Spare parts are very common in industry and military fields, and the investigations of spare parts demand forecasting methods have draws much attention in recent years. However,to the best of our knowledge,only few pa...Spare parts are very common in industry and military fields, and the investigations of spare parts demand forecasting methods have draws much attention in recent years. However,to the best of our knowledge,only few papers reviewed the forecasting papers systematically. This paper is an attempt to provide a novel and comprehensive view to summarize these methods. A new framework was proposed to classify the demand forecasting methods into four categories,including empirical methods,methods based on historical data,analytical methods and simulation methods. Some typical literatures related to each category were reviewed.Moreover, a general spare parts forecasting procedure was summarized and some evaluation criteria were presented. Finally,characteristics of different forecasting methods and some avenues for further research were illustrated. This work provides the managers with a systematical idea about the spare parts demand forecasting and it can be used in practical applications.展开更多
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 re...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.展开更多
Maintenance material reserves must keep an appropriate scale,in order to meet the possible demand of support objectives.According to the sequence of maintenance material consumption,this paper establishes a Gray-Marko...Maintenance material reserves must keep an appropriate scale,in order to meet the possible demand of support objectives.According to the sequence of maintenance material consumption,this paper establishes a Gray-Markov forecasting model by combining Gray system theory and Markov model. Few data are needed in the proposed Gray-Markov forecasting model which has high prediction precision by involving small parameters. The performance of Gray-Markov forecasting model was demonstrated using practical application and the model was proved to be a valid and accurate forecasting method. This Gray-Markov forecasting model can provide reference for making material demand plan and determining maintenance material reserves.展开更多
Regional logistics demand forecast is the basis for government departments to make logistics planning and logistics related policies.It has the characteristics of a small amount of data and being nonlinear,so the trad...Regional logistics demand forecast is the basis for government departments to make logistics planning and logistics related policies.It has the characteristics of a small amount of data and being nonlinear,so the traditional prediction method can not guarantee the accuracy of prediction.Taking Xiamen City as an example,this paper selects the primary industry,the secondary industry,the tertiary industry,the total amount of investment in fixed assets,total import and export volume,per capita consumption expenditure,and the total retail sales of social consumer goods as the influencing factors,and uses a combining model least square and radial basis function(LS-RBF)neural network to analyze the related data from years 2000 to 2019,so as to predict the logistics demand from years 2020 to 2024.The model can well fit the training data,and the experimental results obtained from the comparison between the predicted value and the actual value in 2019 show that the error rate is very small.Therefore,the prediction results are reasonable and reliable.This method has high prediction accuracy,and it is suitable for irregular regional logistics demand forecast.展开更多
This article predicts Southeast Asia’s logistics needs from a Southeast Asian logistics development perspective. This is not only an important prerequisite for supporting Southeast Asia’s trade policy, but also prom...This article predicts Southeast Asia’s logistics needs from a Southeast Asian logistics development perspective. This is not only an important prerequisite for supporting Southeast Asia’s trade policy, but also promoting the development of Southeast Asia’s logistics industry, building logistics infrastructure and improving the level of logistics services. Due to differences in economic development levels, trade structures, infrastructure construction and logistics development levels of Southeast Asian countries. Therefore, considering the actual situation of Southeast Asian countries, this article selected 21 cities in Southeast Asia as the research object. Use L-OD logistics demand forecasting method to forecast logistics demand in Southeast Asia. Obtain the amount of logistics occurrence and attraction in 21 cities in Southeast Asia in the future. And construct a double constrained gravity model to predict logistics distribution in Southeast Asia. The forecast results provide scientific data support for future logistics development planning in Southeast Asia.展开更多
China’s demand for automobiles fallsinto three types:trucks,buses andcars.According to statistics fromdepartment concerned,China’s demand andquantity in the next 15 years is as follows: 1. The demand for trucks will...China’s demand for automobiles fallsinto three types:trucks,buses andcars.According to statistics fromdepartment concerned,China’s demand andquantity in the next 15 years is as follows: 1. The demand for trucks will growsteadily,in line with the growth of the nationaleconomy.The development of展开更多
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 ...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.展开更多
