基于风险态度的两级供应链设计与优化

决策者的风险态度将对供应链系统性能产生重要影响。供应链中考虑了供应商的产品价格、风险等级、供应能力和零售商需求为不确定性风险因素,研究了基于决策者风险态度的两级供应链设计与优化问题。建立了基于可信性测度的模糊机会约束规划模型,分析了风险态度的变化对供应链系统结构和目标的影响。     

含一般模糊弹性约束的广义模糊变量线性规划

建立并讨论了一类含有一般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.然后选取风险中性的决策者来定义序关系,应用Verdegay模糊线性规划方法将含一般模糊弹性约束的广义模糊变量线性规划转化经典的线性规划问题,简化了原问题的求解.最后通过数值算例进一步说明了该方法的有效性.     

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。     

模糊环境下提前期对安全库存的影响

设置安全库存可以有效管理供应链的不确定性,提高服务水平,降低缺货风险.本文基于可信性理论,研究了当需求为模糊变量,提前期分别为固定值和模糊变量时.节点企业安全库存量的确定问题.通过实际算例,分析了模糊环境下提前期对安全库存量的影响.     

考虑供应链风险和绩效的供应商选择的模糊动态非线性多目标规划模型

基于供应链风险和供应链绩效的模糊性和供应商选择问题的动态性,本文考虑供应链风险和供应链绩效作为模糊变量,讨论如何给生产商一个满意的动态多目标供应商选择方案,确定供应链风险和总成本最小,以及供应链绩效最大。然后对该问题提出了一个动态多目标多产品供应商选择模型,该模型是首次同时考虑供应商选择,订单分配,供应链风险和供应链绩效的一个模糊动态非线性多目标规划模型。为了去模糊化和求解该模型,给出了一个风险和绩效的模糊评估法。最后给出一个数值算例验证了该模型的可行性,为决策者选择供应商提供了理论依据。     

考虑决策者风险态度的三角直觉模糊双矩阵博弈均衡及应用

在现实博弈问题中,行动方案的选择不可避免的需要对预期支付值(收益值)进行估计和排序,且选择结果往往受到决策者风险态度有的影响。针对决策者具有不同风险态度的博弈环境,基于模糊值指标和模糊度指标确定的三角直觉模糊数排序关系,研究了具有风险态度的三角直觉模糊双矩阵博弈模型,并利用双线性规划方法,给出了该环境下的均衡策略确定方法,为现实博弈均衡的确定提供了有效的途径。最后通过企业营销策略选择的应用研究,对方法的现实有效性予以说明。     

基于模糊不确定性理论的Bernardo方法

Bernardo方法是一种多属性群决策方法。针对Bernardo方法，本文结合模糊不确定性理论，提出“模糊Bernardo”方法；利用模糊变量表示决策者对多方案排序的模糊目标值，给出其Bernardo方法的模糊混合0-1规划模型和模糊机会约束混合0-1规划模型。该方法为群决策提供了一种多方案排序问题的实用且有效的理论依据和计算方法。最后通过实例对此方法予以验证。     

模糊需求下的供应链收益共享契约模型

在模糊需求环境下,研究由单一供应商和零售商组成的两级供应链系统的协调机制问题。将市场需求视为模糊变量,建立不确定理论下,基于可信性分布的集成供应链模型和收益共享契约模型,并给出了模型中的最优策略。最后,以三角形模糊变量为例,通过数值算例对模型中的参数进行了求解,并分析批发价格和收益系数对其他参数的影响。研究结果表明,零售商的最优订购量在模糊需求中心点的左、右浮动,并随着零售价格的提高而增加;不同的收益系数对应不同的批发价格策略;通过改变收益系数可以实现供应链成员之间的完美协调。     

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.     

