供应商选择的组合多属性群决策

为解决图书供应商选择问题,建立同时考虑多个属性,由多人进行决策的组合多属性群决策模型。先用TOPSIS法求出单个决策者对各供应商的综合评价值,再用三种不同的社会选择函数分别对个体偏好进行集结,然后将三种结果进行组合,得出决策群体对供应商的最终排序结果。最后以一个实例说明模型的应用。该方法可提高供应商选择的有效性。     

群决策专家权重自适应算法研究*

为获得科学合理的群体决策结果,并在综合集成研讨厅中维护一个动态的专家权重信息库,提出了一种基于TOPSIS法和灰色关联度的专家权重自适应调整算法,算法综合考虑了专家个体与群体在方案重要属性以及方案序的判定上的相似度,并根据决策问题的不同,对算法的参数进行相应选取,算例表明算法是有效的,收敛速度也比较快。     

基于多准则决策的软件可靠性模型选择方法

针对现有软件可靠性模型选择方法计算复杂和适用性差的问题,提出基于多准则决策的软件可靠性模型选择方法.定义了指导软件可靠性模型选择的若干准则,包括生命周期阶段准则、模型输入要求准则、模型输出要求准则、模型假设吻合准则和失效数据趋势准则,将这些准则分为确定性准则和不确定性准则,阐述了基于这些准则进行软件可靠性模型选择的算法,并实例验证了该方法的简单可行性.指出将这种方法与现有软件可靠性模型选择方法进行综合运用的研究方向.     

基于多属性决策TOPSIS的簇头选择

针对LEACH协议中因簇头数目和分布位置不合理导致网络能耗不均衡的问题,利用多属性决策逼近理想解排序法(TOPSIS)进行簇头选择。综合考虑节点的剩余能量、邻居节点数量、到基站的距离和传输范围,使用Pareto最优解选择一组簇头,进而通过TOPSIS算法进行排序选出最佳簇头。仿真结果表明,TOPSIS在剩余节点数和网络能耗上优于LEACH和AHP算法,能够有效延长网络生命周期,使网络能耗更均衡。     

考虑QoS偏好的多属性群决策服务选择策略

面对多个功能相同或相似的服务,服务的Qo S是服务选择中重要的考虑因素.将Qo S属性分为精确数型、区间数型和三角模糊数型.在此基础上,利用TOPSIS（technique for order preference by similarity to an ideal solution）条件下的多属性群决策模型给出了服务选择过程,该过程考虑了多个决策者在决策过程中所占的权重,以及多个决策者不同的Qo S偏好权重.通过一个实例验证了该方法的有效性.     

基于直觉梯形模糊TOPSIS的多属性群决策方法

提出一种改进的逼近理想解排序(TOPSIS)方法,即直觉梯形模糊TOPSIS多属性群决策方法。首先,应用直觉梯形模糊数形式表示方案属性偏好和属性权重信息且专家权重完全未知;然后,利用直觉梯形模糊数间距离测度和期望值及直觉梯形模糊加权平均算子来确定决策者权重信息和属性权重信息;进而给出直觉梯形模糊环境下方案优选的算法;最后,通过算例进一步说明了该直觉梯形模糊TOPSIS方法的有效性。     

一种多准则纯语言群决策方法

针对权系数信息和方案的准则值为确定语言等级,或位于两个语言等级之间,甚至缺失的群决策问题,提出一种新的决策方法.该方法利用证据推理算法对准则权系数和方案值在准则下进行群体集成,采用二元语义对方案进行语言集结,并用方案与理想方案的二元语义间距离和群体集成信息等构建非线性规划模型,使用遗传算法求解优化模型,进而得到方案的排序.最后通过实例说明该方法的可行性和有效性.     

