两阶段模糊运输期望值模型及其逼近方法

基于可信性理论和两阶段模糊优化方法,提出一类带有模糊参数的两阶段运输期望值模型.由于提出运输问题包含带有无限支撑的模糊变量系数,因此它是一个无限堆的优化问题.然后,讨论两阶段模糊运输期望值问题的逼近方法并且将逼近方法嵌套到遗传算法中产生一个基于遗传算法的逼近方法求解提出的两阶段模糊运输期望值问题.最后,给出一个数值例子...     

基于可信性理论的生产计划期望值模型

基于可信性理论,提出一类新的模糊生产计划期望值模型.然后,讨论这个模糊生产计划模型的基本性质.最后,利用这个模糊模型的基本性质我们可以把模糊生产计划期望值模型转化为一个线性规划模型并且设计相应的算法求解模糊生产计划问题的一个数值例子.     

2-型供应链网络设计优化

基于模糊可能性理论,研究了2-型模糊供应链网络设计问题。考虑到运输费用和顾客需求的不确定性,我们用2-型模糊变量来刻画,建立了2-型模糊环境下的期望值供应链网络设计模型。当2-型模糊变量相互独立且服从三角分布时,我们将原模型转化为等价的确定模型。等价模型是一个0-1混合整数参数规划,因此可采用Lingo软件求解。最后,我们通过数值例子演示所提建模思想。实验结果证明了所建模型的有效性。     

2-型模糊环境下的能源系统优化

基于模糊可能性理论,建立2-型模糊环境下的能源分配优化模型,其中各种类型能源的成本用2-型模糊变量刻画.用均值简约方法简约2-型模糊成本,建立广义期望值意义下的模糊能源分配优化模型.当成本用相互独立的三角2-型模糊变量刻画时,所建立的模糊能源分配优化模型可以转化为等价的参数线性规划.最后提供一个数值例子表明建模思想.     

含模糊变量的水污染控制系统研究

在水流量为模糊变量且河流中工业污水含量标准给定的条件下,分别建立了水污染控制系统问题的模糊期望值模型和模糊机会约束规划模型来满足不同的优化需求.为了有效求解优化模型,采用了将模糊模拟、神经元网络及遗传算法相结合的混合智能算法.最后用算例进行了验证,结果表明该算法是有效可行的.     

基于遗传算法的随机模糊立体运输问题

随机模糊立体运输问题的研究是为了解决现实生活中双因素不确定性问题,在遗传算法的基础上,运用可信性理论建立随机模糊运输问题的机会约束规划模型.通过算例进行VC++编程模拟计算,验证了此模型的可行性,最终提出了基于遗传算法解决随机模糊立体运输问题的模型.     

一般梯形模糊逼近算子及其在模糊运输问题中的应用

研究运输成本信息为一般模糊数的模糊运输问题.首先,在保持一般模糊数的核不变的条件下,建立一般模糊数与一般梯形模糊数的距离最小优化模型,通过求解模型得到一般模糊数的一般梯形模糊逼近算子,并给出该逼近算子具有的性质如数乘不变性、平移不变性、连续性等.然后利用该逼近算子将一般模糊运输信息表转换成一般梯形模糊运输信息表,再根据已有GFLCM和GFMDM算法得到模糊运输问题的近似最优解,最后给出具体算例分析说明方法的有效性和合理性.     

目标有优先等级的混合线性模糊目标规划的解法及其应用

一、目标有优先等级的混合线性模糊目标规划的解法混合线性模糊同标规划的向量极大化一般模型为(1)对于 i=1,2,…,k,目标 c_i~Tx 具有明确的日标期望值 f_i~0;对于 i=k+1,k+2,…,p,目标c)i~Tx 具有模糊的目标期望值,其上目标为 u_i,下目标为 l_i.各目标的重要程度不同.采用目标规划技术,引进优先等级,可将(1)转化为求解如下模型     

离散模糊需求报童问题的可信性模型研究

基于可信性理论,建立了确定离散模糊需求报童问题订货量的期望成本与利润模型,并与基于可能性理论的质心特征值分析模型进行了比较.数值研究结果表明:1)对应每一模型的最小模糊成本和最大利润的订货量不一致,且模糊期望模型与质心特征值模型确定的订货量不同;2)对应不同订货量,模糊可能性成本、利润之和及期望成本、利润之和均不为固定常数.由于在模糊环境下,与概率测度对应的模糊量描述是可信性测度,所以,相比而言,离散模糊需求报童问题的模糊期望值模型较模糊可能性模型好.     

