具有模糊信息的多目标运输问题求解

提出一种求解具有模糊信息的多目标运输问题的方法。利用专家意见通过模糊算法集给从各产地到各目的地运送单位物资的模糊综合指标值,运用一种对模糊数排序的方法,将模糊多目标运输问题转化为单目标的运输问题进行求解,最后给出了一个数值例子。     

基于改进VIKOR方法的地震应急物资运输决策问题研究

地震灾害应急救援物资方案的合理选择对与减少人员伤亡,降低灾民的财产损失具有重要影响。本文针对属性权重未知情形下的地震应急物资运输方案决策问题,提出了一种Pythagorean模糊不确定语言与前景理论相结合的改进VIKOR决策方法,即PFUL-PT-VIKOR法。首先,采用Pythagorean模糊不确定语言用于描述和融合专家对地震应急物资运输方案在考虑多种属性影响下的感知信息;其次,利用主客观融合法对属性权重进行求解;然后,提出基于前景理论的改进VIKOR法并得出方案排序;最后,通过算例分析,对所提出方法的有效性和实践性给予验证。结果表明,PFUL-PT-VIKOR模型有助于增强决策专家对不确定突发情景信息感知的知识表示能力,解决属性赋权过于主观或过分依赖样本的困难,并突围了应急决策者隐性心理行为较难定量应用的思维定势,增强了模型的现实适用性,为地震应急物资是否合理运输提供决策支持。     

模糊值Choquet积分(Ⅱ)--函数关于模糊值模糊测度的Choquet积分

研究一种取值于模糊数集的Choquet积分，该积分的被积函数是单值函数，所用的测度是模糊值模糊测度。给出其定义、性质和收敛定理。     

带模糊时间窗的配送问题多目标优化研究

针对配送多目标优化问题,综合考虑车辆使用数、运输总里程和客户服务水平,基于双层规划的思想,解决了车辆数函数和运输里程函数的区间伸缩指标问题,并引入客户不满意度的模糊隶属度函数来描述配送服务水平。通过去量纲将三个优化目标转化为总目标函数的功效函数,并运用模糊层次分析法对三个函数分配权重,建立以车辆使用数最少、运输总里程最小、客户不满意度最低的标量化多目标模型,并运用模拟退火算法验证了模型的合理性和普适性。     

基于LINGO的物资运输最短时间计算

根据应急物资运输问题时效性强的要求,建立了完成物资运输任务最短时间问题的数学模型,给出了利用二分法进行搜索的LINGO软件求解计算方法,显著减少了计算的迭代次数.实例结果表明,利用LINGO可以实现快速准确的决策,从而为物资运输最短时间决策提供了一种有效的决策方法.     

震后应急物流系统中带时间窗的模糊动态LRP

针对震后应急物流系统中多层次设施定位－运输路线安排问题(LRP),考虑系统中的动态性、时效性、路网连通性、需求不确定性等特点,建立了一个带时间窗的模糊动态LRP优化模型,据此进行救援过程中不同周期灾区外围应急物资集散点和灾区应急配送中心的定位以及应急物资运输路线安排的联合决策。针对该模型的特点,提出了一种基于动态规划的改进遗传算法,为防遗传算法过早收敛问题,使用了随机遍历抽样法、重组策略和变化变异率法,并通过特定实值编码、罚函数法和物资需求量分割策略处理模型中的约束条件。最后,通过算例分析验证了该模型和算法的有效性。     

扩张原理的一种新的表现形式

提出一种基于“模糊点”的模糊集合的分解定理，在此分解定理的基础上给出模糊扩张原理的一种新的表现形式。新的扩张原理与以往人们所提出的扩张原理是等价的，而表现形式更为直观、简便。同时，利用该扩张原理得到模糊复合函数以及多元模糊关系合成运算的合理解释。     

流向受限运输问题的解法及其应用

本文提出一种解流向受限运输问题的算法.首先给出数学模型,利用位势法解普通运输问题的原理,建立了一种改进型位势法解流向受限运输问题.算法包含了解普通运输问题.对算法进行了理论证明后,研制了软件.用软件在微机 IBM-PC/XT 上编制一个全国性的物资调运计划仅用时间2分钟.长期实用证明方法是有效的.该软件已提供物资调运部门使用20个月.     

基于模糊邻域相对依赖互信息的特征选择方法

模糊粗糙集作为处理不确定性信息的有效工具，已广泛应用于特征选择中。然而当数据分布密度差别较大时，传统模糊粗糙近似不能有效度量样本的隶属度，且大多特征评价函数仅从代数或信息单一视角构造。针对以上问题，提出了一种基于模糊邻域相对依赖互信息的特征选择方法。首先，为克服传统模糊粗糙近似对数据分布敏感的缺陷，引入相对距离计算模糊相似关系，同时考虑模糊邻域粒度结构，提出了模糊邻域相对依赖度，从代数观度量数据的不确定性。然后，基于相对粒度结构提出了模糊邻域相对互信息，并与模糊邻域相对依赖度结合构造出一种新的特征评价函数——模糊邻域相对依赖互信息，将代数观和信息观结合进行特征评价。最后，设计了一种基于模糊邻域相对依赖互信息的特征选择算法(FNRDI)。通过与其他算法在9个公共数据集上进行实验对比分析，结果表明所提算法可有效消除冗余特征且提高数据的分类精度。     

典型模糊控制器的插值形式

推导出了单输入单输出、双输入单输出典型Mamdani模糊控制器的插值解析表达式，并推广到输出采用输入变量函数的典型T—S模糊控制器。典型Mamdani模糊控制器的输入采用正规模糊集、三角形全交迭的隶属函数，输出采用单点模糊数。这些插值表达式在一定程度上揭示了典型模糊控制器的本质特征，为设计和优化提供了准确的解析模型，同时也为模糊控制器的实际应用提供了一种快速精确的控制算法。     

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.     

