基于量子逻辑的模糊线性时序逻辑的模型检测

模型检测的主要问题是处理具有量化信息的系统的验证问题,该文在量子逻辑和时序逻辑的相关理论基础上引入了可能性理论以及模糊线性时间的可能性测度,研究了线性时序逻辑在模糊时态下的模型检测方法.通过引入可能性理论以及广义可能性Kripke结构定义,对广义可能性Kripke结构中的模糊线性时间性质进行了讨论,有利于研究不确定现象...     

基于模糊随机变量的结构可靠性分析

研究了广义应力和广义强度同时具有模糊性和随机性时的结构可靠度计算问题,基于模糊随机变量和模糊随机事件的理论,建立了结构模糊可靠度的计算模型.最后通过一算例,验证了该方法的有效性和合理性.     

结构的失效可能度及模糊概率计算方法

依据模糊可能性理论,系统地建立含模糊变量时结构的可靠性计算模型。旨在解决模糊结构、模糊-随机结构和模糊状态假设下结构的可靠性计算问题。所建模型可给出模糊结构失效的可能度和模糊-随机结构失效概率的可能性分布。研究表明:对同时含模糊变量和随机变量的混合可靠性计算问题,把失效概率(或可靠度)作为模糊变量,能更客观地反映系统的安全状况。算例分析说明了文中方法的合理性和有效性。     

基于LR模糊数的广义模糊DEA模型及其求解

在广义DEA模型基础上,建立基于LR模糊数的广义模糊DEA模型.通过引入LR模糊数的加权平均值,计算了待评价决策单元能体现决策者偏好的广义模糊效率和平均广义模糊效率,对待评价决策单元进行有效性排序.最后通过实例分析,表明了该模型的实用性.     

与年龄相关的模糊随机种群扩散系统的指数稳定性

讨论了一类与年龄相关的模糊随机种群扩散系统,系统受两种不确定性因素的影响,即随机和模糊.在有界和Lipschitz条件下,利用Ito公式和Gronwall引理,建立了均方意义下与年龄相关的模糊随机种群扩散系统指数稳定性的判定准则并通过数值例子对所给出的结论进行了验证.     

广义模糊熵及其诱导的区别度

广义模糊熵是模糊熵在广义模糊补意义下的推广,本文给出广义模糊熵的公理化定义 ,基于σ-广义模糊熵,讨论了广义模糊熵和区别度的相互诱导关系.     

直觉模糊命题逻辑的广义拟重言式及其分类

通过定义一个蕴涵算子,建立一个直觉模糊命题逻辑系统(I20, ,V,→T),讨论了系统I20上的广义拟重言式的分类,将王国俊教授的广义重言式理论从一维推广到二维的直觉模糊命题逻辑上.     

与年龄相关的模糊随机种群扩散系统的均方散逸性

讨论了一类与年龄相关的模糊随机种群扩散系统,该系统受随机和模糊两种不确定性因素的影响.在有界的条件(弱于线性增长条件)和Lipschitz条件下,利用It公式和Bellman-Gronwall-Type引理,建立了均方意义下与年龄相关的模糊随机种群扩散系统均方散逸性的判定准则.并通过数值例子对所给出的结论进行了验证.     

考虑动态相依性的可靠性系统随机Copula模型及其参数估计

动态变化是现代复杂工程系统的典型特征,动态相依系统可靠性理论能更好地揭示系统在工作阶段复杂的状态的性能,动态相依系统成为可靠性研究领域的热点和难点。本文基于随机Copula模型研究了可靠性系统在动态相依下的可靠性,介绍了随机Copula模型,基于极大似然理论的参数估计方法,给出了基于随机Copula模型的串联与并联系统在动态相依下的可靠度计算方法,并对独立系统、静态相依系统及动态相依系统的可靠度进行了比较分析,最后给出了数值算例。仿真算例的结果,验证了本文方法的可行性和有效性。     

一个供应链系统的可靠性模型的适定性分析

供应链系统是一个复杂的动态系统,许多影响因素的存在,使得供应链系统具有强烈的随机性,从而直接影响供应链系统的可靠性,我们通过分析供应链系统的状态之间的转移关系,引入增补变量法,用偏微分方程组建立了供应链系统的可靠性模型,并对该模型系统解的存在唯一性进行了讨论和证明.     

