Decisions of E-Commerce Supply Chain under Consumer Returns and Different Power Structures

Considering the growing phenomenon of consumer returns and channel power struggles in e-commerce supply chains (ESCs), the ESC model is constructed and its equilibrium solutions are calculated and compared. Further, the consumer utility function is constructed to explore the impact of returns and dominant enterprises on consumer utility. Based on this, the “return cost-sharing and commission readjusting” contract is designed to maximize both ESC and consumer utility. Finally, the paper validates and further analyzes conclusions through numerical simulation. The main conclusions are as follows: higher return rates and return handling costs will reduce market demand and ESC profits, while higher salvage value of returned products will have a positive impact on ESC, but the above factors will not affect the online service level under decentralized decisions. The impact of consumer’s service quality preferences on manufacturer’s profits and e-commerce platform’s profit is determined by channel power structure. The impact of return rate on consumer utility depends on two factors: the decision-making model and the hidden cost of consumer returns

A Combined Method of Two-model based on Forecasting Meteorological Data for Photovoltaic Power Generation Forecasting

Under the background of the continuous development of photovoltaic power generation technology, accurate prediction of photovoltaic output power has become an important subject. In this paper, a combined method of two-model based on forecasting meteorological data for photovoltaic power generation forecasting is proposed. To solve the problem of the adaptability of a single model, two different models are used according to the different types of output power characteristics. The K-means clustering algorithm is used to classify different weather types according to the historical meteorological data. After predicting the irradiance and temperature of the period to be predicted and classifying the period into different types, the photovoltaic output power is predicted by a suitable model. The two prediction models are the Wavelet- Decomposition-ARIMA model and EDM-SA-DBN model, which are suitable for periods with larger and smaller fluctuation amplitude of photovoltaic output, respectively. Wavelet decomposition can refine the data with large fluctuations on multiple scales, make the data smooth, and improve the prediction accuracy of the Autoregressive Integrated Moving Average model (ARIMA). The Deep Belief Network (DBN) can effectively process a large number of complex data and deep mining the data features. While the empirical mode decomposition (EMD) can decompose the more stable data and amplify the details in the signal as much as possible. Meanwhile, the simulated annealing algorithm (SA) can avoid the network falling into a local optimal solution and improve the prediction accuracy. This paper uses a large number of photovoltaic power station data for experimental verification. The results show that this combined model has high accuracy and generalization ability

Decision-Making of Electronic Commerce Supply Chain considering EW Service

With the rapid development of the network economy, it is a marketing strategy to provide an extended warranty (EW) service. Considering the differences in the EW service providers and dominant enterprises, this paper proposes four kinds of decision-making models and aims to study decisions of the electronic commerce supply chain, including EW price, sales price, and service level of e-platform. Through comparative analysis and numerical analysis, this research shows that, among four decision-making models, the highest system profit can be achieved when the seller provides the EW service and the e-platform dominates the system. For electronic commerce supply chain enterprises, whether to dominate the system or to provide EW service, it is conducive to the increase of profits. When the e-platform provides the EW service, the conclusion is that who dominates the system is the one who gets more profit. However, when the seller provides the EW service, the conclusion is that who dominates the system is the one who gets less profit. When the EW service is offered by the dominating enterprise, service levels of the e-platform are lower

Study on Ultra-short-time Power Forecast of Photovoltaic System based on Ground-based Cloud Image Recognition and Key Impact Factors

In recent years, under the dual pressure of resource shortage and environmental pollution, the photovoltaic (PV) power generation industry has flourished. The irradiance forecasting technology of PV power plants is of great significance for output prediction, grid dispatching and safe operation. Cloud cover is always the key factor making the irradiance fluctuate. In this article, colorful ground-based cloud images are collected by the all-sky imager every minute as the research object. Based on the traditional threshold method, a hybrid entropy threshold method is proposed to identify cloud clusters. Using the correlation analysis, among many impact factors with high correlation, five are extracted as input parameters of a BP network optimized by genetic algorithm (GA-BP). Through verification and comparison analysis, it is concluded that the recognition accuracy of the hybrid entropy threshold method is higher, and the average relative error can be controlled at about 5%. Based on this, the irradiance prediction of GA-BP also achieved better results than other models. It can meet the application requirements of PV power plants

