Irrigation in lowland rice production systems in Sub-Saharan Africa (SSA) is mainly based on traditional surface irrigation methods with continuous flooding practices. This irrigation method ends up using a lot more w...Irrigation in lowland rice production systems in Sub-Saharan Africa (SSA) is mainly based on traditional surface irrigation methods with continuous flooding practices. This irrigation method ends up using a lot more water that would have otherwise been used to open more land and be used in other water-requiring sectors. Various studies suggest Alternate Wetting and Drying (AWD) as an alternative practice for water management that reduces water use without significantly affecting yield. However, this practice has not been well adopted by the farmers despite its significant benefits of reduced total water use. Improving the adoption of AWD using irrigation Decision Support Systems (DSSs) helps the farmer on two fronts;to know “how much water to apply” and “when to irrigate”, which is very critical in maximizing productivity. This paper reviews the applicability of DSSs using AWD in lowland rice production systems in Sub-Saharan Africa.展开更多
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
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
With the existence of several conventional and advanced building thermal energy demand forecast models to improve the energy efficiency of buildings,it is hard to find an appropriate,convenient,and efficient model.Eva...With the existence of several conventional and advanced building thermal energy demand forecast models to improve the energy efficiency of buildings,it is hard to find an appropriate,convenient,and efficient model.Evaluations based on statistical indexes(MAE,RMSE,MAPE,etc.)that characterize the accuracy of the forecasts do not help in the identification of the efficient building thermal energy demand forecast tool since they do not reflect the efforts entailed in implementation of the forecast model,i.e.,data collection to production/use phase.Hence,this work presents a Gini Index based Measurement of Alternatives and Ranking according to COmpromise Solution(GI-MARCOS),a hybrid Multi Attribute Decision Making(MADM)approach for the identification of the most efficient building energy demand forecast tool.GI-MARCOS employs(i)GI based objective weight method:assigns meaningful objective weights to the attributes in four phases(1:pre-processing,2:implementation,3:post-processing,and 4:use phase)thereby avoiding unnecessary biases in the expert’s opinion on weights and applicable to domains where there is a lack of domain expertise,and(ii)MARCOS:provides a robust and reliable ranking of alternatives in a dynamic environment.A case study with three alternatives evaluated over three to six attributes in four phases of implementation(pre-processing,implementation,post-processing and use)reveals that the use of GI-MARCOS improved the accuracy of alternatives MLR and BM by 6%and 13%,respectively.Moreover,additional validations state that(i)MLR performs best in Phase 1 and 2,while ANN performs best in Phase 3 and 4 with BM providing a mediocre performance in all four phases,(ii)sensitivity analysis:provides robust ranking with interchange of weights across phases and attributes,and(iii)rank correlation:ranks produce by GI-MARCOS has a high correlation with GRA(0.999),COPRAS(0.9786),and ARAS(0.9775).展开更多
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 a...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.展开更多
As the main body of air traffic control safety,the air traffic controller is an important part of the whole air traffic control system. According to the relevant data of civil aviation over the years,a mapping model b...As the main body of air traffic control safety,the air traffic controller is an important part of the whole air traffic control system. According to the relevant data of civil aviation over the years,a mapping model between flight support sorties and air traffic controller demand is constructed by using the prediction algorithm of support vector regression(SVR) based on grid search and cross-validation. Then the model predicts the demand for air traffic controllers in seven regions. Additionally,according to the employment data of civil aviation universities,the future training scale of air traffic controller is predicted. The forecast results show that the average relative error of the number of controllers predicted by the algorithm is 1.73%,and the prediction accuracy is higher than traditional regression algorithms. Under the influence of the epidemic,the demand for air traffic controllers will decrease in the short term,but with the control of the epidemic,the demand of air traffic controllers will return to the pre-epidemic level and gradually increase. It is expected that the controller increment will be about 816 by 2028. The forecast results of the demand for air traffic controllers provide a theoretical basis for the introduction and training of medium and long-term air traffic controllers,and also provide method guidance and decision support for the establishment of professional reserve and dynamic control mechanism in the air traffic control system.展开更多