This paper discusses the development and implementation of an evacuation demand forecasting module that was incorporated into a comprehensive decision support system for the planning and management of contraflow opera...This paper discusses the development and implementation of an evacuation demand forecasting module that was incorporated into a comprehensive decision support system for the planning and management of contraflow operations in the Gulf of Mexico. Contraflow implies the reversing of one direction of a highway in order to permit a substantially increased travel demand exiting away from an area impacted by a natural disaster or any other type of catastrophic event. Correctly estimating the evacuation demand originated from such a catastrophic event is critical to a successful contraflow implementation. One problem faced by transportation officials is the arranging of the different stages of this complex traffic procedure. Both the prompt deployment of resources and personnel as well as the duration of the actual contraflow affect the overall effectiveness, safety and cost of the evacuation event. During this project, researchers from the University of Alabama developed an integral decision support system for contraflow evacuation planning to assist the Alabama Department of Transportation Maintenance Bureau in the evaluation and planning of contraflow operations oriented to mitigate the evacuation burdens of a hurricane event. This paper focuses on the design of the demand forecasting module of such a decision support system.展开更多
Fresh products have the characteristics of perishable, small batch and high frequency. Therefore, for fresh food e-commerce enterprises, market demand forecasting is particularly important. This paper takes the sales ...Fresh products have the characteristics of perishable, small batch and high frequency. Therefore, for fresh food e-commerce enterprises, market demand forecasting is particularly important. This paper takes the sales data of a fresh food e-commerce enterprise as the logistics demand, analyzes the influence of time and meteorological factors on the demand, extracts the characteristic factors with greater influence, and proposes a logistics demand forecast scheme of fresh food e-commerce based on the Bi-LSTM model. The scheme is compared with other schemes based on the BP neural network and LSTM neural network models. The experimental results show that the Bi-LSTM model has good prediction performance on the problem of logistics demand prediction. This facilitates further research on some supply chain issues, such as business decision-making, inventory control, and logistics capacity planning.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
Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supp...Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.展开更多
Emergency ambulance services in the UK are tasked with providing pre-hospital patient care and clinical services with a target response time between call connect to on-scene attendance.In 2017,NHS England introduced f...Emergency ambulance services in the UK are tasked with providing pre-hospital patient care and clinical services with a target response time between call connect to on-scene attendance.In 2017,NHS England introduced four new response time categories based on patient needs.The most challenging is to be on-scene for a life-threatening situation within seven minutes of the call being connected when such calls are random in terms of time and place throughout a large territory.Recent evidence indicates emergency ambulance services regularly fall short of achieving the target ambulance response times set by the National Health Service(NHS).To achieve these targets,they need to undertake transformational change and apply statistical,operations research and artificial intelligence techniques in the form of five separate modules covering demand forecasting,plus locate,allocate,dispatch,monitoring and re-deployment of resources.These modules should be linked in real-time employing a data warehouse to minimise computational data and generate accurate,meaningful and timely decisions ensuring patients receive an appropriate and timely response.A simulation covering a limited geographical area,time and operational data concluded that this form of integration of the five modules provides accurate and timely data upon which to make decisions that effectively improve ambulance response times.展开更多
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
Short-term water demand forecasting provides guidance on real-time water allocation in the water supply network, which help water utilities reduce energy cost and avoid potential accidents. Although a variety of metho...Short-term water demand forecasting provides guidance on real-time water allocation in the water supply network, which help water utilities reduce energy cost and avoid potential accidents. Although a variety of methods have been proposed to improve forecast accuracy, it is still difficult for statistical models to learn the periodic patterns due to the chaotic nature of the water demand data with high temporal resolution. To overcome this issue from the perspective of improving data predictability, we proposed a hybrid Wavelet-CNN-LSTM model, that combines time-frequency decomposition characteristics of Wavelet Multi-Resolution Analysis (MRA) and implement it into an advanced deep learning model, CNN-LSTM. Four models - ANN, Conv1D, LSTM, GRUN - are used to compare with Wavelet-CNN-LSTM, and the results show that Wavelet-CNN-LSTM outperforms the other models both in single-step and multi-steps prediction. Besides, further mechanistic analysis revealed that MRA produce significant effect on improving model accuracy.展开更多