Ultra-short-term prediction method of photovoltaic electric field power based on ground-based cloud image segmentation

As a large number of photovoltaic power stations are built and put into operation, the total amount of photovoltaic power generation accounts for an increasing proportion of the total electricity. The inability to accurately predict solar energy output has brought great uncertainty to the grid. Therefore, predicting the future power of photovoltaic fields is of great significance. According to different time scales, predictions are divided into long-term, medium-term and ultra-short-term predictions. The main difficulty of ultra-short-term forecasting lies in the power fluctuations caused by sudden and drastic changes in environmental factors. The shading of clouds is directly related to the irradiance received on the surface of the photovoltaic panel, which has become the main factor affecting the fluctuation of photovoltaic power generation. Therefore, sky images captured by conventional cameras installed near solar panels can be used to analyze cloud characteristics and improve the accuracy of ultra-short-term predictions. This paper uses historical power information of photovoltaic power plants and cloud image data, combined with machine learning methods, to provide ultra-short-term predictions of the power generation of photovoltaic power plants. First, the random forest method is used to use historical power generation data to establish a single time series prediction model to predict ultra-short-term power generation. Compared with the continuous model, the root mean square (RMSE) error of prediction is reduced by 28.38%. Secondly, the Unet network is used to segment the cloud image, and the cloud amount information is analyzed and input into the random forest prediction model to obtain the bivariate prediction model. The experimental results prove that, based on the cloud amount information contained in the cloud chart, the bivariate prediction model has an 11.56% increase in prediction accuracy compared with the single time series prediction model, and an increase of 36.66% compared with the continuous model

Ultra-short-time prediction technology of wind power station output based on variational mode decomposition and particle swarm optimization least squares vector machine

Wind power is developing rapidly in the context of sustainable development, and a series of problems such as wind curtailment and power curtailment have gradually emerged. The forecast of power generation output has become one of the hotspots of current research. This paper proposes a wind power plant output ultra-short-time prediction technology based on variational modal decomposition and particle swarm optimization least squares vector machine. Variational Modal Decomposition (VMD) method decomposes the historical output data of wind power plants at multiple levels. At the same time, it explores the impact of various decomposition methods such as EMD decomposition on the prediction accuracy, and uses the least squares support vector machine based on particle swarm optimization algorithm. Predictive summation is performed on each level of data separately to obtain a more accurate prediction effect, which has a certain improvement in prediction accuracy compared with traditional prediction algorithms

Decisions and Coordination in E-Commerce Supply Chain under Logistics Outsourcing and Altruistic Preferences

Considering the peculiarities of logistics in the electronic commerce (e-commerce) supply chain (ESC) and e-commerce platform’s altruistic preferences, a model including an e-commerce platform, third-party logistics service provider, and manufacturer is constructed. Based on this, three decision models are proposed and equilibrium solutions are obtained by the Stackelberg game. Then, an “altruistic preference joint fixed-cost” contract is proposed to maximize system efficiency. Finally, numerical analysis is used to validate the findings of the paper. The article not only analyzes and compares the optimal decisions under different ESC models, but also explores the intrinsic factors affecting the decisions. This paper finds that the conclusions of dual-channel supply chains or traditional supply chains do not necessarily apply to ESC, and that the effect of altruistic behavior under ESC is influenced by consumer preferences. Moreover, there is a multiparty win–win state for ESC, and this state can be achieved through the “altruistic preference joint fixed-cost” contract. Therefore, the findings of this paper contribute to the development of an e-commerce market and the cooperation of ESC members