Tourism demand forecasting has attracted substantial interest because of the significant economic contributions of the fast-growing tourism industry. Although various quantitative forecasting techniques have been wide...Tourism demand forecasting has attracted substantial interest because of the significant economic contributions of the fast-growing tourism industry. Although various quantitative forecasting techniques have been widely studied, highly accurate and understandable forecasting models have not been developed. The present paper proposes a novel tourism demand forecasting method that extracts fuzzy Takagi-Sugeno （T-S） rules from trained SVMs. Unlike previous approaches, this study uses fuzzy T-S models extracted from the outputs of trained SVMs on tourism data. Owing to the symbolic fuzzy rules and the generalization ability of SVMs, the extracted fuzzy T-S rules exhibit high forecasting accuracy and include understandable pre-condition parts for practitioners. Based on the tourism demand forecasting problem in Hong Kong SAR, China as a case study, empirical findings on tourist arrivals from nine overseas origins reveal that the proposed approach performs comparably with SVMs and can achieve better prediction accuracy than other forecasting techniques for most origins. The findings demonstrated that decision makers can easily interpret fuzzy T-S rules extracted from SVMs. Thus, the approach is highly beneficial to tourism market management. This finding demonstrates the excellent scientific and practical values of the proposed approach in tourism demand forecasting.展开更多
Enterprise Information System management has become an increasingly vital factor for many firms. Several organizations have encountered problems when attempting to evaluate organizational performance. Measurement of p...Enterprise Information System management has become an increasingly vital factor for many firms. Several organizations have encountered problems when attempting to evaluate organizational performance. Measurement of performance metrics is a key challenge for a huge number of firms. In order to preserve relevance and adaptability in competitive markets, it has become essential to respond proactively to complex events through informed decision-making that is supported by technology. Therefore, the objective of this study was to apply neural networks to the modeling, simulation, and forecasting of the effects of the performance indicators of Enterprise Information Systems on the achievement of corporate objectives and value creation. A set of quantifiable and sizeable conditionally independent associations were derived using a simplified joint probability distribution technique. Bayesian Neural Networks were utilized to describe the link between random variables (features) and to concisely and easily specify the joint probability distribution. The research demonstrated that Bayesian networks could effectively explore complex logical linkages by employing probability to represent uncertainty and probabilistic rules;and by applying impact models from Bayesian taxonomies to achieve learning and reasoning processes.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
文摘Irrigation in lowland rice production systems in Sub-Saharan Africa (SSA) is mainly based on traditional surface irrigation methods with continuous flooding practices. This irrigation method ends up using a lot more water that would have otherwise been used to open more land and be used in other water-requiring sectors. Various studies suggest Alternate Wetting and Drying (AWD) as an alternative practice for water management that reduces water use without significantly affecting yield. However, this practice has not been well adopted by the farmers despite its significant benefits of reduced total water use. Improving the adoption of AWD using irrigation Decision Support Systems (DSSs) helps the farmer on two fronts;to know “how much water to apply” and “when to irrigate”, which is very critical in maximizing productivity. This paper reviews the applicability of DSSs using AWD in lowland rice production systems in Sub-Saharan Africa.
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.
基金supported by The Indian Institute of Technology-Bombay(Institute Postdoctoral Fellowship-AO/Admin-1/Rect/33/2019).
文摘With the existence of several conventional and advanced building thermal energy demand forecast models to improve the energy efficiency of buildings,it is hard to find an appropriate,convenient,and efficient model.Evaluations based on statistical indexes(MAE,RMSE,MAPE,etc.)that characterize the accuracy of the forecasts do not help in the identification of the efficient building thermal energy demand forecast tool since they do not reflect the efforts entailed in implementation of the forecast model,i.e.,data collection to production/use phase.Hence,this work presents a Gini Index based Measurement of Alternatives and Ranking according to COmpromise Solution(GI-MARCOS),a hybrid Multi Attribute Decision Making(MADM)approach for the identification of the most efficient building energy demand forecast tool.GI-MARCOS employs(i)GI based objective weight method:assigns meaningful objective weights to the attributes in four phases(1:pre-processing,2:implementation,3:post-processing,and 4:use phase)thereby avoiding unnecessary biases in the expert’s opinion on weights and applicable to domains where there is a lack of domain expertise,and(ii)MARCOS:provides a robust and reliable ranking of alternatives in a dynamic environment.A case study with three alternatives evaluated over three to six attributes in four phases of implementation(pre-processing,implementation,post-processing and use)reveals that the use of GI-MARCOS improved the accuracy of alternatives MLR and BM by 6%and 13%,respectively.Moreover,additional validations state that(i)MLR performs best in Phase 1 and 2,while ANN performs best in Phase 3 and 4 with BM providing a mediocre performance in all four phases,(ii)sensitivity analysis:provides robust ranking with interchange of weights across phases and attributes,and(iii)rank correlation:ranks produce by GI-MARCOS has a high correlation with GRA(0.999),COPRAS(0.9786),and ARAS(0.9775).