The reformation of the economy system has led the f un ctional department and status of the enterprises into a variable state. Under th e condition of the market economy, the kernel of the enterprises’ functional dep...The reformation of the economy system has led the f un ctional department and status of the enterprises into a variable state. Under th e condition of the market economy, the kernel of the enterprises’ functional dep artment has diverted to that of marketing decision-making, which face to market and meet with the need of consumption. Assuredly, the kernel of marketing decis ion-making is to prognosticate the future market demand of the production of en terprises accurately, so that it can ensure and realize the maximum of the enter prises’ profit increase. Using empirical research and the multi-regression technique, this paper ana lyzes the enterprises’ production demand forecast of the GMC (Global Management Challenge, held every year globally) and changes most of uncontrollable factors of demand forecast to the controllable ones of the enterprises. The method we us ed to forecast demand by using the multi-regression technique is as follows: 1. Look for the main factors which influence the demand of productions; 2. Establish the regression model; 3. Using the historical data, find the resolution of the correlative index an d do the prominent test; 4. Analyze and compare, regression, adjust parameter and optimize the regress ion model. Our method will make the forecast data closer to the actual prices of the future market requirement quantity in the production marketing decision-making of the enterprises and realize the optimizing combination and the working object w ith the minimum of the cost and the maximum of the profit. And it can ensure the realization of the equity maximum of the enterprises and increase the lifecycle of the production.展开更多
This research study explores the use of an innovative freight tour-based approach to model truck trips as an alternative to the conventional trip-based approach. The tour-based approach is more realistic as it capture...This research study explores the use of an innovative freight tour-based approach to model truck trips as an alternative to the conventional trip-based approach. The tour-based approach is more realistic as it captures the intermediate stops of each truck and reflects the implications of those stops on vehicle miles traveled (VMT). The paper describes the truck tour-based model concept, and presents the framework of a truck tour-based travel demand forecasting approach. As a case study, Global Positioning System (GPS) truck data are used to determine origin, destination, and truck stops for trucks moving within the Birmingham, Alabama region. Such information is then utilized to model truck movements within the study region as individual truck tours. The tour-based model is ran, and the resulting performance measures are contrasted to those obtained from the conventional trip-based planning model used by the Regional Planning Commission of Greater Birmingham (RPCGB). This case study demonstrates the feasibility of using a tour-based freight demand forecasting model as an alternative to the conventional 4-step process currently used to estimate truck trips in the Birmingham region. The results and lessons learned from the Birmingham case study are expected to improve truck movement modeling practices in the region and advance the accuracy of truck travel demand forecasting models at other locations in the future.展开更多
Our dependency on energy is so vital that it makes it difficult to imagine how humans can live on our planet earth without it.The demand for electricity,for example,is directly related to the growth of the population ...Our dependency on energy is so vital that it makes it difficult to imagine how humans can live on our planet earth without it.The demand for electricity,for example,is directly related to the growth of the population worldwide,and presently,to meet this demand,we need both renewable and nonrenewable energy.While nonrenewable energy has its shortcomings(negative impact on climate change,for example),renewable energy is not enough to address the ever-changing demand for energy.One way to address this need is to become more innovative,use technology more effectively,and be aware of the costs associated with different sources of renewable energy.In the case of nuclear power plants,new innovative centered around small modular reactors(SMRs)of generation 4th of these plants make them safer and less costly to own them as well as to protect them via means of cyber-security against any attack by smart malware.Of course,understanding the risks and how to address them is an integral part of the study.Natural sources of energy,such as wind and solar,are suggesting other innovating technical approaches.In this article,we are studying these factors holistically,and details have been laid out in a book by the authors’second volume of series title as Knowledge Is Power in Four Dimensions under Energy subtitle.展开更多
Energy transition towards clean,efficient energy supply has been a common sense of the government and public in China.However,lacking reasonable planning will lead to undisciplined development,resource waste,and exces...Energy transition towards clean,efficient energy supply has been a common sense of the government and public in China.However,lacking reasonable planning will lead to undisciplined development,resource waste,and excessive investment.In this context,this paper investigates potential pathways of Beijing energy transition towards a high-level low-carbon,clean and efficient energy system in 2035 with an extended energysocpe model.Firstly,based on available data,future energy demands are predicted by a newly proposed hybrid forecasting method,which combines the traditional regression model,grey model,and support vector machine model with an entropy-based weighted factor.Secondly,the superstructure-based optimization model is employed to investigate the system configuration and operation strategy of the future Beijing energy system.Finally,the uncertainty