全货运航空公司单分配严格枢纽航线网络优化设计

针对全货运航空公司，研究其货运航线网络的构建问题。基于单分配严格枢纽航线网络，对传统模型进行改进。以各航线上货物的委托运输成本和自有机队的运输成本，取代传统的单位运输成本和折扣因子，考虑枢纽点的中转成本和载运率对路径选择的影响。结合航空货运夜航的特征考虑各机队夜间可用时间的限制，以总成本最小为目标，构建数学模型，采用LINGO优化软件求解，验证模型的有效性，为全货运航空公司构建货运航线网络提供参考。     

Big data driven cycle time parallel prediction for production planning in wafer manufacturing

Cycle time forecasting (CTF) is one of the most crucial issues for production planning to keep high delivery reliability in semiconductor wafer fabrication systems (SWFS). This paper proposes a novel data-intensive cycle time (CT) prediction system with parallel computing to rapidly forecast the CT of wafer lots with large datasets. First, a density peak based radial basis function network (DP-RBFN) is designed to forecast the CT with the diverse and agglomerative CT data. Second, the network learning method based on a clustering technique is proposed to determine the density peak. Third, a parallel computing approach for network training is proposed in order to speed up the training process with large scaled CT data. Finally, an experiment with respect to SWFS is presented, which demonstrates that the proposed CTF system can not only speed up the training process of the model but also outperform the radial basis function network, the back-propagation-network and multivariate regression methodology based CTF methods in terms of the mean absolute deviation and standard deviation.     

Work,Care and Gender during the COVID-19 Crisis*

We explore the effects of the COVID-19 crisis and the associated restrictions to economic activity on paid and unpaid work for men and women in the United Kingdom. Using data from the COVID-19 supplement of Understanding Society, we find evidence that labour market outcomes of men and women were roughly equally affected at the extensive margin, as measured by the incidence of job loss or furloughing. But, if anything, women suffered smaller losses at the intensive margin, experiencing slightly smaller changes in hours and earnings. Within the household, women provided on average a larger share of increased childcare needs, but in an important share of households fathers became the primary childcare providers. These distributional consequences of the pandemic may be important to understand its inequality legacy over the longer term.     

Do capital flows drive credit growth and consumption in Central and Eastern Europe?

Domestic credit expansion in CEE economies, fuelled in part by foreign capital inflows, helped increase household welfare before the 2008 financial crisis caused a contraction across the region. How strong are the linkages between the current account, domestic credit and consumer spending? This study compiles a quarterly dataset of domestic credit as a share of GDP for 11 CEE European Union members and isolates structural breaks in the series’ growth rates that often align with the 2008 crisis. Vector autoregressive methods, particularly impulse response functions, show that increased current-account deficits lead to increased consumption in six of the 11 countries and increased credit growth in three, and that shocks to credit growth increase consumption in six countries. Capital inflows significantly increase consumption through domestic credit in Slovenia, while the Baltics show a large share of significant effects.     

Time compression and MBA behavioral skill acquisition

Trends in master of business administration (MBA) education are favoring coursework offerings in time-compressed environments but without careful examination of learning implications. The academic literature is weak in this area, so the authors conducted a study of 558 MBA students using a behaviorally based pedagogy in management. In contrast to previous research, the authors developed arguments supporting time compression assuming certain boundary conditions. They found that students showed better behavioral learning outcomes in a shortened, 4.5-week semester versus a full 14-week semester. The authors conclude with implications for theory and practice.     

An explainable machine learning approach to improve take-off time predictions

Accurate aircraft trajectory predictions are necessary to compute exact traffic demand figures, which are crucial for an efficient and effective air traffic flow and capacity management. At present, the uncertainty of the take-off time is one of the major contributions to the loss of trajectory predictability. In the EUROCONTROL Maastricht Upper Area Control Centre, the predicted take-off time for each individual flight relies on the information received from the Enhanced Traffic Flow Management System. However, aircraft do not always take-off at the times reported by this system due to several factors, which effects and interactions are too complex to be expressed with hard-coded rules. Previous work proposed a machine learning model that, based on historical data, was able to predict the take-off time of individual flights from a set of input features that effectively captures some of these elements. The model demonstrated to reduce by 30% the take-off time prediction errors of the current system one hour before the time that flight is scheduled to depart from the parking position. This paper presents an extension of the model, which overcomes this look-ahead time constraint and allows to improve take-off time predictions as early as the initial flight plan is received. In addition, a subset of the original set of input features has been meticulously selected to facilitate the implementation of the solution in an operational air traffic flow and capacity management system, while minimising the loss of predictive power. Finally, the importance and interactions of the input features are thoroughly analysed with additive feature attribution methods.     

