含弹性约束的多目标模糊线性规划求解

本文讨论了一类含弹性约束的多目标模糊线性规划问题.利用模糊结构元方法引入模糊数的加权特征数概念和序关系,应用Verdegay的模糊线性规划方法及模糊数的加权特征数将此类多目标模糊线性规划问题转化成一类含参数约束条件的清晰多目标线性规划模型,并应用一种基于线性加权函数的规划算法求其α-拟最优可行解.最后,给出了一个数值实例来说明如何求解此类多目标模糊线性规划问题.     

含直觉模糊弹性约束的多目标模糊线性规划求解

基于模糊结构元方法构建并讨论了一类含有直觉模糊弹性约束的多目标模糊线性规划问题.通过引入模糊数的加权特征数,定义了一种序关系并拓展了Verdegay的模糊线性规划方法,将上述多目标模糊线性规划问题转化成两个等价含参数约束条件的清晰多目标线性规划模型,并应用一种线性加权函数法给出了此类线性规划模型的对比最优可行解.最后通过一个数值实例来说明此类问题的一般求解方法.     

全系数模糊的广义模糊变量线性规划

构建并讨论了一类约束条件与目标函数均含模糊系数的广义模糊变量线性规划问题.首先,简单介绍了模糊结构元理论并基于模糊结构元理论定义了表征模糊数面积信息的散度指标.其次,兼顾距离与面积信息,给出了三角模糊数与拟三角模糊数比较排序的新方法,将全系数模糊的广义模糊变量线性规划转化为普通的多目标线性规划.最后,借鉴分层规划的思想,结合模糊数本身性质,给出了此类问题的一种简化求解方法.     

模糊相似关系传递核获取算法

针对模糊相似关系传递核的获取问题进行研究.首先给出模糊相似关系传递核的一些基本性质.之后,利用这些性质构造了三个算法来获取可能为传递核的模糊等价关系.最后,通过实验比较并分析这三种算法在获取传递核时的能力.     

一类系数为梯形模糊数的两层线性规划

讨论了一类系数为梯形模糊数的两层线性规划问题,首先是利用模糊结构元理论将梯形模糊数去模糊化,将其转化成常规的两层线性问题,并验证其去模糊化后的常规的两层线性规划的最优解与系数为梯形模糊数的两层线性规划问题的最优解一致,并给出具体的算法,数例进行验证.     

基于模糊收益率的组合投资模型

本文考虑了收益率为模糊数的投资组合选择问题,利用模型约束简化方差约束,建立了投资组合选择的模糊线性规划模型,然后引进模糊期望把模糊线性规划问题化为普通参数线性规划问题,最后给出了一个数值算例.     

含直觉模糊弹性约束的广义模糊变量线性规划

本文基于模糊结构元方法建立并讨论了一类含有直觉模糊弹性约束的广义模糊变量线性 规划问题。首先,简单介绍了结构元方法并对结构元加权排序中权函数表征决策者风险态度进行了深入分析。然后,通过选取风险中立型决策态度来定义序关系并拓展Verdegay模糊线性规划方法,将新型模糊变量线性规划问题转化为两个含一般模糊弹性约束的模糊变量线性规划模型,给出了此类规划最优直觉模糊解的求法。最后,通过数值算例进一步说明该方法的有效性。     

全系数模糊型线性规划的偏差法

讨论并研究了全系数模糊型线性规划问题的一种新的求解方法.利用新定义的模糊数序关系以及考虑到目标值与决策值之间的关系,可将全系数模糊线性规划问题转换成一个新的普通的线性规划问题,进而可求出目标函数值.     

带模糊约束的线性规划的几点注记

本文证明了带模糊约束的线性规划的对偶定理，利用参数规划给出了带模糊约束的线性规划的一种简单解法，给出了模糊判决为0．5的一个充分条件及相应的解法.     

求解具有max-t-norm合成算子的模糊关系不等式

研究一类具有max-t-norm合成算子的模糊关系不等式,该问题是模糊关系方程以及区间值模糊关系方程的推广.通过分析其解集的特点,提出一个基于筛选原则求解该类问题的算法,并给出数值算例说明该算法的有效性.     