impact of electricity price,natural gas price,hydrogen price,and the capital expenditures of electrolyzer and steam methane reforming for hydrogen applications are studied.The forecasting results show that all walks of life will witness a continuously increasing energy demand in multiple sectors of Beijing towards 2035.The planning results suggest that the imported electricity and natural gas will dominate the energy supply of Beijing in 2035 with a contribution of 86%of the energy resources consumption of 384 TWh.Moreover,the energy system presents a high end-use electrification level of 65%and high penetration of efficient technologies,which supply 119 TWh via combined heat and power,26 TWh via heat pump and 95 TWh via district heating network.The energy use of various sectors of energy resources,technologies and end-use are closely related.Hydrogen will have an increased penetration in the private mobility sector,but the locally generated hydrogen is mainly from steam methane reforming technology.展开更多
文摘Spare parts are very common in industry and military fields, and the investigations of spare parts demand forecasting methods have draws much attention in recent years. However,to the best of our knowledge,only few papers reviewed the forecasting papers systematically. This paper is an attempt to provide a novel and comprehensive view to summarize these methods. A new framework was proposed to classify the demand forecasting methods into four categories,including empirical methods,methods based on historical data,analytical methods and simulation methods. Some typical literatures related to each category were reviewed.Moreover, a general spare parts forecasting procedure was summarized and some evaluation criteria were presented. Finally,characteristics of different forecasting methods and some avenues for further research were illustrated. This work provides the managers with a systematical idea about the spare parts demand forecasting and it can be used in practical applications.
文摘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.
基金Chemical Defense Equipment Maintenance Material Support Methods,Universal Equipment Support Department[2012]No.80,China
文摘Maintenance material reserves must keep an appropriate scale,in order to meet the possible demand of support objectives.According to the sequence of maintenance material consumption,this paper establishes a Gray-Markov forecasting model by combining Gray system theory and Markov model. Few data are needed in the proposed Gray-Markov forecasting model which has high prediction precision by involving small parameters. The performance of Gray-Markov forecasting model was demonstrated using practical application and the model was proved to be a valid and accurate forecasting method. This Gray-Markov forecasting model can provide reference for making material demand plan and determining maintenance material reserves.
基金Social Science Research Project of Education Department of Fujian Province,China(No.JAS160571)Key Project of Education and Teaching Reform of Undergraduate Universities in Fujian Province,China(No.FBJG20190130)Educational Research Project of Social Science for Young and Middle Aged Teachers in Fujian Province,China(No.JAS19371)。
文摘Regional logistics demand forecast is the basis for government departments to make logistics planning and logistics related policies.It has the characteristics of a small amount of data and being nonlinear,so the traditional prediction method can not guarantee the accuracy of prediction.Taking Xiamen City as an example,this paper selects the primary industry,the secondary industry,the tertiary industry,the total amount of investment in fixed assets,total import and export volume,per capita consumption expenditure,and the total retail sales of social consumer goods as the influencing factors,and uses a combining model least square and radial basis function(LS-RBF)neural network to analyze the related data from years 2000 to 2019,so as to predict the logistics demand from years 2020 to 2024.The model can well fit the training data,and the experimental results obtained from the comparison between the predicted value and the actual value in 2019 show that the error rate is very small.Therefore,the prediction results are reasonable and reliable.This method has high prediction accuracy,and it is suitable for irregular regional logistics demand forecast.
文摘This article predicts Southeast Asia’s logistics needs from a Southeast Asian logistics development perspective. This is not only an important prerequisite for supporting Southeast Asia’s trade policy, but also promoting the development of Southeast Asia’s logistics industry, building logistics infrastructure and improving the level of logistics services. Due to differences in economic development levels, trade structures, infrastructure construction and logistics development levels of Southeast Asian countries. Therefore, considering the actual situation of Southeast Asian countries, this article selected 21 cities in Southeast Asia as the research object. Use L-OD logistics demand forecasting method to forecast logistics demand in Southeast Asia. Obtain the amount of logistics occurrence and attraction in 21 cities in Southeast Asia in the future. And construct a double constrained gravity model to predict logistics distribution in Southeast Asia. The forecast results provide scientific data support for future logistics development planning in Southeast Asia.
文摘China’s demand for automobiles fallsinto three types:trucks,buses andcars.According to statistics fromdepartment concerned,China’s demand andquantity in the next 15 years is as follows: 1. The demand for trucks will growsteadily,in line with the growth of the nationaleconomy.The development of
文摘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.