模糊随机需求的连续盘点存储策略研究

考虑到需求的模糊随机性,建立模糊随机需求情况下连续盘点存储策略的模糊随机成本模型。利用模糊随机变量的期望值理论,推导出了其成本期望值模型的解析表达式,进而给出了最优再订货点所属区间的判别条件以及最优再订货点和经济订货量的计算式;基于此,设计了一模糊随机需求的连续盘点最优存储策略算法。最后结合数值算例,分析了模糊随机需求概率分布及缺货成本对最优存储策略的影响。     

Fractional Goal Programming for Fuzzy Solid Transportation Problem with Interval Cost

In this paper, we study a solid transportation problem with interval cost using fractional goal programming approach (FGP). In real life applications of the FGP problem with multiple objectives, it is difficult for the decision-maker(s) to determine the goal value of each objective precisely as the goal values are imprecise, vague, or uncertain. Therefore, a fuzzy goal programming model is developed for this purpose. The proposed model presents an application of fuzzy goal programming to the solid transportation problem. Also, we use a special type of non-linear (hyperbolic) membership functions to solve multi-objective transportation problem. It gives an optimal compromise solution. The proposed model is illustrated by using an example.     

基于可信性理论的运输机会约束模型

基于可信性理论,将提出一类带有模糊参数的运输计划机会约束模型.然后,讨论可信性函数的逼近方法并且设计一个基于逼近方法、神经网络和遗传算法的启发式算法来求解这个模糊运输计划机会约束模型.最后,给出一个数值例子来表明所设计算法的实用性和有效性.     

Reliable design of a logistics network under uncertainty: A fuzzy possibilistic-queuing model

This paper proposes a bi-objective model for designing a reliable network of bi-directional facilities in logistics network under uncertainties. For this purpose, the model utilizes an effective reliability approach to find a robust logistics network design. The objectives of the model are to minimize the total costs and the expected transportation costs after failures of bi-directional facilities of the logistics network. To solve the model, a new solution approach is proposed by combining queuing theory, fuzzy possibilistic programming and fuzzy multi-objective programming. Finally, the computational experiments are provided to illustrate the effectiveness of the proposed model and solution approach.     

A new fuzzy multi-objective programming: Entropy based geometric programming and its application of transportation problems

In this paper, we introduce a fuzzy mathematical programming with generalized fuzzy number as objective coefficients. We also examine a transportation problem with additional restriction. There is an additional entropy objective function in the transportation problem besides transportation cost objective function. Using new fuzzy mathematical programming, this multi-objective entropy transportation problem with generalized trapezoidal fuzzy number costs has been reduced to a primal geometric programming problem. Pareto optimal solution of the transportation model is found. Numerical examples have been provided to illustrate the problem.     

Decision making under uncertainty with fuzzy targets

This paper discusses the issue of how to use fuzzy targets in the target-based model for decision making under uncertainty. After introducing a target-based interpretation of the expected value on which it is shown that this model implicitly assumes a neutral behavior on attitude about the target, we examine the issue of using fuzzy targets considering different attitudes about the target selection of the decision maker. We also discuss the problem for situations on which the decision maker’s attitude about target may change according to different states of nature. Especially, it is shown that the target-based approach can provide an unified way for solving the problem of fuzzy decision making with uncertainty about the state of nature and imprecision about payoffs. Several numerical examples are given for illustration of the discussed issues.