Decisions and Coordination in E-Commerce Supply Chain under Logistics Outsourcing and Altruistic Preferences

Considering the peculiarities of logistics in the electronic commerce (e-commerce) supply chain (ESC) and e-commerce platform’s altruistic preferences, a model including an e-commerce platform, third-party logistics service provider, and manufacturer is constructed. Based on this, three decision models are proposed and equilibrium solutions are obtained by the Stackelberg game. Then, an “altruistic preference joint fixed-cost” contract is proposed to maximize system efficiency. Finally, numerical analysis is used to validate the findings of the paper. The article not only analyzes and compares the optimal decisions under different ESC models, but also explores the intrinsic factors affecting the decisions. This paper finds that the conclusions of dual-channel supply chains or traditional supply chains do not necessarily apply to ESC, and that the effect of altruistic behavior under ESC is influenced by consumer preferences. Moreover, there is a multiparty win–win state for ESC, and this state can be achieved through the “altruistic preference joint fixed-cost” contract. Therefore, the findings of this paper contribute to the development of an e-commerce market and the cooperation of ESC members

The Influence of Sodium Salt on Growth, Photosynthesis, Na⁺/K⁺ Homeostasis and Osmotic Adjustment of <i>Atriplex canescens</i> under Drought Stress

Atriplex canescens is widely cultivated as drought and salt-tolerant fodder in arid regions of Northwest China, which is used for photoremediation of degraded land and soil and water conservation. To explore the growth performance of A. canescens when exposed to drought and salt stress, seedlings were treated with a range of drought stress (WC1: 75 ± 3.6%, WC2: 49 ± 2.9% and WC3: 27 ± 2.5% of soil water content) and the corresponding drought stress with additional sodium salt supplementation (NaCl:Na2SO4 = 1:1 with the total concentration of Na+ set to 150 mM). The findings of this paper indicated that moderate sodium salt could stimulate the growth of A. canescens and effectively alleviate the deleterious impact of drought stress by increasing the turgor potential (ψt) and relative water content (RWC) and decreasing the leaf water osmotic potential (ψs). Furthermore, the photosynthetic capacity was improved and the negative effects of drought stress on photosystem II (PSII) were mitigated. The extra 150 mM sodium salt also markedly increased the contribution of Na+ to ψs and the contribution of betaine to ψs. In summary, these results indicate that A. canescens can adapt to drought stress by accumulating enough Na+ for osmotic adjustment (OA). Additionally, this paper is aimed to provide a fundamental basis for the utilization and cultivation of A. canescens as a favored pasture crop in the Qaidam basin, thus increasing the ecological and environmental benefits for arid regions worldwide

Impacts of Environmental Regulation on the Green Transformation and Upgrading of Manufacturing Enterprises

Since environmental problems are becoming increasingly prominent, macro policies and social development have placed higher requirements on manufacturing enterprises to promote green transformation and upgrading (GTU) in China. Considering that different manufacturing enterprises choose different green technology innovation levels for GTU under environmental regulation, a game model between manufacturing enterprises and the government is constructed. The relationship between the green technology innovation level (GTIL) and the environmental regulation intensity is analyzed. Through numerical examples, the influences of environmental regulation and consumer preference on system decisions are further examined. Moreover, an econometric model is constructed to explore the influence that the environmental regulation exerts on the GTIL using panel data from the Chinese manufacturing industry. Our results show that the increase in environmental regulation intensity contributes to improving GTIL and promoting the GTU of manufacturing enterprises. Furthermore, as the environmental regulation is enhanced, the sales price decreases, benefiting consumers. Consumers&rsquo; preference for high-GTIL products is conducive to GTU under environmental regulation. Empirical analysis shows that there is a U-shaped relationship between environmental regulation and the GTIL. Only when the intensity reaches a threshold can the environmental regulation be beneficial to improve the GTIL and promote the GTU of Chinese manufacturing enterprises