Interval optimization based line sampling method for fuzzy and random reliability analysis

For structural system with fuzzy variables as well as random variables, a novel algorithm for obtaining membership function of fuzzy reliability is presented on interval optimization based Line Sampling (LS) method. In the presented algorithm, the value domain of the fuzzy variables under the given membership level is firstly obtained according to their membership functions. Then, in the value domain of the fuzzy variables, bounds of reliability of the structure are obtained by the nesting analysis of the interval optimization, which is performed by modern heuristic methods, and reliability analysis, which is achieved by the LS method in the reduced space of the random variables. In this way the uncertainties of the input variables are propagated to the safety measurement of the structure, and the membership function of the fuzzy reliability is obtained. The presented algorithm not only inherits the advantage of the direct Monte Carlo method in propagating and distinguishing the fuzzy and random uncertainties, but also can improve the computational efficiency tremendously in case of acceptable precision. Several examples are used to illustrate the advantages of the presented algorithm.     

A Multi-Objective Production Inventory Model with Backorder for Fuzzy Random Demand Under Flexibility and Reliability

In this paper, an Economic Production Quantity (EPQ) model is developed with flexibility and reliability consideration of production process in an imprecise and uncertain mixed environment. The model has incorporated fuzzy random demand, an imprecise production preparation time and shortage. Here, the setup cost and the reliability of the production process along with the backorder replenishment time and production run period are the decision variables. Due to fuzzy-randomness of the demand, expected average demand is a fuzzy quantity and also imprecise preparation time is represented by fuzzy number. Therefore, both are first transformed to a corresponding interval number and then using the interval arithmetic, the single objective function for expected profit over the time cycle is changed to respective multi-objective functions. Due to highly nonlinearity of the expected profit functions it is optimized using a multi-objective genetic algorithm (MOGA). The associated profit maximization problem is illustrated by numerical examples and also its sensitivity analysis is carried out.     

Modelling redundancy allocation for a fuzzy random parallel-series system

Due to subjective judgment, imprecise human knowledge and perception in capturing statistical data, the real data of lifetimes in many systems are both random and fuzzy in nature. Based on the fuzzy random variables that are used to characterize the lifetimes, this paper studies the redundancy allocation problems to a fuzzy random parallel-series system.Two fuzzy random redundancy allocation models (FR-RAM) are developed through reliability maximization and cost minimization, respectively. Some properties of the FR-RAM are obtained, in which an analytical formula of reliability with convex lifetimes is derived and the sensitivity of the reliability is discussed. To solve the FR-RAMs, we first address the computation of reliability. A random simulation method based on the derived analytical formula is proposed to compute the reliability with convex lifetimes. As for the reliability with nonconvex lifetimes, the technique of fuzzy random simulation together with the discretization method of fuzzy random variable is employed to compute the reliability, and a convergence theorem of the fuzzy random simulation is proved. Subsequently, we integrate the computation approaches of the reliability and genetic algorithm (GA) to search for the approximately optimal redundancy allocation of the models. Finally, some numerical examples are provided to illustrate the feasibility of the solution algorithm and quantify its effectiveness.     

Fuzzy mixture two warehouse inventory model involving fuzzy random variable lead time demand and fuzzy total demand

This paper considers a two-warehouse fuzzy-stochastic mixture inventory model involving variable lead time with backorders fully backlogged. The model is considered for two cases—without and with budget constraint. Here, lead-time demand is considered as a fuzzy random variable and the total cost is obtained in the fuzzy sense. The total demand is again represented by a triangular fuzzy number and the fuzzy total cost is derived. By using the centroid method of defuzzification, the total cost is estimated. For the case with fuzzy-stochastic budget constraint, surprise function is used to convert the constrained problem to a corresponding unconstrained problem in pessimistic sense. The crisp optimization problem is solved using Generalized Reduced Gradient method. The optimal solutions for order quantity and lead time are found in both cases for the models with fuzzy-stochastic/stochastic lead time and the corresponding minimum value of the total cost in all cases are obtained. Numerical examples are provided to illustrate the models and results in both cases are compared.     

Fuzzy periodic review system with fuzzy random variable demand

In this paper, a periodic review inventory system has been analyzed in a mixed imprecise and uncertain environment where fuzziness and randomness appear simultaneously. A model has been developed with customer demand assumed to be a fuzzy random variable. The lead-time has been assumed to be a constant. The lead-time demand and the lead-time plus one period’s demand have also been assumed to be fuzzy random variables. A methodology has been developed to determine the optimal inventory level and the optimal period of review such that the total expected annual cost in the fuzzy sense is minimized. A numerical example has been presented to illustrate the model.     