A Note on Fuzzy Relation Programming Problems with Max-Strict-t-Norm Composition

The fuzzy relation programming problem is a minimization problem with a linear objective function subject to fuzzy relation equations using certain algebraic compositions. Previously, Guu and Wu considered a fuzzy relation programming problem with max-product composition and provided a necessary condition for an optimal solution in terms of the maximum solution derived from the fuzzy relation equations. To be more precise, for an optimal solution, each of its components is either 0 or the corresponding component's value of the maximum solution. In this paper, we extend this useful property for fuzzy relation programming problem with max-strict-t-norm composition and present it as a supplemental note of our previous work.     

一类直觉模糊关系线性规划的解

提出了一类目标函数为线性函数,约束是直觉模糊关系方程的最优化问题.这是一类非凸非光滑最优化问题,基于可行域的结构,给出了求全局最优解和最优值的一个算法,最后通过数值例子验证了算法的可行性.     

Minimizing a Linear Objective Function with Fuzzy Relation Equation Constraints

An minimization problem with a linear objective function subject to fuzzy relation equations using max-product composition has been considered by Loetamonphong and Fang. They first reduced the problem by exploring the special structure of the problem and then proposed a branch-and-bound method to solve this 0-1 integer programming problem. In this paper, we provide a necessary condition for an optimal solution of the minimization problems in terms of one maximum solution derived from the fuzzy relation equations. This necessary condition enables us to derive efficient procedures for solving such optimization problems. Numerical examples are provided to illustrate our procedures.     

综合型模糊线性规划分析

模糊线性规划问题是模糊数学规划的研究基础，已经有许多学在这一领域取得了卓有成效的研究成果。但这些研究都是针对特定类型的模糊线性规划开展的，而没有将模糊线性规划放在一般环境下进行综合考虑。本对模糊线性规划的一般模型进行了分析，提出了综合型模糊线性规划问题的求解方法。     

Derivation of intuitionistic fuzzy weights based on intuitionistic fuzzy preference relations

This paper proposes linear goal programming models for deriving intuitionistic fuzzy weights from intuitionistic fuzzy preference relations. Novel definitions are put forward to define additive consistency and weak transitivity for intuitionistic fuzzy preference relations, followed by a study of their corresponding properties. For any given normalized intuitionistic fuzzy weight vector, a transformation formula is furnished to convert the weights into a consistent intuitionistic fuzzy preference relation. For any intuitionistic fuzzy preference relation, a linear goal programming model is developed to obtain its intuitionistic fuzzy weights by minimizing its deviation from the converted consistent intuitionistic fuzzy preference relation. This approach is then extended to group decision-making situations. Three numerical examples are provided to illustrate the validity and applicability of the proposed models.     

Note on “Some models for deriving the priority weights from interval fuzzy preference relations”

Deriving accurate interval weights from interval fuzzy preference relations is key to successfully solving decision making problems. Xu and Chen (2008) proposed a number of linear programming models to derive interval weights, but the definitions for the additive consistent interval fuzzy preference relation and the linear programming model still need to be improved. In this paper, a numerical example is given to show how these definitions and models can be improved to increase accuracy. A new additive consistency definition for interval fuzzy preference relations is proposed and novel linear programming models are established to demonstrate the generation of interval weights from an interval fuzzy preference relation.     

约束条件为加法-取小模糊关系系统的带权min-max规划

定义了目标函数为带权重的min-max规划,约束条件为加法-取小模糊关系不等式系统,研究此模糊关系规划问题的求解思路和方法。当此模糊关系系统相容时,给出了一个求解此模糊关系规划问题的新算法和例子。     

An Algorithm for Solving Optimization Problems with One Linear Objective Function and Finitely Many Constraints of Fuzzy Relation Inequalities

An optimization model with one linear objective function and fuzzy relation equation constraints was presented by Fang and Li (1999) as well as an efficient solution procedure was designed by them for solving such a problem. A more general case of the problem, an optimization model with one linear objective function and finitely many constraints of fuzzy relation inequalities, is investigated in this paper. A new approach for solving this problem is proposed based on a necessary condition of optimality given in the paper. Compared with the known methods, the proposed algorithm shrinks the searching region and hence obtains an optimal solution fast. For some special cases, the proposed algorithm reaches an optimal solution very fast since there is only one minimum solution in the shrunk searching region. At the end of the paper, two numerical examples are given to illustrate this difference between the proposed algorithm and the known ones.     

模糊关系下的半环的广义模糊双理想

给出了半环的广义模糊双理想的概念并讨论了模糊关系下的半环的广义模糊双理想的一些代数性质.