文摘This paper discusses the development and implementation of an evacuation demand forecasting module that was incorporated into a comprehensive decision support system for the planning and management of contraflow operations in the Gulf of Mexico. Contraflow implies the reversing of one direction of a highway in order to permit a substantially increased travel demand exiting away from an area impacted by a natural disaster or any other type of catastrophic event. Correctly estimating the evacuation demand originated from such a catastrophic event is critical to a successful contraflow implementation. One problem faced by transportation officials is the arranging of the different stages of this complex traffic procedure. Both the prompt deployment of resources and personnel as well as the duration of the actual contraflow affect the overall effectiveness, safety and cost of the evacuation event. During this project, researchers from the University of Alabama developed an integral decision support system for contraflow evacuation planning to assist the Alabama Department of Transportation Maintenance Bureau in the evaluation and planning of contraflow operations oriented to mitigate the evacuation burdens of a hurricane event. This paper focuses on the design of the demand forecasting module of such a decision support system.
文摘Fresh products have the characteristics of perishable, small batch and high frequency. Therefore, for fresh food e-commerce enterprises, market demand forecasting is particularly important. This paper takes the sales data of a fresh food e-commerce enterprise as the logistics demand, analyzes the influence of time and meteorological factors on the demand, extracts the characteristic factors with greater influence, and proposes a logistics demand forecast scheme of fresh food e-commerce based on the Bi-LSTM model. The scheme is compared with other schemes based on the BP neural network and LSTM neural network models. The experimental results show that the Bi-LSTM model has good prediction performance on the problem of logistics demand prediction. This facilitates further research on some supply chain issues, such as business decision-making, inventory control, and logistics capacity planning.
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Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.
文摘Emergency ambulance services in the UK are tasked with providing pre-hospital patient care and clinical services with a target response time between call connect to on-scene attendance.In 2017,NHS England introduced four new response time categories based on patient needs.The most challenging is to be on-scene for a life-threatening situation within seven minutes of the call being connected when such calls are random in terms of time and place throughout a large territory.Recent evidence indicates emergency ambulance services regularly fall short of achieving the target ambulance response times set by the National Health Service(NHS).To achieve these targets,they need to undertake transformational change and apply statistical,operations research and artificial intelligence techniques in the form of five separate modules covering demand forecasting,plus locate,allocate,dispatch,monitoring and re-deployment of resources.These modules should be linked in real-time employing a data warehouse to minimise computational data and generate accurate,meaningful and timely decisions ensuring patients receive an appropriate and timely response.A simulation covering a limited geographical area,time and operational data concluded that this form of integration of the five modules provides accurate and timely data upon which to make decisions that effectively improve ambulance response times.
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.
基金financially supported by the National Natural Science Foundation of China(No.51978494)the Science and Technology Innovation Program Project of Shanghai City Investment Co.,Ltd.(No.CTKY-ZDXM-2020-012).
文摘Short-term water demand forecasting provides guidance on real-time water allocation in the water supply network, which help water utilities reduce energy cost and avoid potential accidents. Although a variety of methods have been proposed to improve forecast accuracy, it is still difficult for statistical models to learn the periodic patterns due to the chaotic nature of the water demand data with high temporal resolution. To overcome this issue from the perspective of improving data predictability, we proposed a hybrid Wavelet-CNN-LSTM model, that combines time-frequency decomposition characteristics of Wavelet Multi-Resolution Analysis (MRA) and implement it into an advanced deep learning model, CNN-LSTM. Four models - ANN, Conv1D, LSTM, GRUN - are used to compare with Wavelet-CNN-LSTM, and the results show that Wavelet-CNN-LSTM outperforms the other models both in single-step and multi-steps prediction. Besides, further mechanistic analysis revealed that MRA produce significant effect on improving model accuracy.
