As an alternative or complementary approach to the classical probability theory,the ability of the evidence theory in uncertainty quantification(UQ) analyses is subject of intense research in recent years.Two state-...As an alternative or complementary approach to the classical probability theory,the ability of the evidence theory in uncertainty quantification(UQ) analyses is subject of intense research in recent years.Two state-of-the-art numerical methods,the vertex method and the sampling method,are commonly used to calculate the resulting uncertainty based on the evidence theory.The vertex method is very effective for the monotonous system,but not for the non-monotonous one due to its high computational errors.The sampling method is applicable for both systems.But it always requires a high computational cost in UQ analyses,which makes it inefficient in most complex engineering systems.In this work,a computational intelligence approach is developed to reduce the computational cost and improve the practical utility of the evidence theory in UQ analyses.The method is demonstrated on two challenging problems proposed by Sandia National Laboratory.Simulation results show that the computational efficiency of the proposed method outperforms both the vertex method and the sampling method without decreasing the degree of accuracy.Especially,when the numbers of uncertain parameters and focal elements are large,and the system model is non-monotonic,the computational cost is five times less than that of the sampling method.展开更多
This study focuses on investigating the optimal investment strategy for an optimization problem with delay using the uncertainty theory. The financial market is composed of a risk-free asset and a risk asset with an u...This study focuses on investigating the optimal investment strategy for an optimization problem with delay using the uncertainty theory. The financial market is composed of a risk-free asset and a risk asset with an uncertain price process described by an uncertain differential equation. An optimization problem is assumed that its objective is a nonlinear function of decision variable. By deriving the equation of optimality, an analytical solution is obtained for the optimal delay investment strategy, and the optimal delay value function. Finally, an economic analysis and numerical sensitivity analysis are conducted to evaluate the research results.展开更多
Cognitive Reliability and Error Analysis Method(CREAM)is widely used in human reliability analysis(HRA).It defines nine common performance conditions(CPCs),which represent the factors thatmay affect human reliability ...Cognitive Reliability and Error Analysis Method(CREAM)is widely used in human reliability analysis(HRA).It defines nine common performance conditions(CPCs),which represent the factors thatmay affect human reliability and are used to modify the cognitive failure probability(CFP).However,the levels of CPCs are usually determined by domain experts,whichmay be subjective and uncertain.What’smore,the classicCREAMassumes that the CPCs are independent,which is unrealistic.Ignoring the dependence among CPCs will result in repeated calculations of the influence of the CPCs on CFP and lead to unreasonable reliability evaluation.To address the issue of uncertain information modeling and processing,this paper introduces evidence theory to evaluate the CPC levels in specific scenarios.To address the issue of dependence modeling,the Decision-Making Trial and Evaluation Laboratory(DEMATEL)method is used to process the dependence among CPCs and calculate the relative weights of each CPC,thus modifying the multiplier of the CPCs.The detailed process of the proposed method is illustrated in this paper and the CFP estimated by the proposed method is more reasonable.展开更多
