Forecasting Study on Demand and Supply of Medical Postgraduates

This article was designed to forecast the supply and demand of medical postgraduate from 2001 to 2010 and bring forward the development strategy of medical postgraduate education in Hubei province. The line regression, the ratio of health manpower to population, grey dynamics model (1,1) was employed to forecast the supply and demand of medical postgraduate according to the corresponding data from 1991 to 2000 in Hubei province. The results showed that the number of health professionals of Hubei province in 2010 would attain 265892, the graduates' proportion of which would be about 1.8 %; and the demand and supply of medical postgraduate would be 4699 and 2264 respectively. To improve conditions actively to attract and cultivate excellent professionals, especially the senior medical scholars, are effective measures to adjust the degree structure of health manpower as soon as possible in Hubei province.

RS-SVM forecasting model and power supply-demand forecast

A support vector machine (SVM) forecasting model based on rough set (RS) data preprocess was proposed by combining the rough set attribute reduction and the support vector machine regression algorithm, because there are strong complementarities between two models. Firstly, the rough set was used to reduce the condition attributes, then to eliminate the attributes that were redundant for the forecast, Secondly, it adopted the minimum condition attributes obtained by reduction and the corresponding original data to re-form a new training sample, which only kept the important attributes affecting the forecast accuracy. Finally, it studied and trained the SVM with the training samples after reduction, inputted the test samples re-formed by the minimum condition attributes and the corresponding original data, and then got the mapping relationship model between condition attributes and forecast variables after testing it. This model was used to forecast the power supply and demand. The results show that the average absolute error rate of power consumption of the whole society and yearly maximum load are 14.21% and 13.23%, respectively, which indicates that the RS-SVM forecast model has a higher degree of accuracy.

Synthetic Reconstruction of Water Demand Time Series for Real Time Demand Forecasting

The forecasting of the demand applied to water supply systems has been an important tool to realize time control. The use of the time series to do the forecasting of the demand is the main way that has been used by researchers. By this way, the need of a complete time demand series increases. This work presents two ways to reconstruct the water demand time series synthetically, using the Average Reconstruction Method and Fourier Method. Both the methods were considered interesting to do the synthetic reconstruction and able to complete the time series, but the Fourier Method showed better results and a better fitness to approximation of the water consumption pattern.

RESEARCH ON MUNICIPAL WATER DEMANDS FORECAST

Based on the changing law of municipal water demand,a trigonometric function model for short-term water demand forecast is established using the time-series analysis approach.The method for forecasting water demand during holidays and under unexpected events is also presented.Meanwhile,a computer software is developed.Through actual application,this method performs well and has high accuracy,so it can be applied to the daily operation of a water distribution system and lay a foundation for on-line optimal operation.

Supply Chain Demand Forecast Based on SSA-XGBoost Model

Supply chain management usually faces problems such as high empty rate of transportation, unreasonable inventory management, and large material consumption caused by inaccurate market demand forecasts. To solve these problems, using artificial intelligence and big data technology to achieve market demand forecasting and intelligent decision-making is becoming a strategic technology trend of supply chain management in the future. Firstly, this paper makes a visual analysis of the historical data of the Stock Keeping Unit (SKU);Then, the characteristic factors affecting the future demand are constructed from the storage level, product level, historical usage of SKU, etc;Finally, a supply chain demand forecasting algorithm based on SSA-XGBoost model has proposed around three aspects of feature engineering, parameter optimization and model integration, and is compared with other machine learning models. The experiment shows that the forecasting result of SSA-XGBoost forecasting model is highly consistent with the actual value, so it is of practical significance to adopt this forecasting model to solve the supply chain demand forecasting problem.