直觉模糊数风险偏好一致排序方法和决策

证明直觉模糊数的Hong排序法、刘华文排序法和陈东峰排序法都要求决策者的风险态度随直觉模糊数变化而变化,不满足风险偏好一致性,违背决策者的风险态度相对稳定的实际情况.提出基于风险偏好系数的直觉模糊数排序方法,它能保证决策者风险偏好一致;并且,面对相同的决策问题,不同风险偏好的决策者可能有不同的决策结果.最后,把基于风险偏好系数的直觉模糊数排序法应用于直觉模糊集多属性决策.     

考虑破产风险约束的多项目投资组合决策模型

项目投资决策不导致破产事件的发生,是投资者获得预期收益的前提,故控制破产事件发生的概率至关重要。鉴于此,本文基于可信性测度理论,根据Roy的定义给出了未来现金流量隶属三角模糊变量的控制破产风险的数学表达式,并构建了项目投资过程中受到破产风险因素影响的具有破产风险约束的多项目投资组合决策模型。最后,运用遗传算法对模型进行求解,并给出算例演示本文模型的实用性和有效性。     

An approximation-based approach for fuzzy multi-period production planning problem with credibility objective

This paper develops a fuzzy multi-period production planning and sourcing problem with credibility objective, in which a manufacturer has a number of plants or subcontractors. According to the credibility service levels set by customers in advance, the manufacturer has to satisfy different product demands. In the proposed production problem, production cost, inventory cost and product demands are uncertain and characterized by fuzzy variables. The problem is to determine when and how many products are manufactured so as to maximize the credibility of the fuzzy costs not exceeding a given allowable invested capital, and this credibility can be regarded as the investment risk criteria in fuzzy decision systems. In the case when the fuzzy parameters are mutually independent gamma distributions, we can turn the service level constraints into their equivalent deterministic forms. However, in this situation the exact analytical expression for the credibility objective is unavailable, thus conventional optimization algorithms cannot be used to solve our production planning problems. To overcome this obstacle, we adopt an approximation scheme to compute the credibility objective, and deal with the convergence about the computational method. Furthermore, we develop two heuristic solution methods. The first is a combination of the approximation method and a particle swarm optimization (PSO) algorithm, and the second is a hybrid algorithm by integrating the approximation method, a neural network (NN), and the PSO algorithm. Finally, we consider one 6-product source, 6-period production planning problem, and compare the effectiveness of two algorithms via numerical experiments.     

Maximum cut in fuzzy nature: Models and algorithms

The maximum cut (Max-Cut) problem has extensive applications in various real-world fields, such as network design and statistical physics. In this paper, a more practical version, the Max-Cut problem with fuzzy coefficients, is discussed. Specifically, based on credibility theory, the Max-Cut problem with fuzzy coefficients is formulated as an expected value model, a chance-constrained programming model and a dependent-chance programming model respectively according to different decision criteria. When these fuzzy coefficients are represented by special fuzzy variables like triangular fuzzy numbers and trapezoidal fuzzy numbers, the crisp equivalents of the fuzzy Max-Cut problem can be obtained. Finally, a genetic algorithm combined with fuzzy simulation techniques is designed for the general fuzzy Max-Cut problem under these models and numerical experiment confirms the effectiveness of the designed genetic algorithm.     

A structured solution framework for fuzzy minimum spanning tree problem and its variants under different criteria

This paper develops a unified and structured solution framework for the minimum spanning tree (MST) problem and its variants (e.g., constrained MST problem and inverse MST problem) on networks with fuzzy link weights. It is applicable to any additive decision criterion under fuzziness (e.g., expected value, value at risk, and conditional value at risk), for generalized cases that the link weights may be represented by arbitrary types of fuzzy variables. It also applies to the entropy criterion while the link weights are continuous fuzzy variables. Following the optimality conditions of the fuzzy MST under different decision criteria proved first in this paper, it is shown that the MST problem and its variants on a fuzzy network can be converted into equivalent deterministic counterparts on their corresponding crisp networks. Consequently, these problems can be effectively solved via their deterministic counterparts without fuzzy simulation, and meanwhile, the performance of the trees under a specified criterion is precisely measured. The accuracy and efficiency are both significantly improved compared with other fuzzy simulation-based approaches. Numerical examples illustrate the superiority of the proposed solution framework. Furthermore, some new theoretical conclusions on the MST problem under fuzziness are also presented.

