact of airline demand perturbations to national-level U.S. sectors is made possible using I-O matrices. A ranking of the affected sectors according to their vulnerability to perturbations originating from a primary sector (e.g., air transportation) can serve as important input to risk management. For example, limited resources can be prioritized for the "top-n" sectors that are perceived to suffer the greatest economic losses due to terrorism. In addition, regional decomposition via location quotients enables the analysis of local-level terrorism events. The Regional I-O Multiplier System II (RIMS II) Division of the U.S. Department of Commerce is the agency responsible for releasing the regional multipliers for various geographical resolutions (economic areas, states, and counties). A regional-level case study demonstrates a process of estimating the economic impact of transportation-related scenarios on industry sectors within Economic Area 010 (the New York metropolitan region and vicinities).     

非线性需求下具有网络外部性的二级歧视定价研究

研究了非线性需求函数条件下产品存在网络外部性时,垄断企业采用对需求区间分段定价的方法进行二级歧视定价的策略。首先分析了单阶段垄断企业二级价格歧视的静态定价策略,接着给出了在一个较长时期内垄断企业分两阶段进行二级歧视的动态定价策略。结果表明网络外部性不影响最优需求区间分段单调递增的性质,但价格随网络外部性的增强而相应提高,且第一阶段的价格高于第二阶段的贴现值。     

基于可控提前期和延迟供货的短生命周期产品库存模型

本文用斜坡型函数来描述短生命周期产品需求变化特征,用持有成本不断增加的形式来表示存货的无形变质所带来的损失,通过向供应商支付额外的赶工成本来缩短提前期,同时在允许缺货的情况下设置合理的缺货期价格折扣来控制订单的流失,即以价格折扣的损失来挽留住更多的顾客,使其总成本到达最优。本文在此基础上建立了短生命周期产品的库存模型,并提出了最优解的求解方法。     

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously.

In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems.     

A Data‐Driven Approach to Assessing Supply Inadequacy Risks Due to Climate‐Induced Shifts in Electricity Demand

The U.S. electric power system is increasingly vulnerable to the adverse impacts of extreme climate events. Supply inadequacy risk can result from climate‐induced shifts in electricity demand and/or damaged physical assets due to hydro‐meteorological hazards and climate change. In this article, we focus on the risks associated with the unanticipated climate‐induced demand shifts and propose a data‐driven approach to identify risk factors that render the electricity sector vulnerable in the face of future climate variability and change. More specifically, we have leveraged advanced supervised learning theory to identify the key predictors of climate‐sensitive demand in the residential, commercial, and industrial sectors. Our analysis indicates that variations in mean dew point temperature is the common major risk factor across all the three sectors. We have also conducted a statistical sensitivity analysis to assess the variability in the projected demand as a function of the key climate risk factor. We then propose the use of scenario‐based heat maps as a tool to communicate the inadequacy risks to stakeholders and decisionmakers. While we use the state of Ohio as a case study, our proposed approach is equally applicable to all other states.     

Agent‐Based Recovery Model for Seismic Resilience Evaluation of Electrified Communities

In this article, an agent‐based framework to quantify the seismic resilience of an electric power supply system (EPSS) and the community it serves is presented. Within the framework, the loss and restoration of the EPSS power generation and delivery capacity and of the power demand from the served community are used to assess the electric power deficit during the damage absorption and recovery processes. Damage to the components of the EPSS and of the community‐built environment is evaluated using the seismic fragility functions. The restoration of the community electric power demand is evaluated using the seismic recovery functions. However, the postearthquake EPSS recovery process is modeled using an agent‐based model with two agents, the EPSS Operator and the Community Administrator. The resilience of the EPSS–community system is quantified using direct, EPSS‐related, societal, and community‐related indicators. Parametric studies are carried out to quantify the influence of different seismic hazard scenarios, agent characteristics, and power dispatch strategies on the EPSS–community seismic resilience. The use of the agent‐based modeling framework enabled a rational formulation of the postearthquake recovery phase and highlighted the interaction between the EPSS and the community in the recovery process not quantified in resilience models developed to date. Furthermore, it shows that the resilience of different community sectors can be enhanced by different power dispatch strategies. The proposed agent‐based EPSS–community system resilience quantification framework can be used to develop better community and infrastructure system risk governance policies.     

融合内容分析和关联分析的短生命周期体验品需求特征模式挖掘方法研究

挖掘特定产品的需求模式无法从整体掌握该类产品的市场特征;短生命周期体验品因缺乏历史销售数据,并且销售总量波动性极大,尤其需要从整体掌握销售总量与产品属性间关系的需求特征规律,但又难以挖掘,亟待提出适用于该类产品的需求特征模式挖掘方法。基于按销售总量分区后各区的需求特征的规律性,提出了一种按销售总量分区、以已有产品介绍集和销售总量为源信息、适用于新产品开发前使用、融合内容分析和关联分析的短生命周期体验品需求特征模式挖掘方法。该方法包括基于内容分析法的产品属性挖掘方法和基于关联分析的产品属性关系模式挖掘方法。前者可以得到较全面的产品属性;后者能够构建不同销售总量区间内产品集的属性关系模式,得到各区间的产品属性关系网,获得高销售总量区间具备,但中、低区间不具备的属性关系模式,从而获得需求特征模式。通过不断更新产品介绍集和销售总量并迭代挖掘,该方法能够动态挖掘需求特征模式。最后利用2013至2016年国产犯罪和爱情类电影数据验证了该方法的可行性,并得到了这两类电影的产品属性及近年的需求特征模式,可用于指导这两类电影的创作。