A Study on an Extensive Hierarchical Model for Demand Forecasting of Automobile Components

Demand forecasting and big data analytics in supply chain management are gaining interest.This is attributed to the wide range of big data analytics in supply chain management,in addition to demand forecasting,and behavioral analysis.In this article,we studied the application of big data analytics forecasting in supply chain demand forecasting in the automotive parts industry to propose classifications of these applications,identify gaps,and provide ideas for future research.Algorithms will then be classified and then applied in supply chain management such as neural networks,k-nearest neighbors,time series forecasting,clustering,regression analysis,support vector regression and support vector machines.An extensive hierarchical model for short-term auto parts demand assess-ment was employed to avoid the shortcomings of the earlier models and to close the gap that regarded mainly a single time series.The concept of extensive relevance assessment was proposed,and subsequently methods to reflect the relevance of automotive demand factors were discussed.Using a wide range of skills,the factors and co-factors are expressed in the form of a correlation characteristic matrix to ensure the degree of influence of each factor on the demand for automotive components.Then,it is compared with the existing data and predicted the short-term historical data.The result proved the predictive error is less than 6%,which supports the validity of the prediction method.This research offers the basis for the macroeconomic regulation of the government and the production of auto parts manufacturers.

Analysis on the situation and countermeasures of water resources supply and demand in the cities of small and medium-sized river basins along southeast coast of China-taking Xiamen City as an example

The small and medium-sized river basins along southeast coast of China hold comparatively abundant water resources.However,the rapid resources urbanization in recent years has produced a series of water problems such as deterioration of river water quality,water shortage and exacerbated floods,which have constrained urban economic development.By applying the principle of triple supply-demand equilibrium,this paper focuses on the estimation of levels of water supply and demand in 2030 at different guarantee probabilities,with a case study of Xiamen city.The results show that water shortage and inefficient utilization are main problems in the city,as the future water supply looks daunting,and a water shortage may hit nearly 2×10^(8)m^(3)in an extraordinarily dry year.Based on current water supply-demand gap and its trend,this paper proposes countermeasures and suggestions for developing and utilizing groundwater resources and improving the utilization rate of water resources,which can supply as a reference for other southeast middle-to-small-sized basin cities in terms of sustainable water resources and water environment protection.

Power Market Analyses on 2001 and Demand Forecast for 2002

Based on the analysis on economic situation in China in 2001, the paperdiscusses power supply and demand features nationwide and by regions andprovinces, present estimation of power supply and demand in 2002. In conclusion,the paper presents suggestions to overcome difficulties on capital funds andtechniques.[

CHINA'S GRAIN SUPPLY AND DEMAND IN THE 21ST CENTURY

This paper makes predictions for China’s grain production and its associated supply and demand situation up to 2030 by using input-occupancy-output techniques and systems science methodology. It argues that, taking into account its basic situation and world grain resources, China has no other choice but to count on self-sufficiency in terms of grain supply.

RESEARCH ON VALUE OF INFORMATION SHARING IN ONE SUPPLIER-MULTIPLE RETAILER SUPPLY CHAIN

Prior research has generally focused on models involving a single or multiple retailers with independent demands. The value of demand information sharing is analyzed in a two-level supply chain involving one supplier-multiple retailer model in which retailer demands may be correlated. Each member in the supply chain forecasts its demand using an AR (1) demand process. Two conditions of the information sharing are considered (1) Without the information sharing, the retailers only communicate their orders to the supplier. (2) With the information sharing, retailers communicate their orders and forecasting models to the supplier. Analyses showthat the reductions of supplier's average inventory and average costs are substantial with the information sharing. However, the retailers donot get direct benefit from the information sharing. The retailers may ask the supplier to reduce the replenishment leadtime, so that the retailers will obtain substantial cost savings and inventory reduction. Both partners may obtain benefits when information sharing and leadtime reduction are implemented together.

Characterization of Peaks and Valleys of Electricity Demand. Application to the Spanish Mainland System in the Period 2000-2020

Energy planning must anticipate the development and strengthening of power grids, power plants construction times, and the provision of energy resources with the aim of increasing security of supply and its quality. This work presents a methodology for predicting power peaks in mainland Spain’s system in the decade 2011-2020. Forecasts of total electricity demand of Spanish energy authorities set the boundary conditions. The accuracy of the results has successfully been compared with records of demand (2000-2010) and with various predictions published. Three patterns have been observed: 1) efficiency in the winter peak;2) increasing trend in the summer peak;3) increasing trend in the annual valley of demand. By 2020, 58.1 GW and 53.0 GW are expected, respectively, as winter and summer peaks in a business-as-usual scenario. If the observed tendencies continue, former values can go down to 55.5 GW in winter and go up to 54.7 GW in summer. The annual minimum valley of demand will raise 5.5 GW, up to 23.4 GW. These detailed predictions can be very useful to identify the types of power plants needed to have an optimum structure in the electricity industry.

