Construction project risk allocation is one of the problems which all the project participants concerning about. On the basis of studying the principles of project risk allocation, this paper establish the evaluating indicator that evaluates the risk carrying capacity of all the project participants. The evaluating factor set in the risk allocation is determined, the integrated risk allocation coefficients of all project participants are calculated in the method of fuzzy integrated evaluation, and the method of the risk loss allocation of each project participant is proposed. This method clears the risk responsibility of each project participant, and points out a new train of thought in the field of quantitative analysis research of project risk allocation.
{"title":"The Risk Allocation Method Based on Fuzzy Integrated Evaluation of Construction Projects","authors":"Yun-li Gao, Lei Jiang","doi":"10.1109/ICRMEM.2008.28","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.28","url":null,"abstract":"Construction project risk allocation is one of the problems which all the project participants concerning about. On the basis of studying the principles of project risk allocation, this paper establish the evaluating indicator that evaluates the risk carrying capacity of all the project participants. The evaluating factor set in the risk allocation is determined, the integrated risk allocation coefficients of all project participants are calculated in the method of fuzzy integrated evaluation, and the method of the risk loss allocation of each project participant is proposed. This method clears the risk responsibility of each project participant, and points out a new train of thought in the field of quantitative analysis research of project risk allocation.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"42 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":"124320075","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 earned value measure is introduced into the cost and schedule control of warship planned maintenance for the over budget and schedule delays problem. The flowchart and application range of earned value measure is summarized. The application procedure of the earned value measure in the cost and schedule control of warship planned maintenance is given. Then the over budget and schedule delays are analyzed quantitatively in repair of some type warship. For different instance, the concrete forecast methods of cost at completion are put forward in the date of examine, which finally proved that the application of the earned value measure in the cost and schedule control of warship maintenance is feasible and valid.
{"title":"Study on Cost and Schedule Control in Warship Planned Maintenance","authors":"Xie Li, W. Xiang, Chen Chuan","doi":"10.1109/ICRMEM.2008.29","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.29","url":null,"abstract":"The earned value measure is introduced into the cost and schedule control of warship planned maintenance for the over budget and schedule delays problem. The flowchart and application range of earned value measure is summarized. The application procedure of the earned value measure in the cost and schedule control of warship planned maintenance is given. Then the over budget and schedule delays are analyzed quantitatively in repair of some type warship. For different instance, the concrete forecast methods of cost at completion are put forward in the date of examine, which finally proved that the application of the earned value measure in the cost and schedule control of warship maintenance is feasible and valid.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"16 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":"122856477","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}
A framework of combining importance sampling with Structured Database Monte Carlo strategy is developed. The proposed method attempts to devise a generic method for designing importance sampling method. Firstly, evaluation function and objective function are expressed in a way that there is a linear relation between response estimator and majorized function. Order structure is imposed not only on sample paths but also on parameters of candidate density. Then the parameters are estimated by surrogate maximization algorithm. Secondly, cut-off point at which response function can maintain the same sample paths structure is obtained. Based on the low quadratic bound principle and the convexity of the second moment of the estimator, a quadratic surrogate function for objective function is derived. Finally, empirical results show that our approach is straightforward to implement and flexible to be applied in a generic Monte Carlo setting.
{"title":"Structure Database Strategy for Importance Sampling and Application to Pricing Options","authors":"Gao Quan-sheng","doi":"10.1109/ICRMEM.2008.55","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.55","url":null,"abstract":"A framework of combining importance sampling with Structured Database Monte Carlo strategy is developed. The proposed method attempts to devise a generic method for designing importance sampling method. Firstly, evaluation function and objective function are expressed in a way that there is a linear relation between response estimator and majorized function. Order structure is imposed not only on sample paths but also on parameters of candidate density. Then the parameters are estimated by surrogate maximization algorithm. Secondly, cut-off point at which response function can maintain the same sample paths structure is obtained. Based on the low quadratic bound principle and the convexity of the second moment of the estimator, a quadratic surrogate function for objective function is derived. Finally, empirical results show that our approach is straightforward to implement and flexible to be applied in a generic Monte Carlo setting.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"75 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":"127225662","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 theory of quadratic variation suggests that, realized volatility is an unbiased and highly efficient estimator of return volatility under suitable conditions. In this article, we compare the realized logarithmic volatilities models VAR-RV and AR-RV computed from high-frequency intra-period data with the traditional daily return evaluation models VAR-R and Daily-GARCH in China A-stock market. The result suggests that the realized volatility do a better and more efficient measure in evaluating and forecasting the volatility characteristic for China stock market.
