For a three-echelon stochastic inventory system in the supply chain, it is difficult to get optimal inventory control policies by analytical methods. The paper sets up an optimization model, which is built on the basis of the discrete event system simulation theory. The simulation-based optimization method is used to solve the problem by combining the simulation model and the particle swarm optimization algorithm together. The simulation sample not only demonstrates the feasibility and the effectiveness of the simulation-based optimization method by comparisons and analyses but also shows its applicability in the supply chain management.
{"title":"An Optimization Model of Multi-echelon Stochastic Inventory System in the Supply Chain","authors":"Hua Jiang, Junhu Ruan","doi":"10.1109/ICRMEM.2008.117","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.117","url":null,"abstract":"For a three-echelon stochastic inventory system in the supply chain, it is difficult to get optimal inventory control policies by analytical methods. The paper sets up an optimization model, which is built on the basis of the discrete event system simulation theory. The simulation-based optimization method is used to solve the problem by combining the simulation model and the particle swarm optimization algorithm together. The simulation sample not only demonstrates the feasibility and the effectiveness of the simulation-based optimization method by comparisons and analyses but also shows its applicability in the supply chain management.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127622008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Based on the actual control of listed companies as the standards of the ultimately control, the state-controlled listed companies will be divided into county government control, the municipal government control, the provincial government control and the central government control. this paper provides an analysis of the relationship between government control level and cash dividends of the listed companies, using all the state controlled listed companies which pay dividends in 2003 or 2004. This paper concludes that the higher level of government control, and the more cash dividends that the company paid.
{"title":"Government Control Level and Cash Dividend Policy - An Empirial Evidence from the China Securities Market","authors":"Jianhui Jian, Zhong Yu, Pinglin He","doi":"10.1109/ICRMEM.2008.52","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.52","url":null,"abstract":"Based on the actual control of listed companies as the standards of the ultimately control, the state-controlled listed companies will be divided into county government control, the municipal government control, the provincial government control and the central government control. this paper provides an analysis of the relationship between government control level and cash dividends of the listed companies, using all the state controlled listed companies which pay dividends in 2003 or 2004. This paper concludes that the higher level of government control, and the more cash dividends that the company paid.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124145486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Short-term load forecasting (STLF) has always been a very important issue in power system planning and operation. Recently, along with power system privatization and deregulation, accurate forecast of electricity load has received increasing attention. Electricity load forecasting is complex to conduct due to its nonlinearity of influenced factors. Support vector machine (SVM) is a novel type of learning machine, which has been successfully employed to solve nonlinear regression and time series problems. In this paper, a new optimal model has been proposed, which integrates a traditional support vector machines (SVM) forecasting technique with the reduction attributes of rough sets (RS) based on immune genetic algorithm (IGA) to form a new forecasting model. The model is proved to be able to enhance the accuracy and search ability to the whole of the algorithm and reduce operation time by numerical experiments. Subsequently, examples of electricity load data from a city in China are used to illustrate the performance of the proposed model. The empirical results reveal that the proposed model outperforms the other models. Therefore, the model provides an effective and feasible arithmetic to forecast electricity load in power industry.
{"title":"Electricity Load Forecasting Using Rough Set Attribute Reduction Algorithm Based on Immune Genetic Algorithm and Support Vector Machines","authors":"Jingmin Wang, Zejian Liu, Pan Lu","doi":"10.1109/ICRMEM.2008.85","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.85","url":null,"abstract":"Short-term load forecasting (STLF) has always been a very important issue in power system planning and operation. Recently, along with power system privatization and deregulation, accurate forecast of electricity load has received increasing attention. Electricity load forecasting is complex to conduct due to its nonlinearity of influenced factors. Support vector machine (SVM) is a novel type of learning machine, which has been successfully employed to solve nonlinear regression and time series problems. In this paper, a new optimal model has been proposed, which integrates a traditional support vector machines (SVM) forecasting technique with the reduction attributes of rough sets (RS) based on immune genetic algorithm (IGA) to form a new forecasting model. The model is proved to be able to enhance the accuracy and search ability to the whole of the algorithm and reduce operation time by numerical experiments. Subsequently, examples of electricity load data from a city in China are used to illustrate the performance of the proposed model. The empirical results reveal that the proposed model outperforms the other models. Therefore, the model provides an effective and feasible arithmetic to forecast electricity load in power industry.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114384985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Electricity futures exchange is usually regarded as a valuable way to reduce price volatility of electricity market, but its risk to the equilibrium of the market is almost neglected; therefore, it is impossible to find out a workable approach to avoid the impact from supply and demand to electricity market. However, it is so important that it should not be avoided. In view of the case, this paper mainly researches the power plantspsila feasible allocation strategies of generation capacity and their maximum profits with concerning the introduction of electricity future. The models are constructed in this paper concerned both of electricity future being and not. On the basis of comparison, the paper shows that the future transaction will initiate the imbalance between supply and demand of electricity; in the meanwhile, the paper finds that there is a sole optimal strategy for power plants to increase their profits, and the strategy is a Nash-equilibrium. These mean that although transaction of electricity futures is helpful to improve the efficiency of electricity markets from the view of the price, it will bring about direct impact on the equilibrium between supply and demand; therefore, it is necessary to redesign the transaction rules and regulation for being on guard against the impact.
