The impact of consumer price forecasting behaviour on the bullwhip effect

As prices fluctuate over time, a strategic consumer may buy more in advance to reduce his or her future needs in anticipation of higher prices in the future, or may choose to postpone a purchase in anticipation of lower prices in the future. We investigate the bullwhip effect from a consumer price forecasting behavioural perspective in the context of a simple two-level supply chain composed of a supplier and a retailer. We consider two different forms for the demand function – linear and iso-elastic demand functions, both depending on the prices in multiple periods. Assuming that the retailer employs an order-up-to inventory policy with exponential smoothing forecasting technology, we derive analytical expressions for the bullwhip effect under the two demand functions, and extend the results to the multiple-retailer case. We find that consumer forecasting behaviour can reduce the bullwhip effect, most significantly when the consumer sensitivity to price changes is medium (approximately 0.5) for both the demand forms. In addition, for iso-elastic demand, the mitigation of the bullwhip effect induced by consumer price forecasting behaviour becomes more significant as the product price sensitivity coefficient and standard deviation of the price decrease. These findings are applicable to the development of managerial strategies by supply chain members that are conducive to bullwhip effect reduction through customer behaviour.     

How big data and analytics reshape the wearable device market – the context of e-health

The wearable device can be a key link between health care and big data and analytics (BDA). The benefits of BDA in health care have been widely acknowledged, but the uncertainty of the implementation of BDA has led some firms to hesitate in adopting this technology. In this research, we are keen to answer the key questions of whether the wearable device firms would adopt the BDA strategy, and how much effort they would put into it. We propose a competition model between the wearable device firms with and without BDA strategies, and consider the consumer’s preference towards BDA and network effects. Our model demonstrates that the investment on BDA directly affects the firm’s equilibrium price, market share and profit and at the same time, this strategy also affects the rival’s performances. When the firm with BDA strategy adopts different competition strategy: conservative or expansive, the outcomes of market are different. We also find that different market structures, fully covered and partially covered, have different impacts on the firms’ competition when the consumers have heterogeneous preference on BDA strategy.     

基于大数据简约及PCA改进RBF网络的短期电力负荷预测

针对电力负荷大数据化越发突出,引入最小绝对值收敛及选择（Lasso）算法解决电力负荷大数据难题,对电力负荷及相关天气因素大数据进行高维数据特征提取,获得有用数据集。为避免输入空间严重自相关及网络维数较高,造成径向基函数（RBF）神经网络预测精度严重下降的不良影响,提出基于主元分析(PCA)改进的RBF神经网络电力负荷预测模型,消除多气象因素相关性,剔除冗余,提取天气因素特征量,将新天气特征量与历史负荷数据共同作为RBF网络的建模对象,既全面表征天气因素对电力负荷的影响,又简化预测模型,加快预测速率。 经美国南部某地区实际电力负荷数据的预测分析,充分证明该方法的有效性及可靠性。     

Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques

The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study we examined to solve these problems described by (1) extracting region-of-interest in the images (2) vehicle detection based on instance segmentation, and (3) building deep learning model based on the key features obtained from input parking images. We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces. Image augmentation techniques were performed using edge detection, cropping, refined by rotating, thresholding, resizing, or color augment to predict the region of bounding boxes. A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling, training, validating and testing on parking video frames through video-camera. The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6% than previous reported methodologies. Moreover, this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection. The results are verified using Python, TensorFlow, OpenCV computer simulation frameworks.     

基于大数据的消费者行为和餐饮空间规划研究

目的研究在大数据背景下,餐饮空间规划和消费者行为之间的关系,为优化空间规划提供依据。方法研究消费者的行为流程模式,找到影响空间规划的行为数据,分析这些行为数据和餐厅空间规划之间的关系。结论寻座和离座行为产生的交通数据是影响餐饮空间规划的直接因素,等位和就餐行为产生的时间、消费数据是间接影响因素。关于行为—体验—空间的数据关联分析,可作为优化空间规划的依据,能增加消费者的满意度和餐厅的翻台率。     

Using big data to make better decisions in the digital economy

The question this special issue would like to address is how to harvest big data to help decision-makers to deliver better fact-based decisions aimed at improving performance or to create better strategy? This special issue focuses on the big data applications in supporting operations decisions, including advanced research on decision models and tools for the digital economy. Responds to this special issue was great and we have included many high-quality papers. We are pleased to present 13 of the best papers. The techniques presented include data mining, simulation and expert system with applications span across online reviews, food retail chain to e-health.     