Based on the uncertainty theory, market demand information updating as the background, we study the coordination and optimization problem of three-stage supply chain in this paper. In half a asymmetric market informat...Based on the uncertainty theory, market demand information updating as the background, we study the coordination and optimization problem of three-stage supply chain in this paper. In half a asymmetric market information, participants are risk neutral;under the situation of the manufacturers and wholesalers having twice pre-season decision-making opportunity, wholesalers can be replenished in the season;manufacturers join the lowest supply contract of commitment: manufacturers for exchanging the information that they cannot get directly from the market will promise wholesalers to have a season lowest supply in pre-season. According to this contract, we establish optimization models of manufacturers and wholesalers respectively, and get the optimal strategy of supply chain members by analyzing the supply chain system. Finally, by giving a numerical example and comparing the results with that under random circumstances, the result is reasonable.展开更多
This paper introduces a novice solution methodology for multi-objective optimization problems having the coefficients in the form of uncertain variables. The embedding theorem, which establishes that the set of uncert...This paper introduces a novice solution methodology for multi-objective optimization problems having the coefficients in the form of uncertain variables. The embedding theorem, which establishes that the set of uncertain variables can be embedded into the Banach space C[0, 1] × C[0, 1] isometrically and isomorphically, is developed. Based on this embedding theorem, each objective with uncertain coefficients can be transformed into two objectives with crisp coefficients. The solution of the original m-objectives optimization problem with uncertain coefficients will be obtained by solving the corresponding 2 m-objectives crisp optimization problem. The R & D project portfolio decision deals with future events and opportunities, much of the information required to make portfolio decisions is uncertain. Here parameters like outcome, risk, and cost are considered as uncertain variables and an uncertain bi-objective optimization problem with some useful constraints is developed. The corresponding crisp tetra-objective optimization model is then developed by embedding theorem. The feasibility and effectiveness of the proposed method is verified by a real case study with the consideration that the uncertain variables are triangular in nature.展开更多
From functions,ornament and art,on the basis of the behavioral theory,the utility of urban public facilities was surveyed and studied with Longhua District of Haikou City as an example.It summed up the basis for desig...From functions,ornament and art,on the basis of the behavioral theory,the utility of urban public facilities was surveyed and studied with Longhua District of Haikou City as an example.It summed up the basis for designing urban public facilities behind behavior habits of residents,in the hope of making future urban construction and management more humanized.Accordingly,it is expected to set up appropriate concept of public facilities,and play especially important role in creating favorable urban living environment.展开更多
Structural reliability is an important method to measure the safety performance of structures under the influence of uncertain factors.Traditional structural reliability analysis methods often convert the limit state ...Structural reliability is an important method to measure the safety performance of structures under the influence of uncertain factors.Traditional structural reliability analysis methods often convert the limit state function to the polynomial form to measure whether the structure is invalid.The uncertain parameters mainly exist in the form of intervals.This method requires a lot of calculation and is often difficult to achieve efficiently.In order to solve this problem,this paper proposes an interval variable multivariate polynomial algorithm based on Bernstein polynomials and evidence theory to solve the structural reliability problem with cognitive uncertainty.Based on the non-probabilistic reliability index method,the extreme value of the limit state function is obtained using the properties of Bernstein polynomials,thus avoiding the need for a lot of sampling to solve the reliability analysis problem.The method is applied to numerical examples and engineering applications such as experiments,and the results show that the method has higher computational efficiency and accuracy than the traditional linear approximation method,especially for some reliability problems with higher nonlinearity.Moreover,this method can effectively improve the reliability of results and reduce the cost of calculation in practical engineering problems.展开更多