{"title":"The Realized Volatilities Research on China A-Stock Returns","authors":"J. Chen, Handong Li","doi":"10.1109/ICRMEM.2008.26","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.26","url":null,"abstract":"The theory of quadratic variation suggests that, realized volatility is an unbiased and highly efficient estimator of return volatility under suitable conditions. In this article, we compare the realized logarithmic volatilities models VAR-RV and AR-RV computed from high-frequency intra-period data with the traditional daily return evaluation models VAR-R and Daily-GARCH in China A-stock market. The result suggests that the realized volatility do a better and more efficient measure in evaluating and forecasting the volatility characteristic for China stock market.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"49 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":"132757922","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}
Inventory control is an important aspect of production logistics management in power system. According to the characteristic of raw material purchase and stock, the paper puts forward an optimal inventory model to minimize the cost. A novel hybrid chaos immune evolutionary optimization algorithm (HCIEOA) of solving the minimal purchasing cost problem is presented. This algorithm integrates space-searching advantages of the chaos optimization algorithm (COA) and immune evolutionary algorithm (IEA). It uses the ergodic property of the chaos system to overcome redundancies, and uses the chaos initial sensitivity to enlarge the searching space. Thus, the diversity of population is retained, the local optimization is avoided, and the rapidity of global optimization is improved. Then, this model is applied to the process of searching the optimization in the purchase and storage model. At last, the example shows that the HCIEOA is effective and reliable.
{"title":"Research of Electric Power Industry's Production Logistics Model Based on Hybrid Chaos Immune Evolutionary Optimization Algorithm","authors":"Xing Mian","doi":"10.1109/ICRMEM.2008.107","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.107","url":null,"abstract":"Inventory control is an important aspect of production logistics management in power system. According to the characteristic of raw material purchase and stock, the paper puts forward an optimal inventory model to minimize the cost. A novel hybrid chaos immune evolutionary optimization algorithm (HCIEOA) of solving the minimal purchasing cost problem is presented. This algorithm integrates space-searching advantages of the chaos optimization algorithm (COA) and immune evolutionary algorithm (IEA). It uses the ergodic property of the chaos system to overcome redundancies, and uses the chaos initial sensitivity to enlarge the searching space. Thus, the diversity of population is retained, the local optimization is avoided, and the rapidity of global optimization is improved. Then, this model is applied to the process of searching the optimization in the purchase and storage model. At last, the example shows that the HCIEOA is effective and reliable.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"15 5 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":"115225917","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 combines rough sets and ANN to analyze inventory early-warning in supply chains. The introduction of Rough sets cuts down the input dimensions of ANN, and the ANN algorithm is improved by adding the momentum factor mc and applying adaptive learning rate. Lastly, according to the inventory data of a manufacturing enterprise in Handan City, the paper proves the validity of the proposed model.
{"title":"Study on Inventory Early-Warning in Supply Chains Based on Rough Sets and BP Neural Network","authors":"J. Hua, Ruan Jun-hu","doi":"10.1109/ICRMEM.2008.118","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.118","url":null,"abstract":"The paper combines rough sets and ANN to analyze inventory early-warning in supply chains. The introduction of Rough sets cuts down the input dimensions of ANN, and the ANN algorithm is improved by adding the momentum factor mc and applying adaptive learning rate. Lastly, according to the inventory data of a manufacturing enterprise in Handan City, the paper proves the validity of the proposed model.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"57 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":"114921784","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}
With the development of electricity market reformation in China, it is especially important to evaluate the competition competence of power generating enterprises. Based on the characteristics of their, this paper bring forwards an index system to evaluate the competition competence of power generating enterprises. SVMs are widely used in load forecasting and bioinformatics systems. Conventional methods are usually used in the parameter estimation process of SVMs. However, these methods can yield to local optimum parameter values. In this work, we use artificial techniques such as Artificial Immune Systems (AIS) and Genetic Algorithms (GA) to estimate SVM parameters. These techniques are global search optimization techniques inspired from biological systems. Also, the hybrid between genetic algorithms and artificial immune system was used to optimize SVM parameters to evaluate the competitivity of power plants.