大数据下的深度学习研究

给出了大数据和机器学习的子领域——深度学习的概念,阐述了深度学习对获取大数据中的有价值信息的重要作用。描述了大数据下利用图像处理单元(GPU)进行并行运算的深度学习框架,对其中的大规模卷积神经网络(CNN)、大规模深度置信网络(DBN)和大规模递归神经网络(RNN)进行了重点论述。分析了大数据的容量、多样性、速率特征,介绍了大规模数据、多样性数据、高速率数据下的深度学习方法。展望了大数据背景下深度学习的发展前景,指出在不远的将来,大数据与深度学习融合的技术将会在计算机视觉、机器智能等多个领域获得突破性进展。     

需求不确定下考虑顾客策略行为的易逝品定价策略

在市场需求不确定且顾客具有策略行为时，研究易逝品厂商动态定价和价格承诺策略。通过建立厂商和顾客的不完全信息博弈模型，分析了两竞争厂商在2种定价策略下的精炼贝叶斯均衡，求得了均衡定价和期望收益。利用数值分析进一步比较2种定价策略的最佳适用范围，并讨论了需求预期、顾客策略程度和顾客购买意愿对均衡结果的影响。研究表明，在动态定价策略下，当市场处于适度竞争时，顾客策略程度越大，厂商收益越大；顾客购买意愿适中时，动态定价策略更优；顾客策略程度适中，或顾客策略程度较大且需求预期也较大时，价格承诺策略更优。     

Transactive Demand Response Operation at the Grid Edge using the IEEE 2030.5 Standard

This paper presents a transactive demand response (TDR) scheme for a network of residential customers with generation assets that emphasizes interoperability within a transactive energy architecture. A complete laboratory-based implementation provides the first (to our knowledge) realization of a comprehensive TDR use case that is fully compliant with the Institute of Electrical and Electronics Engineers (IEEE) 2030.5 standard, which addresses interoperability within a cybersecure smart energy profile (SEP) context. Verification is provided by a full system integration with commercial hardware using Internet Protocol (IP)-based (local area network (LAN) and Wi-Fi) communication protocols and transport layer security (TLS) 1.2 cryptographic protocol, and validation is provided by emulation using extensive residential smart meter data. The demand response (DR) scheme is designed to accommodate privacy concerns, allows customers to select their DR compliance level, and provides incentives to maximize their participation. The proposed TDR scheme addresses privacy through the implementation of the SEP 2.0 messaging protocol between a transactive agent (TA) and home energy management system (HEMS) agents. Customer response is handled by a multi-input multi-output (MIMO) fuzzy controller that manages negotiation between the customer agent and the TA. We take a multi-agent system approach to neighborhood coordination, with the TA servicing multiple residences on a common transformer, and use a reward mechanism to maximize customer engagement during the event-based optimization. Based on a set of smart meter data acquired over an extended time period, we engage in multiple TDR scenarios, and demonstrate with a fully-functional IEEE 2030.5-compliant implementation that our scheme can reduce network peak power consumption by 22% under realistic conditions.     

Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects

Big data has recently been recognised as one of the most important areas of future technology. It has attracted the attention of many industries, since it has the potential to provide companies with high business value. This paper examines the forms of business value that companies can create from big data analytics investments, the direct impacts it has on the financial performance of a firm, and the mediating effects of market performance and customer satisfaction. Drawing on the resource-based view theory, this study demonstrates that the business value achieved from investments in big data analytics leads to advantages in terms of the financial performance of a firm. The results offer evidence of the existence of a customer satisfaction mediation effect and of the absence of a market performance mediation effect. Theoretical and practical implications are discussed at the end of the paper.     

基于遗传算法和灰色神经网络的电力机车产品需求预测方法

目的 应对快速多变的市场,提前预知市场发展,制定相应的排产计划,使企业在竞争中占据先发优势。方法 目前基于灰色神经网络的预测算法,准确地预测产品需求通常需要连续且大量的样本数据,对小数据非线性系统的预测结果精确度低、可靠性差,针对这一问题,提出一种耦合遗传算法的灰色神经网络预测方法,综合灰色模型和神经网络理论,构建了面向产品订单量需求预测的灰色神经网络模型;通过电力机车产品实例分析了模型的预测性能;为解决预测过程中模型早熟收敛的问题,利用遗传算法对训练网络的权重和阈值进行了迭代优化。结论 研究结果表明,优化后产品预测模型的精确性和鲁棒性得到提高,验证了所设计方法的可行性。     