{"title":"Inherent Supply Risk of Electricity Futures to Electricity Market","authors":"Wu Zhong-qun","doi":"10.1109/ICRMEM.2008.97","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.97","url":null,"abstract":"Electricity futures exchange is usually regarded as a valuable way to reduce price volatility of electricity market, but its risk to the equilibrium of the market is almost neglected; therefore, it is impossible to find out a workable approach to avoid the impact from supply and demand to electricity market. However, it is so important that it should not be avoided. In view of the case, this paper mainly researches the power plantspsila feasible allocation strategies of generation capacity and their maximum profits with concerning the introduction of electricity future. The models are constructed in this paper concerned both of electricity future being and not. On the basis of comparison, the paper shows that the future transaction will initiate the imbalance between supply and demand of electricity; in the meanwhile, the paper finds that there is a sole optimal strategy for power plants to increase their profits, and the strategy is a Nash-equilibrium. These mean that although transaction of electricity futures is helpful to improve the efficiency of electricity markets from the view of the price, it will bring about direct impact on the equilibrium between supply and demand; therefore, it is necessary to redesign the transaction rules and regulation for being on guard against the impact.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117078041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The combination application of electric heat pump and storage capacity technology has simultaneously achieved two goals, of which one is energy saving and the other is improvement on electricity load characteristic. The performance of the combination system engineering is tested on site. Based on the analysis result, problems and ameliorative suggestions existed in the engineering practice are put forward.
{"title":"On Site Test and Analysis about the Electric Heat Pump Project of Storage Capacity Mode","authors":"Zhang Yu, Guan Yong, Bai-ting, Wang He","doi":"10.1109/ICRMEM.2008.114","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.114","url":null,"abstract":"The combination application of electric heat pump and storage capacity technology has simultaneously achieved two goals, of which one is energy saving and the other is improvement on electricity load characteristic. The performance of the combination system engineering is tested on site. Based on the analysis result, problems and ameliorative suggestions existed in the engineering practice are put forward.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128363940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The use of neural networks (NNs) for financial applications is quite common because of their excellent performances of treating non-linear data with self-learning capability. Often arises the problem of a black-box approach,i.e. after having trained neural networks for a particular problem, it is almost impossible to analyse them for how they work. The Fuzzy Neural Networks(FNN) allow to add rules to neural networks. This avoids the black-box but lacks of effective learning capability. To overcome these drawbacks, in this study a Integration of Genetic Algorithm and fuzzy neural networks (GFNN) are proposed to forecast corporation bankruptcy. The results indicate that the predictive accuracies obtained from GFNN are much higher than the ones obtained from NNs. To make this clearer, an illustrative example is also demonstrated in this study.
{"title":"A Genetic Fuzzy Neural Network for Bankruptcy Prediction in Chinese Corporations","authors":"Huang Fu-yuan","doi":"10.1109/ICRMEM.2008.93","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.93","url":null,"abstract":"The use of neural networks (NNs) for financial applications is quite common because of their excellent performances of treating non-linear data with self-learning capability. Often arises the problem of a black-box approach,i.e. after having trained neural networks for a particular problem, it is almost impossible to analyse them for how they work. The Fuzzy Neural Networks(FNN) allow to add rules to neural networks. This avoids the black-box but lacks of effective learning capability. To overcome these drawbacks, in this study a Integration of Genetic Algorithm and fuzzy neural networks (GFNN) are proposed to forecast corporation bankruptcy. The results indicate that the predictive accuracies obtained from GFNN are much higher than the ones obtained from NNs. To make this clearer, an illustrative example is also demonstrated in this study.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"297-301 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130803935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The paper proposes credit risk assessment model of commercial banks based on fuzzy probabilistic neural network model (FPNN) which combines the relative membership degree in fuzzy mathematics with Probabilistic Neural Network (PNN). The model makes up for a deficiency of ANN and BP arithmetic. Finally, an example is used to prove the calculation of this method is succinct rapid, and the evaluative result is objective.
