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 ...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.展开更多
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...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.展开更多
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 beh...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.展开更多
This paper discusses how ML can be leveraged to enhance supply chain forecasting through demand prediction,risk mitigation and demand-supply match optimization.Even deterministic and time-series supply chain approache...This paper discusses how ML can be leveraged to enhance supply chain forecasting through demand prediction,risk mitigation and demand-supply match optimization.Even deterministic and time-series supply chain approaches don’t have an edge over volatile and challenging data environments,making them imprecise and inflexible.Through the use of ML models,such as recurrent neural networks(RNNs),support vector machines(SVMs),and reinforcement learning(RL)agents,this study shows the accuracy in demand prediction,risk detection,and supply-demand match.The primary findings include:the RNN decreases the mean squared error by 15%over traditional approaches and the RL agent minimizes inventory turnover and lead times to enhance supply chain efficiencies.These results highlight the potential of ML to react rapidly to real-time shifts and drive better decisions.The report provides a comprehensive approach to data-driven predictive models,and useful advice for companies looking to improve supply chain resilience and profitability.展开更多
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 con...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.展开更多
基金This paper was funded by the Geological Survey Project of China Geological Survey"Comprehensive Geological Survey of Xiamen-Zhangzhou-Quanzhou City"(DD20190303).
文摘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.
文摘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.
文摘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.
文摘This paper discusses how ML can be leveraged to enhance supply chain forecasting through demand prediction,risk mitigation and demand-supply match optimization.Even deterministic and time-series supply chain approaches don’t have an edge over volatile and challenging data environments,making them imprecise and inflexible.Through the use of ML models,such as recurrent neural networks(RNNs),support vector machines(SVMs),and reinforcement learning(RL)agents,this study shows the accuracy in demand prediction,risk detection,and supply-demand match.The primary findings include:the RNN decreases the mean squared error by 15%over traditional approaches and the RL agent minimizes inventory turnover and lead times to enhance supply chain efficiencies.These results highlight the potential of ML to react rapidly to real-time shifts and drive better decisions.The report provides a comprehensive approach to data-driven predictive models,and useful advice for companies looking to improve supply chain resilience and profitability.
文摘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.