神经网络法及其在暖通空调负荷预测中的应用

综述了人工神经网络的发展历史及优缺点,阐述了人工神经网络模型的改进及在暖通空调负荷预测方面的应用,并展望了进一步的研究方向。     

负载预测更新下的实时电价Stackelberg博弈模型

智能电网环境下,在实时电价Stackelberg博弈模型的基础上引入负载预测,以匹配实时负载和预测负载为目标,设计售电商与用户之间的主从博弈模型以及负载预测更新下的实时定价机制,得到双方的最优实时电力价格和最优用电行为。通过将当日实时电价机制均衡状态下的负载时间序列融入电力供应商电力价格权重时间序列向量,得到进一步优化的日前定价实时电价下的均衡负载时间序列,构成整体不断推进不断优化的闭环。同时,给出实时负载与预测负载序列的匹配程度评价指标与判断标准。通过数值仿真分析,在与未优化的实时定价机制对比以后,发现所提出的负载预测更新下的实时定价机制能够在提高电网运行效率的同时显著降低电力用户用电成本。     

基于多传感器融合的电喷汽油机过渡工况进气流速预测模型

为了解决车用汽油发动机工作在过渡工况时,进气状态变化大,空气流量传感器的滞后响应严重影响空燃比控制精度的问题,提出了一种基于多传感器融合的过渡工况进气流速的预测模型,建立了过渡工况进气流速预测的径向基神经网络的拓朴结构,以HL495Q电喷汽油机加减速工况实验数据进行离线训练,仿真结果表明该预测模型能准确地预测过渡工况的空气进气流速,为精确及时地测试汽油机空气进气流量提供了一种新的方法.     

Load management as a smart grid concept for sizing and designing of hybrid renewable energy systems

Optimal sizing of hybrid renewable energy systems (HRES) to satisfy load requirements with the highest reliability and lowest cost is a crucial step in building HRESs to supply electricity to remote areas. Applying smart grid concepts such as load management can reduce the size of HRES components and reduce the cost of generated energy considerably. In this article, sizing of HRES is carried out by dividing the load into high- and low-priority parts. The proposed system is formed by a photovoltaic array, wind turbines, batteries, fuel cells and a diesel generator as a back-up energy source. A smart particle swarm optimization (PSO) algorithm using MATLAB is introduced to determine the optimal size of the HRES. The simulation was carried out with and without division of the load to compare these concepts. HOMER software was also used to simulate the proposed system without dividing the loads to verify the results obtained from the proposed PSO algorithm. The results show that the percentage of division of the load is inversely proportional to the cost of the generated energy.     

Modelling quality dynamics,business value and firm performance in a big data analytics environment

Big data analytics have become an increasingly important component for firms across advanced economies. This paper examines the quality dynamics in big data environment that are linked with enhancing business value and firm performance (FPER). The study identifies that system quality (i.e. system reliability, accessibility, adaptability, integration, response time and privacy) and information quality (i.e. completeness, accuracy, format and currency) are key to enhance business value and FPER in a big data environment. The study also proposes that the relationship between quality and FPER is mediated by business value of big data. Drawing on the resource-based theory and the information systems success literature, this study extends knowledge in this domain by linking system quality, information quality, business value and FPER.     

Technology Landscape for Epidemiological Prediction and Diagnosis of COVID-19

The COVID-19 outbreak initiated from the Chinese city of Wuhan and eventually affected almost every nation around the globe. From China, the disease started spreading to the rest of the world. After China, Italy became the next epicentre of the virus and witnessed a very high death toll. Soon nations like the USA became severely hit by SARS-CoV-2 virus. The World Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the world has instituted various policies like physical distancing, isolation of infected population and researching on the potential vaccine of SARS-CoV-2. To identify the impact of various policies implemented by the affected countries on the pandemic spread, a myriad of AI-based models have been presented to analyse and predict the epidemiological trends of COVID-19. In this work, the authors present a detailed study of different artificial intelligence frameworks applied for predictive analysis of COVID-19 patient record. The forecasting models acquire information from records to detect the pandemic spreading and thus enabling an opportunity to take immediate actions to reduce the spread of the virus. This paper addresses the research issues and corresponding solutions associated with the prediction and detection of infectious diseases like COVID-19. It further focuses on the study of vaccinations to cope with the pandemic. Finally, the research challenges in terms of data availability, reliability, the accuracy of the existing prediction models and other open issues are discussed to outline the future course of this study.     

Forecasting Damage Mechanics By Deep Learning

We in this paper exploit time series algorithm based deep learning in forecasting damage mechanics problems. The methodologies that are able to work accurately for less computational and resolving attempts are a significant demand nowadays. Relied on learning an amount of information from given data, the long short-term memory (LSTM) method and multi-layer neural networks (MNN) method are applied to predict solutions. Numerical examples are implemented for predicting fracture growth rates of L-shape concrete specimen under load ratio, single-edge-notched beam forced by 4-point shear and hydraulic fracturing in permeable porous media problems such as storage-toughness fracture regime and fracture-height growth in Marcellus shale. The predicted results by deep learning algorithms are well-agreed with experimental data.