A combined forecasting method for intermittent demand using the automotive aftermarket data

Intermittent demand forecasting is an important challenge in the process of smart supply chain transformation,and accurate demand forecasting can reduce costs and increase efficiency for enterprises.This study proposes an intermittent demand combination forecasting method based on internal and external data,builds intermittent demand feature engineering from the perspective of machine learning,predicts the occurrence of demand by classification model,and predicts non-zero demand quantity by regression model.Based on the strategy selection on the inventory side and the stocking needs on the replenishment side,this study focuses on the optimization of the classification problem,incorporates the internal and external data of the enterprise,and proposes two combination forecasting optimization methods on the basis of the best classification threshold searching and transfer learning,respectively.Based on the real data of auto after-sales business,these methods are evaluated and validated in multiple dimensions.Compared with other intermittent forecasting methods,the models proposed in this study have been improved significantly in terms of classification accuracy and forecasting precision,which validates the potential of combined forecasting framework for intermittent demand and provides an empirical study of the framework in industry practice.The results show that this research can further provide accurate upstream inputs for smart inventory and guarantee intelligent supply chain decision-making in terms of accuracy and efficiency.

Plant-Wide Supply-Demand Forecast and Optimization of Byproduct Gas System in Steel Plant

Considerable energy is consumed during steel manufacturing process. Byproduct gas emerges as secondary energy in the process; however, it is also an atmospheric pollution source if it is released into the air. Therefore, the optimal utilization of byproduct gas not only saves energy but also protects environment. To solve this issue, a fore- cast model of gas supply, gas demand and surplus gas in a steel plant was proposed. With the progress of energy conservation, the amount of surplus gas was very large. In a steel plant, the surplus gas was usually sent to boilers to generate steam. However, each boiler had an individual efficiency. So the optimization of the utilization of surplus gas in boilers was a key topic. A dynamic programming method was used to develop an optimal utilization strategy for surplus gas. Finally, a case study providing a sound confirmation was given.

Status and Trends in International Trade of Major Wood Products in China

Based on the current conditions, a forecast of trends in imports and exports of wood products and their demand and supply is presented in this paper for the years of 2005 and 2015. It is expected that imports will continue to exceed exports but that the trade deficit in wood products will decline. The form of trade will be changed from a condition of unilateral imports to one of exerting mutual advantage through imports and exports. The structure of trade in forest products will alter with changes in the forest resource base and with new developments in the forest industry.

Supply and Demand Levels for Livestock and Poultry Products in the Chinese Mainland and the Potential Demand for Feed Grains

The widening gap between the supply and demand levels for livestock and poultry products in the Chinese mainland poses a significant challenge to the secure supply of feed grains. Therefore, the accurate prediction of the demand potential for feed grains represents a key scientific issue for ensuring food security in the Chinese mainland. This study is based on an analysis of several factors, such as the Chinese mainland’s output, trade volume, apparent consumption of livestock and poultry products, and two different scenarios for predicting the future demand for feed grains are assessed. The results indicate that output and consumption of livestock and poultry products, as well as the country’s trade deficit and the pressure of the supply and demand balance with respect to these products, have been increasing in recent years. The analysis predicts that the demand for feed grains in the Chinese mainland will reach 425.5 or 389.6 million tons in 2030 based on the two scenarios. This finding indicates that with the increasing demand for livestock and poultry products in the Chinese mainland, the demand for feed grains will continue to increase, and the shortfall in feed grains and raw materials will expand further, especially dependence on external sources of protein-rich feed grains will remain high.

Vendor managed inventory and bullwhip effect

This paper studies that the bullwhip effect of order releases and the amplifications of safety stock arise within the supply chain even when the demand model is ARIMA(0, 1, 1) and the forecast method used is a simple exponentially weighted moving average. It also examines a vendor managed inventory (VMI) program to determine how it can help alleviate such negative effects, and gives the theoretical proofs and numerical illustrations. The results show that the effects with VMI are better than the effect without VMI in demand forecasting and safety stock levels, etc.

