In order to study the effects of the contract coordination in the supply chain, the complex adaptive system (CAS), Agent model-building and supply chain theories were introduced, and the competitive rules and assumption conditions of simulation of the corporations in the supply chain were described. Then competitive environment of the supply chain was constructed on the Swarm simulation platform, and then the three types of contractspsila processes (the optimization of the supply chain, the optimization of the supplier and the retailer) were simulated. The results indicate that an appropriate contract can make all the corporations beneficial and almost maximization, and it can coordinate and promote the whole supply chain continuous and healthy development, which is the goal of contract design. The results have strong practical and instructional significance.
{"title":"Research on Supply Chain Contract Coordination Simulation Based on Swarm","authors":"Xiuguang Bai, Huaying Shu","doi":"10.1109/ICRMEM.2008.15","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.15","url":null,"abstract":"In order to study the effects of the contract coordination in the supply chain, the complex adaptive system (CAS), Agent model-building and supply chain theories were introduced, and the competitive rules and assumption conditions of simulation of the corporations in the supply chain were described. Then competitive environment of the supply chain was constructed on the Swarm simulation platform, and then the three types of contractspsila processes (the optimization of the supply chain, the optimization of the supplier and the retailer) were simulated. The results indicate that an appropriate contract can make all the corporations beneficial and almost maximization, and it can coordinate and promote the whole supply chain continuous and healthy development, which is the goal of contract design. The results have strong practical and instructional significance.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"37 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":"134593976","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}
This paper investigates R&D efficiency (intermediate output efficiency and final output efficiency) of Chinese new high-tech industry and its influencing factors through stochastic frontier analysis method based on panel data. After dividing the new high-tech industry into five different trades (23 subdivided trades), comparative study is carried on concerning R&D efficiencies among the divisions. Finally, suggestions are given on the improvements of R&D efficiencies of Chinese new high-tech industry.
{"title":"A Study of R&D Efficiency of Chinese New High-Tech Industry Based on Stochastic Frontier Analysis","authors":"Tong Liang, Xiu-de Chen","doi":"10.1109/ICRMEM.2008.21","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.21","url":null,"abstract":"This paper investigates R&D efficiency (intermediate output efficiency and final output efficiency) of Chinese new high-tech industry and its influencing factors through stochastic frontier analysis method based on panel data. After dividing the new high-tech industry into five different trades (23 subdivided trades), comparative study is carried on concerning R&D efficiencies among the divisions. Finally, suggestions are given on the improvements of R&D efficiencies of Chinese new high-tech industry.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"329 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":"134159265","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 improved the evaluation model of real estate project risk which based on Monte Carlo simulation technology. That is use three point which are maximum possible value, minimum possible value and the most possible value to estimate the risk variable. Using AHP method, variable weight of delivered and extended risk to determine the weight of each risk factor in the model objectively, and verifying the method validity by example. The result denote the model can be effective to evaluate the real estate project risks.
{"title":"The Research on Real Estate Project Risk Evaluation Based on Monte Carlo Simulation and the Theory of Variable Weight","authors":"Li Shuang-chen, Yang Yu-mei","doi":"10.1109/ICRMEM.2008.37","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.37","url":null,"abstract":"The paper improved the evaluation model of real estate project risk which based on Monte Carlo simulation technology. That is use three point which are maximum possible value, minimum possible value and the most possible value to estimate the risk variable. Using AHP method, variable weight of delivered and extended risk to determine the weight of each risk factor in the model objectively, and verifying the method validity by example. The result denote the model can be effective to evaluate the real estate project risks.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"10 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":"122182079","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}
Relative contribution effectiveness (RCE) of leaders' main body is the core part of comprehensive indexes of government leaders' performance (GLP), meanwhile showing valid increase of organizational performance (OP) led by subjective efforts. The development of government system is composed of one by itself and the other by leaderspsila main body from the future of OP. In this paper RCE can be measured by building the benefit possible set to weigh effectiveness of present benefit indexes by the method of data envelopment analysis (DEA). Its results lay a solid foundation for further study on leaderspsila performance evaluation.