{"title":"Research on Credit Risk Assessment Model of Commercial Banks Based on Fuzzy Probabilistic Neural Network","authors":"Yuansheng Huang, Chengfang Tian","doi":"10.1109/ICRMEM.2008.33","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.33","url":null,"abstract":"The paper proposes credit risk assessment model of commercial banks based on fuzzy probabilistic neural network model (FPNN) which combines the relative membership degree in fuzzy mathematics with Probabilistic Neural Network (PNN). The model makes up for a deficiency of ANN and BP arithmetic. Finally, an example is used to prove the calculation of this method is succinct rapid, and the evaluative result is objective.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131245615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The stable prices rose in the real estate market attracted a large amount of funds injected into it, to choose a good investment environment has been a keyto get profit from investment. In this paper, a Support Vector Machine (SVM) model is founded to do the evaluation. Based on the comprehensive evaluation index system of real estate investment environment, Rough set (RS) is introduced to reduce numbers of evaluation indicators, thus reducing the dimensions of the input space of SVM., when treating the reduced data as the input space of SVM, we find that both the convergence speed and the classify accuracy are enhanced in comparison with the general SVM comprehensive evaluation method and BP evaluation method.
{"title":"Application of SVM Based on Rough Set in Real Estate Investment Environment Comprehensive Evaluation","authors":"Ting Wang, Yanqing Li","doi":"10.1109/ICRMEM.2008.22","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.22","url":null,"abstract":"The stable prices rose in the real estate market attracted a large amount of funds injected into it, to choose a good investment environment has been a keyto get profit from investment. In this paper, a Support Vector Machine (SVM) model is founded to do the evaluation. Based on the comprehensive evaluation index system of real estate investment environment, Rough set (RS) is introduced to reduce numbers of evaluation indicators, thus reducing the dimensions of the input space of SVM., when treating the reduced data as the input space of SVM, we find that both the convergence speed and the classify accuracy are enhanced in comparison with the general SVM comprehensive evaluation method and BP evaluation method.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126678863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to implement the multiple factors and multi-target assessment of real estate investment risk, a model of real estate investment risk evaluation based on fuzzy DEA method was put forward by using the relatively effective evaluation characteristic of Data Envelopment Analysis (DEA). And the feasibility of this method was verified through an example. This study offers a more rational basis for making decision to real estate investment.
{"title":"Research of Real Estate Investment Risk Evaluation Based on Fuzzy Data Envelopment Analysis Method","authors":"Z. Shu-jing, Zhang Zhong-da, Li Yan-cang","doi":"10.1109/ICRMEM.2008.113","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.113","url":null,"abstract":"In order to implement the multiple factors and multi-target assessment of real estate investment risk, a model of real estate investment risk evaluation based on fuzzy DEA method was put forward by using the relatively effective evaluation characteristic of Data Envelopment Analysis (DEA). And the feasibility of this method was verified through an example. This study offers a more rational basis for making decision to real estate investment.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126321110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhao Chang-hong, Kang Jian-gang, Wang Jing, Yuan Jiahai
Intelligent engineering (IE) has been proposed and many real world applications have preliminarily demonstrated its viability. The main idea of intelligent engineering is multi-space operation for complex problem solving. This paper discusses on subspace operation algorithm in subspace relation in IE. Two subspace relation algorithms of serial and parallel operator are defined. We show the viability of the algorithm by applying it in the water ecology system modeling and discuss in details the modeling process.
{"title":"Intelligent Engineering and Application in Water Ecology System Model","authors":"Zhao Chang-hong, Kang Jian-gang, Wang Jing, Yuan Jiahai","doi":"10.1109/ICRMEM.2008.64","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.64","url":null,"abstract":"Intelligent engineering (IE) has been proposed and many real world applications have preliminarily demonstrated its viability. The main idea of intelligent engineering is multi-space operation for complex problem solving. This paper discusses on subspace operation algorithm in subspace relation in IE. Two subspace relation algorithms of serial and parallel operator are defined. We show the viability of the algorithm by applying it in the water ecology system modeling and discuss in details the modeling process.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"235 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122438248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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