Some Views about Recent Electric Power Supply Shortage in Shenzhen

Since the beginning of the year 2000, the power demands in Guangdong, Zhejiang provinces and Beijing Tianjin-Tangshan district have been increasing dramatically, power supply shortages have appeared again. This paper analyzes the reasons for the current power supply shortages in Shenzhen district and the problems existing presently in Shenzhen power system. It indicates that, to strengthen power demand forecast, to speed up power construction steps and with ’to develop power ahead of the rest’ as a fundamental target, are the precondition to the long term, steady development of power industry.

新时期种植业保障我国食物安全战略研究

种植业产品是保障我国食物安全的关键,种植业的发展支撑了我国由“吃得饱”到“吃得好”的历史性转变,促进了居民膳食质量的逐步提升;新时期我国种植业发展面临资源环境压力、极端气候冲击以及国际地缘政治格局不稳等内外部风险因素的挑战,亟需探讨可行性发展战略以确保食物安全。通过对2035年、2050年种植业食物供需形势预测后得出,未来我国粮食净进口主要集中在大豆和玉米,油菜籽和糖的自给水平将继续下降,花生和水果的自给水平将先降后升,蔬菜始终保持自给有余的态势。基于此,本文梳理了新时期保障我国食物安全面临的挑战,提出了新时期种植业保障我国食物安全的战略构想,主要包括采取种植业产品生产能力提升和结构优化、资源高效利用与低碳生产推进、消费结构引导与健康观念培育、农业科技创新与装备支撑、新型经营主体创新等战略举措,实施种植业科技创新、种植业产品质量提升、种植业生态保护、蛋白替代等重大工程。研究建议,坚持“口粮绝对安全、谷物基本自给”总体战略,分区域制定产业发展优先序,加快补齐农业基础设施与科技短板,健全重大危机应对战略体系,以此增强种植业发展水平,切实保障我国食物安全。

中国成品油市场2023年回顾与2024年供需分析预测

回顾2023年中国成品油市场发展,结合国内经济发展各种因素,对2024年成品油市场进行预测。分析认为:2023年中国成品油需求持续复苏,消费属性突出的汽油和航空煤油(航煤)消费大幅增长,经济增速加快拉动柴油消费温和增长,中国成品油消费量预估为3.66×10^(8)t,同比增长12.2%,为2011年以来再次出现两位数增长;成品油消费税扩围征收提高了调合油的原料成本,减少了低价资源数量;出口配额大幅增加则缓解了国内资源过剩压力。2024年,消费对经济增长的贡献将继续加大,航煤消费将持续快速增长,汽车电动化将导致汽油消费增速大幅放缓,能耗双控逐步转向碳排放双控,柴油消费量将重回下降通道。2024年中国成品油市场仍将呈现供需双增的良好态势,消费量预计为3.69×10^(8)t,较2023年小幅增长0.8%;地方炼厂成品油资源持续增加,国内成品油过剩规模将超过5000×10^(4)t,国内市场竞争形势依然严峻。

重大传染病疫情下应急医疗物资需求预测和配置研究

为了科学合理地进行应急医疗物资配置,提高重大传染病疫情防控效率,根据疫情演化不同阶段的特点开展应急医疗物资需求预测和配置研究。首先,根据疫情数据特征,提出传染病模型SEIR(Susceptible Exposed Infectious Recovered)和长短期记忆(Long Short-Term Memory,LSTM)网络相结合的模型(SEIR-LSTM)预测各需求点的应急医疗物资需求量,该方法利用LSTM对时间序列数据良好的学习能力预测感染率,输入SEIR模型提高预测准确率。然后,根据传染病疫情演化关键阶段的特点,考虑物资配送成本、需求紧迫度和分配公平性等因素构建分阶段多目标物资配置模型。最后,以上海新冠肺炎疫情进行实例分析,结果表明,基于SEIR-LSTM的应急物资需求量预测方法准确率较高,根据分阶段配置模型求出的方案能够满足各个阶段物资分配的要求,验证了提出的模型和算法的有效性。