可靠性分析中的模糊信息处理方法

在可靠性分析中,除了常见的随机不确定信息,还普遍存在着模糊不确定信息,因此在传统可靠性的基础上基于模糊理论提出了一种综合处理随机不确定信息与模糊不确定信息的可靠度计算方法。并在应力—强度干涉模型的基础上得出了未被大家重视的可靠度分布曲线,获得了能同时处理概率密度函数与隶属函数的可靠度计算模型,并探索了此模型与模糊可靠性、经典可靠性之间的内在联系。研究结果表明:在随机性与模糊性并存的情况下,此可靠性度量模型能够较好的处理随机信息与模糊信息,所得结果有很高的准确性,并且能够动态的反映可靠度的计算机理。     

Novel reliability-based optimization method for thermal structure with hybrid random,interval and fuzzy parameters

In this paper, novel reliability-based optimization model and method are proposed for thermal structure design with random, interval and fuzzy uncertainties in material properties, external loads and boundary conditions. Random variables are used to quantify the probabilistic uncertainty with sufficient sample data; whereas, interval variables and fuzzy variables are adopted to model the non-probabilistic uncertainty associated with objective limited information and subjective expert opinions, respectively. Using the interval ranking strategy, the level-cut limit state function is precisely quantified to represent the safety state. The eventual safety possibility is derived based on multiple integral, where the cut levels of different fuzzy variables are considered to be independent. Then a hybrid reliability-based optimization model is established with considerable computational cost caused by three-layer nested loop. To improve the computational efficiency, a subinterval vertex method is presented to replace the inner-loop and middle-loop. Comparing numerical results with traditional reliability model, a mono-objective example and a multi-objective example are provided to demonstrate the feasibility of proposed method for hybrid reliability analysis and optimization in practical engineering.     

A multi-objective wholesaler–retailers inventory-distribution model with controllable lead-time based on probabilistic fuzzy set and triangular fuzzy number

This paper develops a single wholesaler and multi retailers mixture inventory distribution model for a single item involving controllable lead-time with backorder and lost sales. The retailers purchase their items from the wholesaler in lots at some intervals throughout the year to meet the customers’ demand. Not to loose the demands, the retailers offer a price discount to the customers on the stock-out items. Here, it is assumed that the lead-time demands of retailers are uncertain in both stochastic and fuzzy sense, i.e., these are simultaneously random and imprecise. To implement this behavior of the lead-time demands, at first, these demands are assumed to be random, say following a normal distribution. With these random demands, the expected total cost for each retailer is obtained. Now, the mean lead-time demands (which are crisp ones) of the retailers are fuzzified. This fuzzy nature of the lead-time demands implies that the annual average demands of the retailers must be fuzzy numbers, suppose these are triangular fuzzy numbers. Using signed distance technique for defuzzification, the estimate of total costs for each retailer is derived. Therefore, the problem is reduced to optimize the crisp annual costs of wholesaler and retailers separately. The multi-objective model is solved using Global Criteria method. Numerical illustrations have been made with the help of an example taking two retailers into consideration. Mathematical analyses have been made for global pareto-optimal solutions of the multi-objective optimization problem. Sensitivity analyses have been made on backorder ratio and pareto-optimal solutions for wholesaler and different retailers are compared graphically.     

Random fuzzy renewal process

This paper attempts to discuss a random fuzzy renewal process based on random fuzzy theory. The interarrival times are characterized as nonnegative random fuzzy variables which is a more reasonable consideration in the real world. Under this setting, the rate of the random fuzzy renewal process is discussed and a random fuzzy elementary renewal theorem is presented. Furthermore, the expected value of renewals in an arbitrary interval is investigated and Blackwell’s theorem in random fuzzy sense is also established.     

Fuzzy Bayesian system reliability assessment based on exponential distribution

The Bayesian system reliability assessment under fuzzy environments is proposed in this paper. In order to apply the Bayesian approach, the fuzzy parameters are assumed as fuzzy random variables with fuzzy prior distributions. The (conventional) Bayesian estimation method will be used to create the fuzzy Bayes point estimator of system reliability based on Exponential distribution by invoking the well-known theorem called “Resolution Identity” in fuzzy sets theory. On the other hand, we also provide the computational procedures to evaluate the membership degree of any given Bayes point estimate of system reliability. In order to achieve this purpose, we transform the original problem into a nonlinear programming problem. This nonlinear programming problem is then divided into four subproblems for the purpose of simplifying computation. Finally, the subproblems can be solved by using any commercial optimizers, e.g., GAMS or LINGO (LINDO).