{"title":"Analysis on Evaluating Relative Contribution Effectiveness of Government Leaders' Performance","authors":"Wei Wang, Yongzhi Yao","doi":"10.1109/ICRMEM.2008.120","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.120","url":null,"abstract":"Relative contribution effectiveness (RCE) of leaders' main body is the core part of comprehensive indexes of government leaders' performance (GLP), meanwhile showing valid increase of organizational performance (OP) led by subjective efforts. The development of government system is composed of one by itself and the other by leaderspsila main body from the future of OP. In this paper RCE can be measured by building the benefit possible set to weigh effectiveness of present benefit indexes by the method of data envelopment analysis (DEA). Its results lay a solid foundation for further study on leaderspsila performance evaluation.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"19 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":"125141146","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}
To the loan offers, credit risk evaluation is the decisive link for investment. In order to evaluate credit of construction enterprises more scientifically and comprehensively, this paper establishes a systematic evaluation system, in which indexes, such as comprehensive loans status, qualities of leaders, third-party guarantee, have received due attention, and peculiar characteristics of the construction industry are full considered. As an advanced system, the Back Propagation (BP) neural network has found wide application in comprehensive evaluation, however, it increasingly shows its limitations, such as slow convergent speed and easy convergence to the local minimum points. To break through and develop, this paper proposes a new evaluation model that combined ant colony algorithm (ACA) with radial basis function (RBF) neural network, which performs better in extensive mapping ability, the evaluation accuracy, convergence rate, distributed computation of ACA and training span. Take credit status of 30 construction enterprises as samples, experimental results shows that it is effective and suitable to apply this method to credit comprehensive evaluation.
{"title":"Application of RBF Neural Network Based on Ant Colony Algorithm in Credit Risk Evaluation of Construction Enterprises","authors":"Wu Yunna, Si Zhaomin","doi":"10.1109/ICRMEM.2008.54","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.54","url":null,"abstract":"To the loan offers, credit risk evaluation is the decisive link for investment. In order to evaluate credit of construction enterprises more scientifically and comprehensively, this paper establishes a systematic evaluation system, in which indexes, such as comprehensive loans status, qualities of leaders, third-party guarantee, have received due attention, and peculiar characteristics of the construction industry are full considered. As an advanced system, the Back Propagation (BP) neural network has found wide application in comprehensive evaluation, however, it increasingly shows its limitations, such as slow convergent speed and easy convergence to the local minimum points. To break through and develop, this paper proposes a new evaluation model that combined ant colony algorithm (ACA) with radial basis function (RBF) neural network, which performs better in extensive mapping ability, the evaluation accuracy, convergence rate, distributed computation of ACA and training span. Take credit status of 30 construction enterprises as samples, experimental results shows that it is effective and suitable to apply this method to credit comprehensive evaluation.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"1 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":"129456588","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 the electricity market of imperfect competition, the behavior of generation bidding is affected by many risk factors, which include fuel price, weather condition, load forecasting and so on. The potential impact of bidding strategy is quantitative calculation, which adapted from risk factors; the risk management on bidding strategy choice is brought forward, and considering the diversity of risk preference due to difference decision makers. The correctness and necessity is proved by numeral example.
{"title":"Generation Company Bidding Strategy based on Risk Factors","authors":"Li-ying Zhang, Jian-xun Qi","doi":"10.1109/ICRMEM.2008.110","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.110","url":null,"abstract":"In the electricity market of imperfect competition, the behavior of generation bidding is affected by many risk factors, which include fuel price, weather condition, load forecasting and so on. The potential impact of bidding strategy is quantitative calculation, which adapted from risk factors; the risk management on bidding strategy choice is brought forward, and considering the diversity of risk preference due to difference decision makers. The correctness and necessity is proved by numeral example.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"41 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":"128894350","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}
This paper optimizes the wavelet neural networks with genetic algorithms which has the optimization of the overall search capabilities, and establishes the model of wavelet neural networks based on genetic algorithms. It overcomes the shortcomings of BP neural network for their own, and it can get higher accuracy and faster convergence. The examples also show that the model can improve forecast accuracy effectively, reducing the error of load forecasting, and the inherent defects of BP neural network have been avoid.