文摘The reformation of the economy system has led the f un ctional department and status of the enterprises into a variable state. Under th e condition of the market economy, the kernel of the enterprises’ functional dep artment has diverted to that of marketing decision-making, which face to market and meet with the need of consumption. Assuredly, the kernel of marketing decis ion-making is to prognosticate the future market demand of the production of en terprises accurately, so that it can ensure and realize the maximum of the enter prises’ profit increase. Using empirical research and the multi-regression technique, this paper ana lyzes the enterprises’ production demand forecast of the GMC (Global Management Challenge, held every year globally) and changes most of uncontrollable factors of demand forecast to the controllable ones of the enterprises. The method we us ed to forecast demand by using the multi-regression technique is as follows: 1. Look for the main factors which influence the demand of productions; 2. Establish the regression model; 3. Using the historical data, find the resolution of the correlative index an d do the prominent test; 4. Analyze and compare, regression, adjust parameter and optimize the regress ion model. Our method will make the forecast data closer to the actual prices of the future market requirement quantity in the production marketing decision-making of the enterprises and realize the optimizing combination and the working object w ith the minimum of the cost and the maximum of the profit. And it can ensure the realization of the equity maximum of the enterprises and increase the lifecycle of the production.
文摘This research study explores the use of an innovative freight tour-based approach to model truck trips as an alternative to the conventional trip-based approach. The tour-based approach is more realistic as it captures the intermediate stops of each truck and reflects the implications of those stops on vehicle miles traveled (VMT). The paper describes the truck tour-based model concept, and presents the framework of a truck tour-based travel demand forecasting approach. As a case study, Global Positioning System (GPS) truck data are used to determine origin, destination, and truck stops for trucks moving within the Birmingham, Alabama region. Such information is then utilized to model truck movements within the study region as individual truck tours. The tour-based model is ran, and the resulting performance measures are contrasted to those obtained from the conventional trip-based planning model used by the Regional Planning Commission of Greater Birmingham (RPCGB). This case study demonstrates the feasibility of using a tour-based freight demand forecasting model as an alternative to the conventional 4-step process currently used to estimate truck trips in the Birmingham region. The results and lessons learned from the Birmingham case study are expected to improve truck movement modeling practices in the region and advance the accuracy of truck travel demand forecasting models at other locations in the future.
文摘Our dependency on energy is so vital that it makes it difficult to imagine how humans can live on our planet earth without it.The demand for electricity,for example,is directly related to the growth of the population worldwide,and presently,to meet this demand,we need both renewable and nonrenewable energy.While nonrenewable energy has its shortcomings(negative impact on climate change,for example),renewable energy is not enough to address the ever-changing demand for energy.One way to address this need is to become more innovative,use technology more effectively,and be aware of the costs associated with different sources of renewable energy.In the case of nuclear power plants,new innovative centered around small modular reactors(SMRs)of generation 4th of these plants make them safer and less costly to own them as well as to protect them via means of cyber-security against any attack by smart malware.Of course,understanding the risks and how to address them is an integral part of the study.Natural sources of energy,such as wind and solar,are suggesting other innovating technical approaches.In this article,we are studying these factors holistically,and details have been laid out in a book by the authors’second volume of series title as Knowledge Is Power in Four Dimensions under Energy subtitle.
基金the National Natural Science Foundation of China(No.51821004)the Major Program of the National Natural Science Foundation of China(No.52090062)the Interdisciplinary Innovation Program of North China Electric Power University,and the China Scholarship Council(CSC).
文摘Energy transition towards clean,efficient energy supply has been a common sense of the government and public in China.However,lacking reasonable planning will lead to undisciplined development,resource waste,and excessive investment.In this context,this paper investigates potential pathways of Beijing energy transition towards a high-level low-carbon,clean and efficient energy system in 2035 with an extended energysocpe model.Firstly,based on available data,future energy demands are predicted by a newly proposed hybrid forecasting method,which combines the traditional regression model,grey model,and support vector machine model with an entropy-based weighted factor.Secondly,the superstructure-based optimization model is employed to investigate the system configuration and operation strategy of the future Beijing energy system.Finally,the uncertainty impact of electricity price,natural gas price,hydrogen price,and the capital expenditures of electrolyzer and steam methane reforming for hydrogen applications are studied.The forecasting results show that all walks of life will witness a continuously increasing energy demand in multiple sectors of Beijing towards 2035.The planning results suggest that the imported electricity and natural gas will dominate the energy supply of Beijing in 2035 with a contribution of 86%of the energy resources consumption of 384 TWh.Moreover,the energy system presents a high end-use electrification level of 65%and high penetration of efficient technologies,which supply 119 TWh via combined heat and power,26 TWh via heat pump and 95 TWh via district heating network.The energy use of various sectors of energy resources,technologies and end-use are closely related.Hydrogen will have an increased penetration in the private mobility sector,but the locally generated hydrogen is mainly from steam methane reforming technology.