     

Credibilistic Markov decision processes: The average case

Using a concept of random fuzzy variables in credibility theory, we formulate a credibilistic model for unichain Markov decision processes under average criteria. And a credibilistically optimal policy is defined and obtained by solving the corresponding non-linear mathematical programming. Also we give a computational example to illustrate the effectiveness of our new model.     

Dynamic pricing model and algorithm for perishable products with fuzzy demand

This paper studies the dynamic pricing problem of selling fixed stock of perishable items over a finite horizon, where the decision maker does not have the necessary historic data to estimate the distribution of uncertain demand, but has imprecise information about the quantity demand. We model this uncertainty using fuzzy variables. The dynamic pricing problem based on credibility theory is formulated using three fuzzy programming models, viz.: the fuzzy expected revenue maximization model, α‐optimistic revenue maximization model, and credibility maximization model. Fuzzy simulations for functions with fuzzy parameters are given and embedded into a genetic algorithm to design a hybrid intelligent algorithm to solve these three models. Finally, a real‐world example is presented to highlight the effectiveness of the developed model and algorithm. Copyright © 2009 John Wiley & Sons, Ltd.     

考虑可信度的犹豫模糊多属性决策方法研究

为深入研究犹豫环境下多属性决策问题,提高决策的科学性和准确性,本文在已有研究成果的基础上,充分考虑犹豫模糊集中隶属度函数的差异性对决策结果的影响。论文引入了专家可信度的思想对犹豫模糊信息进行集成,将一组数值转化为一个数值,进而实现犹豫模糊信息的对比研究,以此构建了一种考虑可信度的犹豫模糊多属性决策方法。最后,论文以汽车零部件供应商选择问题为例,验证了该方法的有效性。     

基于可信性理论的模糊熵权的MADM模型研究

针对属性值以模糊变量形式给出的特点,运用2004年基础数学领域完成的研究模糊现象数量规律的一个新的数学分支——可信性理论的原理,提出了基于可信性测度的模糊变量的熵的权重求解公式,结合模糊变量之间的距离,构造了属性值与理想点之间距离的加权算术平均算子,并以其算子的计算值的大小获得最优方案和排序的一种模糊熵权的多属性决策模型.结合一实例进行分析和计算,显示该模型较为贴近实际,体现了模型的正确性.     

提前期需求为模糊随机条件下的连续库存补货策略

考虑提前期内需求为模糊随机变量且提前期为可缩短情形下,建立由购买商和供应商所组成的简单供应链连续库存补货策略优化模型,其中订单量、再订货点和提前期为决策变量.首先推导出模糊随机需求条件下购买商和供应链的成本函数,然后,进一步考虑总需求为三角模糊数,推导出供应商、购买商和供应链的模糊成本函数.在此基础上分别从购买商成本最小和供应链成本最小角度对模型进行求解,结合具体算例对模型进行应用分析和比较分析,结果表明模型具有有效性和实用性,并得出如下结论:从购买商本身角度考虑订购策略所产生的供应链成本总是大于从供应链整体角度考虑订货策略所产生的供应链成本,同时从购买商本身角度考虑订货策略所产生的最优订购量、购买商成本低于从供应链整体角度考虑订货策略所产生的最优订购量、购买商成本.     

需求不确定下供应链间古诺博弈决策分析

通过引入区间数和心态指标将需求函数模糊化,研究了需求不确定下供应链间竞争的决策问题。首先,分别构建了分散-分散决策、集中-分散决策和集中-集中决策三种模式下的供应链间价格竞争模型;其次,分析了不同竞争模式下市场规模等变量对零售价格、顾客需求量和供应链利润的影响;最后,以两条供应链为例,进行算例分析,并在此基础上,进一步分析了心态指标、市场规模等变量对最终决策结果的影响。