{"title":"Application for Short-Term Power Load Forecasting Using Improved Wavelet Neural Networks Based on GA","authors":"Jia Zheng-yuan, Tian Li, Zhao Dan","doi":"10.1109/ICRMEM.2008.40","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.40","url":null,"abstract":"This paper optimizes the wavelet neural networks with genetic algorithms which has the optimization of the overall search capabilities, and establishes the model of wavelet neural networks based on genetic algorithms. It overcomes the shortcomings of BP neural network for their own, and it can get higher accuracy and faster convergence. The examples also show that the model can improve forecast accuracy effectively, reducing the error of load forecasting, and the inherent defects of BP neural network have been avoid.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"48 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":"124327634","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}
This paper is on how to build an investment model of security investment fund which will produce the maximum in profits. The deficiencies in Markowitzpsilas portfolio selection decision model are analysed. In this paper, the writers use nonlinear and dynamic model depending on Return-Variance model under the limited condition to decide how to invest. The design method of fund portfolio is presented which can optimize portfolio gain with the mixture experience design applying.
{"title":"Optimal Investment Decision of Security Investment Fund Based on the Experiment Design","authors":"Chenguang Wei, Jin-yu Wang, Jing-ting Ma","doi":"10.1109/ICRMEM.2008.51","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.51","url":null,"abstract":"This paper is on how to build an investment model of security investment fund which will produce the maximum in profits. The deficiencies in Markowitzpsilas portfolio selection decision model are analysed. In this paper, the writers use nonlinear and dynamic model depending on Return-Variance model under the limited condition to decide how to invest. The design method of fund portfolio is presented which can optimize portfolio gain with the mixture experience design applying.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"45 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":"124432772","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 electric power industry is regarded as the basic industry of national economy, which makes it unable to get rid of the governmentpsilas regulation, as well as its own technology and economic characteristic. After reforming of regulation, government pays a good deal of attention to the performance-base regulation which has been used in telecommunication successfully and can solve the low efficiency problem effectively. But the performance-base regulation method cause the contradiction between efficiency and quality, so people call in query to its efficiency. On this background, after analyzing the price cap regulation method and considering the situation of power reforming in our country, the paper propose the optimal electricity transmission pricing regulation method which contains the factor of quality and also can solve the contradiction between efficiency and quality in a certain extent. Then the paper puts forward some suggestion on decision of parameters and makes study example of quality factor.
{"title":"Research on the Optimal Regulation Model of Electricity Transmission and Distribution Price","authors":"Liang Zhou, Zhang Ting, Xu Zhi-yong","doi":"10.1109/ICRMEM.2008.121","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.121","url":null,"abstract":"The electric power industry is regarded as the basic industry of national economy, which makes it unable to get rid of the governmentpsilas regulation, as well as its own technology and economic characteristic. After reforming of regulation, government pays a good deal of attention to the performance-base regulation which has been used in telecommunication successfully and can solve the low efficiency problem effectively. But the performance-base regulation method cause the contradiction between efficiency and quality, so people call in query to its efficiency. On this background, after analyzing the price cap regulation method and considering the situation of power reforming in our country, the paper propose the optimal electricity transmission pricing regulation method which contains the factor of quality and also can solve the contradiction between efficiency and quality in a certain extent. Then the paper puts forward some suggestion on decision of parameters and makes study example of quality factor.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"146 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":"122610626","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}
As the power load forecasting sequence has stochastic growth and nonlinear wave characteristics, grey neural network model can effective reflect the growth properties of the sequence and fit the nonlinear relation. Markov chain can easily embody the random characteristic of system by complex factors, so the Markov chain error correction method was introduce in this paper, the whole forecasting precision of the sequence was optimized, and the transfer matrix for the forecasting sequence was decided, then the accuracy for power load forecasting was greatly improved. Through the demonstration test, the precision is better than ingenuous grey neural network, the method in this paper have feasibility in practice.
{"title":"The Markov Error Correcting Method in Gray Neural Network for Power Load Forecasting","authors":"D. Niu, Jia-liang Lv","doi":"10.1109/ICRMEM.2008.36","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.36","url":null,"abstract":"As the power load forecasting sequence has stochastic growth and nonlinear wave characteristics, grey neural network model can effective reflect the growth properties of the sequence and fit the nonlinear relation. Markov chain can easily embody the random characteristic of system by complex factors, so the Markov chain error correction method was introduce in this paper, the whole forecasting precision of the sequence was optimized, and the transfer matrix for the forecasting sequence was decided, then the accuracy for power load forecasting was greatly improved. Through the demonstration test, the precision is better than ingenuous grey neural network, the method in this paper have feasibility in practice.","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":"122991229","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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