基金Project(70901025) supported by the National Natural Science Foundation of China
文摘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.
基金supported by the National Natural Science Foundation of China(No.71971114)。
文摘As the main body of air traffic control safety,the air traffic controller is an important part of the whole air traffic control system. According to the relevant data of civil aviation over the years,a mapping model between flight support sorties and air traffic controller demand is constructed by using the prediction algorithm of support vector regression(SVR) based on grid search and cross-validation. Then the model predicts the demand for air traffic controllers in seven regions. Additionally,according to the employment data of civil aviation universities,the future training scale of air traffic controller is predicted. The forecast results show that the average relative error of the number of controllers predicted by the algorithm is 1.73%,and the prediction accuracy is higher than traditional regression algorithms. Under the influence of the epidemic,the demand for air traffic controllers will decrease in the short term,but with the control of the epidemic,the demand of air traffic controllers will return to the pre-epidemic level and gradually increase. It is expected that the controller increment will be about 816 by 2028. The forecast results of the demand for air traffic controllers provide a theoretical basis for the introduction and training of medium and long-term air traffic controllers,and also provide method guidance and decision support for the establishment of professional reserve and dynamic control mechanism in the air traffic control system.
文摘Tourism demand forecasting has attracted substantial interest because of the significant economic contributions of the fast-growing tourism industry. Although various quantitative forecasting techniques have been widely studied, highly accurate and understandable forecasting models have not been developed. The present paper proposes a novel tourism demand forecasting method that extracts fuzzy Takagi-Sugeno （T-S） rules from trained SVMs. Unlike previous approaches, this study uses fuzzy T-S models extracted from the outputs of trained SVMs on tourism data. Owing to the symbolic fuzzy rules and the generalization ability of SVMs, the extracted fuzzy T-S rules exhibit high forecasting accuracy and include understandable pre-condition parts for practitioners. Based on the tourism demand forecasting problem in Hong Kong SAR, China as a case study, empirical findings on tourist arrivals from nine overseas origins reveal that the proposed approach performs comparably with SVMs and can achieve better prediction accuracy than other forecasting techniques for most origins. The findings demonstrated that decision makers can easily interpret fuzzy T-S rules extracted from SVMs. Thus, the approach is highly beneficial to tourism market management. This finding demonstrates the excellent scientific and practical values of the proposed approach in tourism demand forecasting.
文摘Enterprise Information System management has become an increasingly vital factor for many firms. Several organizations have encountered problems when attempting to evaluate organizational performance. Measurement of performance metrics is a key challenge for a huge number of firms. In order to preserve relevance and adaptability in competitive markets, it has become essential to respond proactively to complex events through informed decision-making that is supported by technology. Therefore, the objective of this study was to apply neural networks to the modeling, simulation, and forecasting of the effects of the performance indicators of Enterprise Information Systems on the achievement of corporate objectives and value creation. A set of quantifiable and sizeable conditionally independent associations were derived using a simplified joint probability distribution technique. Bayesian Neural Networks were utilized to describe the link between random variables (features) and to concisely and easily specify the joint probability distribution. The research demonstrated that Bayesian networks could effectively explore complex logical linkages by employing probability to represent uncertainty and probabilistic rules;and by applying impact models from Bayesian taxonomies to achieve learning and reasoning processes.
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.