An Exploratory analysis of flight delay propagation in China

Delay propagation is the flight departure delay caused by the arrival delay of pre-segment flight. Chinese airline market has suffered very poor on-time performance (OTP) in recent years. It is, however, unclear whether delay propagation prevails as one major source for such problem. This study first aims to empirically quantify delay propagation in the Chinese airline market. Specifically, we shed light on heterogenous levels of delay propagations across different airports and airlines. Then, the distinct delay propagation patterns in China are also discussed and compared with other developed airline markets (e.g., the US and Europe). Our estimation is based on OTP data for over 12 million Chinese flights covering the 2015–2017 period. Specifically, it is found that 10 min arrival delay of pre-segment flight within 1 hr before the departure lead to an average of 7.49 mins delay propagation for subsequent departure flight. Arrival delay of earlier pre-segments (1–2 and 2–3 hr before the departure) leads to much less delay propagation, due to longer ground buffer. Chinese airlines arrange longer ground and flight buffer than that of the US airlines to prevent the delay propagation from accumulating along the subsequent flights in a day. Thus, unlike the US market, delay propagation is not the major reason for poor OTP in China. In addition, delay propagation is less prevailing at the Chinese hub airport. This is because China has relied on point-to-point network, which does not require sophisticated schedule coordination. And the local passengers at these Chinese hub airports have higher time value such that the Chinese airlines also try to improve OTP at these hub airports to better serve these lucrative but time-sensitive local passengers. Unlike the European LCCs, Spring Airlines, the largest low-cost carrier (LCC) in China, outperforms major full-service carriers (FSCs) in controlling delay propagation. This finding may also apply to other Northeast Asian LCCs sharing common operational characteristics as Spring Airlines. Last, we find that airlines purposely tolerate moderate departure delays of up to 15 min, which is the threshold that defines delays, no matter whether the pre-segment flight arrives late or on-time. The relevant policy and managerial implications are also discussed.     

The mechanism of credit risk contagion among internet P2P lending platforms based on a SEIR model with time-lag

The credit risk contagion of Internet peer-to-peer (P2P) lending platforms is an important part of Internet financial risk management and supervision. This study analyzes the contagion path of credit risk in Internet P2P lending. Based on complex network theory and the theory of infectious disease dynamics, the characteristics of Internet P2P lending development are combined to construct a SEIR model of credit risk transmission among Internet P2P lending platforms with time lag, and the robustness of the model is analyzed and proven. The influence of platform correlations, the susceptible immune rate, the platform elimination rate, contagion latency, the saturation coefficient, and the susceptibility input rate on credit risk contagion behavior among Internet P2P lending platforms is analyzed, using the equilibrium point and threshold value. The impact of each variable is analyzed by simulation. Corresponding countermeasures and suggestions are proposed to prevent and control credit risk contagion among these platforms.     

Real-time co-creation and nowness service: lessons from tourism and hospitality

Brands take advantage of technology, social media and constant connectivity to foster organic consumer engagement and interactions towards co-creating personalised customer service. Real-time service offers dynamic engagement with connected consumers. Brands in tourism and hospitality use technology to dynamically enhance consumer experience through co-creation. The integration of real-time consumer intelligence, dynamic big data mining, artificial intelligence, and contextualisation can transform service co-creation by mobilising recourses in the ecosystem. Nowness service emerges by dynamically engaging consumers in experience cocreation in real time. It has five interconnected characteristics that revolutionise the tourism and hospitality, namely: real-time, co-creation, data-driven, consumer-centric and experience co-creation.     

Company reputation and its influence on consumer trust in response to ongoing CSR communication

There is considerable disagreement in academic scholarship and beyond as to whether, in cases of bad reputations, companies will want to communicate corporate social responsibility (CSR) activities because this kind of communication is likely to increase consumers’ skepticism. In this regard, the two empirical studies discussed in this article examine the influence of prior company reputation on consumers’ trust in response to CSR communication in general and the development in response to continuous CSR activities and their ongoing communication by companies as well as by the media in particular. The two studies show that consumers’ trust in companies’ CSR activities increases after a second presentation of information by companies even in instances when companies had prior negative reputations. The second study in particular fills a gap in the literature on underlying mechanisms of consumers’ reactions to CSR communication. The results suggest that companies with prior negative reputations do not need to remain silent about their CSR activities; instead, CSR communication can help improve consumers’ trust in companies. However, the findings of these two studies also suggest that this positive development is short-lived. Consumers’ developing trust in companies’ CSR activities decreases significantly if they are exposed to negative information on these companies.