微电网的能量管理与优化调度作为构建新型电力系统的重要环节,提高其可再生能源的消纳水平、降低源荷不确定性风险以及优化系统运行成本具有重要意义。因此,文中提出一种基于信息间隙决策理论(information gap decision theory,IGDT)的...微电网的能量管理与优化调度作为构建新型电力系统的重要环节,提高其可再生能源的消纳水平、降低源荷不确定性风险以及优化系统运行成本具有重要意义。因此,文中提出一种基于信息间隙决策理论(information gap decision theory,IGDT)的含广义储能的独立直流微电网日前优化调度模型。首先,构建含超级电容的混合储能系统,以降低蓄电池运行成本,将具备虚拟储能特性的柔性负荷与混合储能相结合,形成广义储能,充分发挥微电网系统内灵活性资源特性;其次,考虑系统风光荷不确定性,引入IGDT模型,在确定性模型基础上建立风险规避策略下的鲁棒模型和风险投机策略下的机会模型,从2种决策角度追求降低风险与最大化收益;最后,基于算例仿真分析,证明该调度策略在降低微电网运行成本的基础上可量化不确定性因素对系统调度决策的影响,验证了模型的有效性和可参考性。展开更多
目的:构建适用于不确定环境下的应急物资分级库存模型,以为传染病暴发后的应急响应提供更准确和有效的决策支持。方法:首先,使用“之”字形不确定分布构建应急物资分级模型;其次,将经济订购量(economic order quantity,EOQ)模型与应急...目的:构建适用于不确定环境下的应急物资分级库存模型,以为传染病暴发后的应急响应提供更准确和有效的决策支持。方法:首先,使用“之”字形不确定分布构建应急物资分级模型;其次,将经济订购量(economic order quantity,EOQ)模型与应急物资分级模型相结合构建应急物资分级库存模型;最后,以W市某次急性呼吸道传染病事件为例,验证提出的应急物资分级库存模型的实用性和有效性。结果:该模型在大部分情况下能够准确地对应急物资进行分级,且以该模型计算得到的库存量十分接近需求量。结论:建立的应急物资分级库存模型在急性呼吸道传染病的应急响应中具有实用性和有效性,可以帮助决策者更准确地预测应急物资的需求,提高应急响应的能力和效率。展开更多
基于精确Zoeppritz方程的叠前地震反演方法在面向低信噪比地震资料的应用时仍然存在较大挑战。马尔科夫链蒙特卡洛(Markov chain Monte Carlo, MCMC)模拟是一种启发式的全局寻优算法,是实现叠前弹性参数非线性反演的有效途径。常规基于M...基于精确Zoeppritz方程的叠前地震反演方法在面向低信噪比地震资料的应用时仍然存在较大挑战。马尔科夫链蒙特卡洛(Markov chain Monte Carlo, MCMC)模拟是一种启发式的全局寻优算法,是实现叠前弹性参数非线性反演的有效途径。常规基于MCMC算法的叠前反演采用高斯分布来刻画弹性参数的统计特征,在应用于复杂岩性储层时有较大的局限性。同时,由于地下模型参数空间巨大以及地震数据中噪声等因素的影响,MCMC对弹性参数后验概率分布的搜索过程极易受到局部极值的影响,这使得基于MCMC的叠前反演较难获得稳定、准确的结果。本文针对实际复杂储层及低信噪比地震资料条件下基于精确Zoeppritz方程的叠前反演问题,提出了一种改进的MCMC弹性参数反演方法。该方法首先利用低频模型约束,将待反演参数转换为模型参数的扰动量,从而降低后验概率分布的复杂度;其次,通过对似然函数取对数,并利用低频模型来约束地震正演过程;最后,利用基于随机子空间采样的多链算法对叠前非线性反演问题进行全局寻优,以避免采样过程过早地收敛到局部极值。低信噪比模拟数据和实际数据的测试表明,本文所提方法能够获得更加准确、稳定的弹性参数反演结果,并且能够对反演结果给出可信、定量的不确定性估计。展开更多
为高效处理综合能源系统IES(integrated energy system)中因热电供需矛盾导致的弃风及碳排放问题,构建了考虑碳捕集与封存CCS(carbon capture and storage)技术以及光热CSP(concentrating solar power)电站的优化调度模型。首先,利用CC...为高效处理综合能源系统IES(integrated energy system)中因热电供需矛盾导致的弃风及碳排放问题,构建了考虑碳捕集与封存CCS(carbon capture and storage)技术以及光热CSP(concentrating solar power)电站的优化调度模型。首先,利用CCS技术对热电联产CHP(combined heat and power)机组进行低碳化改造,建立碳捕集热电联产机组的数学模型;然后,在此基础上引入CSP电站,构成CSP-CHP-CCS协同框架,并建立含CSP-CHPCCS的IES低碳经济调度模型;接着,针对系统中的源、荷不确定性,采用信息间隙决策理论进行模拟分析,构建风险规避鲁棒模型;最后,通过算例仿真对比,验证了所提模型在促进新能源消纳和降低碳排放方面的有效性。展开更多
基金supported by the Advanced Research of National Defense Foundation of China(426010501)
文摘As an alternative or complementary approach to the classical probability theory,the ability of the evidence theory in uncertainty quantification(UQ) analyses is subject of intense research in recent years.Two state-of-the-art numerical methods,the vertex method and the sampling method,are commonly used to calculate the resulting uncertainty based on the evidence theory.The vertex method is very effective for the monotonous system,but not for the non-monotonous one due to its high computational errors.The sampling method is applicable for both systems.But it always requires a high computational cost in UQ analyses,which makes it inefficient in most complex engineering systems.In this work,a computational intelligence approach is developed to reduce the computational cost and improve the practical utility of the evidence theory in UQ analyses.The method is demonstrated on two challenging problems proposed by Sandia National Laboratory.Simulation results show that the computational efficiency of the proposed method outperforms both the vertex method and the sampling method without decreasing the degree of accuracy.Especially,when the numbers of uncertain parameters and focal elements are large,and the system model is non-monotonic,the computational cost is five times less than that of the sampling method.