Possibility linear programming with trapezoidal fuzzy numbers

Fuzzy linear programming with trapezoidal fuzzy numbers (TrFNs) is considered and a new method is developed to solve it. In this method, TrFNs are used to capture imprecise or uncertain information for the imprecise objective coefficients and/or the imprecise technological coefficients and/or available resources. The auxiliary multi-objective programming is constructed to solve the corresponding possibility linear programming with TrFNs. The auxiliary multi-objective programming involves four objectives: minimizing the left spread, maximizing the right spread, maximizing the left endpoint of the mode and maximizing the middle point of the mode. Three approaches are proposed to solve the constructed auxiliary multi-objective programming, including optimistic approach, pessimistic approach and linear sum approach based on membership function. An investment example and a transportation problem are presented to demonstrate the implementation process of this method. The comparison analysis shows that the fuzzy linear programming with TrFNs developed in this paper generalizes the possibility linear programming with triangular fuzzy numbers.     

Fuzzy goal programming for multiobjective transportation problems

Several fuzzy approaches can be considered for solving multiobjective transportation problem. This paper presents a fuzzy goal programming approach to determine an optimal compromise solution for the multiobjective transportation problem. We assume that each objective function has a fuzzy goal. Also we assign a special type of nonlinear (hyperbolic) membership function to each objective function to describe each fuzzy goal. The approach focuses on minimizing the negative deviation variables from 1 to obtain a compromise solution of the multiobjective transportation problem. We show that the proposed method and the fuzzy programming method are equivalent. In addition, the proposed approach can be applied to solve other multiobjective mathematical programming problems. A numerical example is given to illustrate the efficiency of the proposed approach.     

Application of fuzzy optimization to a supply chain network design: A case study of an edible vegetable oils manufacturer

This study applies fuzzy sets to integrate the supply chain network of an edible vegetable oils manufacturer. The proposed fuzzy multi-objective linear programming model attempts to simultaneously minimize the total transportation costs. The first part of the total transportation costs is between suppliers and silos; and rest one is between manufacturer and warehouses. The approach incorporates all operating realities and actual flow patterns at production/distribution network with reference to demands of warehouses, capacities of tin and pet packaging lines. The model has been formulated as a multi objective linear programming model where data are modeled by triangular fuzzy numbers. Finally, the developed fuzzy model is applied for the case study, compiled the results and discussed.     

Fuzzy programming and profit and cost allocation for a production and transportation problem

In this paper, we deal with a real problem on production and transportation in a housing material manufacturer, and consider a production and transportation planning under the assumption that the manufacturer makes multiple products at factories in multiple regions and the products are in demand in each of the regions. First, we formulate mixed zero–one programming problems such that the cost of production and transportation is minimized subject to capacities of factories and demands of regions. Second, to realize stable production and satisfactory supply of the products in fuzzy environments, fuzzy programming for the production and transportation problem is incorporated. Finally, under the optimal planning of production and transportation, we show a profit and cost allocation by applying a solution concept from game theory. Using actual data, we show usefulness of the fuzzy programming and a rational allocation scheme of the profit and cost.     

A linear programming priority method for a fuzzy transportation problem with non-linear constraints

Demand and supply pattern for most products varies during their life cycle in the markets. In this paper, the author presents a transportation problem with non-linear constraints in which supply and demand are symmetric trapezoidal fuzzy value. In order to reflect a more realistic pattern, the unit of transportation cost is assumed to be stochastic. Then, the non-linear constraints are linearized by adding auxiliary constraints. Finally, the optimal solution of the problem is found by solving the linear programming problem with fuzzy and crisp constraints and by applying fuzzy programming technique. A new method proposed to solve this problem, and is illustrated through numerical examples. Multi-objective goal programming methodology is applied to solve this problem. The results of this research were developed and used as one of the Decision Support System models in the Logistics Department of Kayson Co.     

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。     

模糊系数规划

给出了一模糊系统规划的定义,该定义与通常的模糊规划义有所不同,它容许规划中的目标函数系数和所有约束函数系数可以是模糊数,并且容许既有模糊系数不等式的束函数又有模糊和所有约束函数系数都可以提模糊数,并且容许既有模糊系数不等式的束函数又有模糊系数等式约束函数,本文还对满足一定条件的模糊系数规划,包括模糊系数线性规划和模糊系数二次规划,给出了切实可行的求解方法。     

服务管理目标优化配置的Fuzzy-QFD线性规划模型构建与求解

由于服务管理的复杂性和模糊性,现有方法难以有效解决基于主观语言评价的服务质量改进问题。本文拓展了质量功能展开(QFD)方法在服务业中的应用,通过构建一个模糊线性规划模型,以求解最大化提高顾客需求综合满意度的企业能力优化配置问题。首先基于顾客感知-期望差距的模糊评估确定顾客需求、需求权重和边界约束等模型参数,接着运用模糊线性回归和非对称三角模糊数的隶属函数,将含有模糊变量的模糊线性规划问题转化为经典线性规划问题,进而求得不同模糊条件下的模型解。最后通过网购平台的实例验证了模型的有效性和可行性。     

具有模糊系数的模糊线性规划问题

本文考虑具有模糊系数的模糊线性规划问题中各系数的模糊可能性分布，而用指数（或线性）的隶属函数来描述，然后使用模糊数集上的实值函数，使模糊数在模型均值的意义下对应于一个实数，借此，将原问题公式化为一个普通线性规划。