{"title":"Evaluation of Competitiveness of Power Plants Based on Optimized SVM Using GA and AIS","authors":"Wei Sun, Jie Zhang","doi":"10.1109/ICRMEM.2008.124","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.124","url":null,"abstract":"With the development of electricity market reformation in China, it is especially important to evaluate the competition competence of power generating enterprises. Based on the characteristics of their, this paper bring forwards an index system to evaluate the competition competence of power generating enterprises. SVMs are widely used in load forecasting and bioinformatics systems. Conventional methods are usually used in the parameter estimation process of SVMs. However, these methods can yield to local optimum parameter values. In this work, we use artificial techniques such as Artificial Immune Systems (AIS) and Genetic Algorithms (GA) to estimate SVM parameters. These techniques are global search optimization techniques inspired from biological systems. Also, the hybrid between genetic algorithms and artificial immune system was used to optimize SVM parameters to evaluate the competitivity of power plants.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"25 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":"126781050","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}
Analysis of regional power demand cycle is the foundation of researching the national power demand cycle. Firstly the growth rate data of power demand in China from 1994 to 2005 is dealt with the H-P filter, and the periodicity factors of the nation and provinces power demand are obtained. Secondly the periodicity factors of the provinces power demand are treated with cluster analysis, and the problem whether the regional power demand cycle exist in China is discussed. Subsequently through studying the question which provinces mainly affect the national power demand cycle, the problem that the regional power demand cycle influences the national power demand cycle is analyzed. The result show that regional power demand cycle exists in China, and it plays an important role in effecting the national power demand cycle.
{"title":"The Analysis of China Regional Power Demand Cycle Existence","authors":"Shuxia Yang","doi":"10.1109/ICRMEM.2008.16","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.16","url":null,"abstract":"Analysis of regional power demand cycle is the foundation of researching the national power demand cycle. Firstly the growth rate data of power demand in China from 1994 to 2005 is dealt with the H-P filter, and the periodicity factors of the nation and provinces power demand are obtained. Secondly the periodicity factors of the provinces power demand are treated with cluster analysis, and the problem whether the regional power demand cycle exist in China is discussed. Subsequently through studying the question which provinces mainly affect the national power demand cycle, the problem that the regional power demand cycle influences the national power demand cycle is analyzed. The result show that regional power demand cycle exists in China, and it plays an important role in effecting the national power demand cycle.","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":"123357396","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 electricity load forecasting is important both from the technological and the economical point of view, but it is also a difficult work because the accuracy of forecasting is influenced by many unpredicted factors whose relationships are commonly complex, implicit and nonlinear. By studying the methods proposed by other scholars, a mew method, KPCA (kernel principal component analysis) -SVRM (support vector regression machine) is proposed by this paper. The first step of this method is to apply KPCA to SVRM for feature extraction. KPCA first maps the original inputs into a high dimensional feature space using the kernel method and then calculates PCA in the high dimensional feature space. These new features are then used as the inputs of SVRM to solve the load forecasting problem. By learning and training, we use the data of this subset to get the solution and find interrelationship of input and output by the SVRM. Practical examples are cited in this paper to illustrate the process. The KPCA-SVRM method can also be used to solve other forecasting problems.
{"title":"Combining KPCA with Support Vector Regression Machine for Short-Term Electricity Load Forecasting","authors":"Cai-qing Zhang, Pan Lu, Zejian Liu","doi":"10.1109/ICRMEM.2008.84","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.84","url":null,"abstract":"Short-term electricity load forecasting is important both from the technological and the economical point of view, but it is also a difficult work because the accuracy of forecasting is influenced by many unpredicted factors whose relationships are commonly complex, implicit and nonlinear. By studying the methods proposed by other scholars, a mew method, KPCA (kernel principal component analysis) -SVRM (support vector regression machine) is proposed by this paper. The first step of this method is to apply KPCA to SVRM for feature extraction. KPCA first maps the original inputs into a high dimensional feature space using the kernel method and then calculates PCA in the high dimensional feature space. These new features are then used as the inputs of SVRM to solve the load forecasting problem. By learning and training, we use the data of this subset to get the solution and find interrelationship of input and output by the SVRM. Practical examples are cited in this paper to illustrate the process. The KPCA-SVRM method can also be used to solve other forecasting problems.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"143 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":"124114670","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}
By rough programming, we mean the optimization theory dealing with rough decision problems. This paper constructs a general framework of rough chance-constrained programming. We also design a spectrum of rough simulations for computing uncertain functions arising in the area of rough programming. To speed up the process of handling uncertain functions, we train a neural network to approximate uncertain functions. Finally, we integrate rough simulation, neural network, and cultural algorithm to produce a more powerful and effective hybrid intelligent algorithm for solving rough programming models and illustrate its effectiveness by example of production planning.
{"title":"Rough Programming and Its Application to Production Planning","authors":"Peng Lv, Peng Chang","doi":"10.1109/ICRMEM.2008.8","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.8","url":null,"abstract":"By rough programming, we mean the optimization theory dealing with rough decision problems. This paper constructs a general framework of rough chance-constrained programming. We also design a spectrum of rough simulations for computing uncertain functions arising in the area of rough programming. To speed up the process of handling uncertain functions, we train a neural network to approximate uncertain functions. Finally, we integrate rough simulation, neural network, and cultural algorithm to produce a more powerful and effective hybrid intelligent algorithm for solving rough programming models and illustrate its effectiveness by example of production planning.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"2 6 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":"123798991","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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