文摘This study focuses on investigating the optimal investment strategy for an optimization problem with delay using the uncertainty theory. The financial market is composed of a risk-free asset and a risk asset with an uncertain price process described by an uncertain differential equation. An optimization problem is assumed that its objective is a nonlinear function of decision variable. By deriving the equation of optimality, an analytical solution is obtained for the optimal delay investment strategy, and the optimal delay value function. Finally, an economic analysis and numerical sensitivity analysis are conducted to evaluate the research results.
基金Shanghai Rising-Star Program(Grant No.21QA1403400)Shanghai Sailing Program(Grant No.20YF1414800)Shanghai Key Laboratory of Power Station Automation Technology(Grant No.13DZ2273800).
文摘Cognitive Reliability and Error Analysis Method(CREAM)is widely used in human reliability analysis(HRA).It defines nine common performance conditions(CPCs),which represent the factors thatmay affect human reliability and are used to modify the cognitive failure probability(CFP).However,the levels of CPCs are usually determined by domain experts,whichmay be subjective and uncertain.What’smore,the classicCREAMassumes that the CPCs are independent,which is unrealistic.Ignoring the dependence among CPCs will result in repeated calculations of the influence of the CPCs on CFP and lead to unreasonable reliability evaluation.To address the issue of uncertain information modeling and processing,this paper introduces evidence theory to evaluate the CPC levels in specific scenarios.To address the issue of dependence modeling,the Decision-Making Trial and Evaluation Laboratory(DEMATEL)method is used to process the dependence among CPCs and calculate the relative weights of each CPC,thus modifying the multiplier of the CPCs.The detailed process of the proposed method is illustrated in this paper and the CFP estimated by the proposed method is more reasonable.
文摘Based on the uncertainty theory, market demand information updating as the background, we study the coordination and optimization problem of three-stage supply chain in this paper. In half a asymmetric market information, participants are risk neutral;under the situation of the manufacturers and wholesalers having twice pre-season decision-making opportunity, wholesalers can be replenished in the season;manufacturers join the lowest supply contract of commitment: manufacturers for exchanging the information that they cannot get directly from the market will promise wholesalers to have a season lowest supply in pre-season. According to this contract, we establish optimization models of manufacturers and wholesalers respectively, and get the optimal strategy of supply chain members by analyzing the supply chain system. Finally, by giving a numerical example and comparing the results with that under random circumstances, the result is reasonable.
文摘This paper introduces a novice solution methodology for multi-objective optimization problems having the coefficients in the form of uncertain variables. The embedding theorem, which establishes that the set of uncertain variables can be embedded into the Banach space C[0, 1] × C[0, 1] isometrically and isomorphically, is developed. Based on this embedding theorem, each objective with uncertain coefficients can be transformed into two objectives with crisp coefficients. The solution of the original m-objectives optimization problem with uncertain coefficients will be obtained by solving the corresponding 2 m-objectives crisp optimization problem. The R & D project portfolio decision deals with future events and opportunities, much of the information required to make portfolio decisions is uncertain. Here parameters like outcome, risk, and cost are considered as uncertain variables and an uncertain bi-objective optimization problem with some useful constraints is developed. The corresponding crisp tetra-objective optimization model is then developed by embedding theorem. The feasibility and effectiveness of the proposed method is verified by a real case study with the consideration that the uncertain variables are triangular in nature.
文摘From functions,ornament and art,on the basis of the behavioral theory,the utility of urban public facilities was surveyed and studied with Longhua District of Haikou City as an example.It summed up the basis for designing urban public facilities behind behavior habits of residents,in the hope of making future urban construction and management more humanized.Accordingly,it is expected to set up appropriate concept of public facilities,and play especially important role in creating favorable urban living environment.
文摘Structural reliability is an important method to measure the safety performance of structures under the influence of uncertain factors.Traditional structural reliability analysis methods often convert the limit state function to the polynomial form to measure whether the structure is invalid.The uncertain parameters mainly exist in the form of intervals.This method requires a lot of calculation and is often difficult to achieve efficiently.In order to solve this problem,this paper proposes an interval variable multivariate polynomial algorithm based on Bernstein polynomials and evidence theory to solve the structural reliability problem with cognitive uncertainty.Based on the non-probabilistic reliability index method,the extreme value of the limit state function is obtained using the properties of Bernstein polynomials,thus avoiding the need for a lot of sampling to solve the reliability analysis problem.The method is applied to numerical examples and engineering applications such as experiments,and the results show that the method has higher computational efficiency and accuracy than the traditional linear approximation method,especially for some reliability problems with higher nonlinearity.Moreover,this method can effectively improve the reliability of results and reduce the cost of calculation in practical engineering problems.
文摘微电网的能量管理与优化调度作为构建新型电力系统的重要环节,提高其可再生能源的消纳水平、降低源荷不确定性风险以及优化系统运行成本具有重要意义。因此,文中提出一种基于信息间隙决策理论(information gap decision theory,IGDT)的含广义储能的独立直流微电网日前优化调度模型。首先,构建含超级电容的混合储能系统,以降低蓄电池运行成本,将具备虚拟储能特性的柔性负荷与混合储能相结合,形成广义储能,充分发挥微电网系统内灵活性资源特性;其次,考虑系统风光荷不确定性,引入IGDT模型,在确定性模型基础上建立风险规避策略下的鲁棒模型和风险投机策略下的机会模型,从2种决策角度追求降低风险与最大化收益;最后,基于算例仿真分析,证明该调度策略在降低微电网运行成本的基础上可量化不确定性因素对系统调度决策的影响,验证了模型的有效性和可参考性。
文摘目的:构建适用于不确定环境下的应急物资分级库存模型,以为传染病暴发后的应急响应提供更准确和有效的决策支持。方法:首先,使用“之”字形不确定分布构建应急物资分级模型;其次,将经济订购量(economic order quantity,EOQ)模型与应急物资分级模型相结合构建应急物资分级库存模型;最后,以W市某次急性呼吸道传染病事件为例,验证提出的应急物资分级库存模型的实用性和有效性。结果:该模型在大部分情况下能够准确地对应急物资进行分级,且以该模型计算得到的库存量十分接近需求量。结论:建立的应急物资分级库存模型在急性呼吸道传染病的应急响应中具有实用性和有效性,可以帮助决策者更准确地预测应急物资的需求,提高应急响应的能力和效率。
文摘基于精确Zoeppritz方程的叠前地震反演方法在面向低信噪比地震资料的应用时仍然存在较大挑战。马尔科夫链蒙特卡洛(Markov chain Monte Carlo, MCMC)模拟是一种启发式的全局寻优算法,是实现叠前弹性参数非线性反演的有效途径。常规基于MCMC算法的叠前反演采用高斯分布来刻画弹性参数的统计特征,在应用于复杂岩性储层时有较大的局限性。同时,由于地下模型参数空间巨大以及地震数据中噪声等因素的影响,MCMC对弹性参数后验概率分布的搜索过程极易受到局部极值的影响,这使得基于MCMC的叠前反演较难获得稳定、准确的结果。本文针对实际复杂储层及低信噪比地震资料条件下基于精确Zoeppritz方程的叠前反演问题,提出了一种改进的MCMC弹性参数反演方法。该方法首先利用低频模型约束,将待反演参数转换为模型参数的扰动量,从而降低后验概率分布的复杂度;其次,通过对似然函数取对数,并利用低频模型来约束地震正演过程;最后,利用基于随机子空间采样的多链算法对叠前非线性反演问题进行全局寻优,以避免采样过程过早地收敛到局部极值。低信噪比模拟数据和实际数据的测试表明,本文所提方法能够获得更加准确、稳定的弹性参数反演结果,并且能够对反演结果给出可信、定量的不确定性估计。
文摘为高效处理综合能源系统IES(integrated energy system)中因热电供需矛盾导致的弃风及碳排放问题,构建了考虑碳捕集与封存CCS(carbon capture and storage)技术以及光热CSP(concentrating solar power)电站的优化调度模型。首先,利用CCS技术对热电联产CHP(combined heat and power)机组进行低碳化改造,建立碳捕集热电联产机组的数学模型;然后,在此基础上引入CSP电站,构成CSP-CHP-CCS协同框架,并建立含CSP-CHPCCS的IES低碳经济调度模型;接着,针对系统中的源、荷不确定性,采用信息间隙决策理论进行模拟分析,构建风险规避鲁棒模型;最后,通过算例仿真对比,验证了所提模型在促进新能源消纳和降低碳排放方面的有效性。