图书供应商选择的混合型多属性群决策

针对同时存在基数评价信息与序数偏好的情形,考虑每个属性下的决策者权重,为图书供应商选择建立混合型多属性群决策模型。对定性和定量属性分别用0-1规划和线性加权和法求出决策群体对各供应商的评价值,再用0-1规划求得决策群体对供应商的最终排序结果。最后以一个实例说明模型的应用。此方法可提高供应商选择的科学性和有效性。     

基于前景理论的风险型混合模糊多准则群决策

针对准则值为模糊数的风险型多准则群决策问题,提出一种基于前景理论的多准则群决策方法.首先运用方差分析原理构建群决策参考点;然后分析区间数、三角模糊数、梯形模糊数等无量纲化方法,给出各类模糊数的价值函数计算方法,并提出群体信息集成决策步骤;最后通过算例表明所提出方法的有效性和可行性.     

基于二元联系数的区间直觉模糊数多准则决策方法

针对准则值为区间直觉模糊数且准则权重为区间数的多准则决策问题,提出一种基于二元联系数的区间直觉模糊型多准则决策方法.首先,介绍区间直觉模糊数和二元联系数;其次,研究区间数转化为联系数、二元联系数转化为实数的3种转化方法,对传统区间数和二元联系数的运算结果进行比较;再次,将区间型贴近度转化为基于二元联系数的实数进行方案优选;最后,运用算例表明所提出方法的优越性和可行性.     

敏捷制造中制造资源选择问题的遗传算法

对敏捷制造系统重构中的制造资源选择问题进行了分析,建立了数学模型,提出了一种适合求解该问题的遗传算法。该算法与解决同类问题的已有算法相比,编码方案和遗传算子均比较简单。实验结果表明,遗传算法在解的质量、稳定性和收敛速度方面具有优良性能。     

求解合作伙伴选择问题的高效蚁群算法

敏捷制造中的合作伙伴优化选择问题属于组合优化领域的NP-hard问题,随着规模的增大,应用传统的方法求解非常困难,甚至不可能.对敏捷制造中的合作伙伴选择问题进行了分析,建立了数学模型,设计了一个适合求解该问题的蚁群算法.实验结果表明,该算法求解效率高,性能稳定.     

Simplified neutrosophic sets and their applications in multi-criteria group decision-making problems

As a variation of fuzzy sets and intuitionistic fuzzy sets, neutrosophic sets have been developed to represent uncertain, imprecise, incomplete and inconsistent information that exists in the real world. Simplified neutrosophic sets (SNSs) have been proposed for the main purpose of addressing issues with a set of specific numbers. However, there are certain problems regarding the existing operations of SNSs, as well as their aggregation operators and the comparison methods. Therefore, this paper defines the novel operations of simplified neutrosophic numbers (SNNs) and develops a comparison method based on the related research of intuitionistic fuzzy numbers. On the basis of these operations and the comparison method, some SNN aggregation operators are proposed. Additionally, an approach for multi-criteria group decision-making (MCGDM) problems is explored by applying these aggregation operators. Finally, an example to illustrate the applicability of the proposed method is provided and a comparison with some other methods is made.     

一种基于系统总误码率的协同伙伴选择算法

提出了一种基于系统总误码率的协同伙伴选择算法。当系统总发送功率一定时,通过系统误码率公式,采用带CRC校验的DF协同通信的协同增益,来降低系统总的误码率。仿真表明,此算法有效地降低了系统总误码率,并且系统SNR越大,协同通信对系统数据传输的帮助越明显。     

Multi-criteria decision-making method based on a cross-entropy with interval neutrosophic sets

In this paper, two optimisation models are established to determine the criterion weights in multi-criteria decision-making situations where knowledge regarding the weight information is incomplete and the criterion values are interval neutrosophic numbers. The proposed approach combines interval neutrosophic sets and TOPSIS, and the closeness coefficients are expressed as interval numbers. Furthermore, the relative likelihood-based comparison relations are constructed to determine the ranking of alternatives. A fuzzy cross-entropy approach is proposed to calculate the discrimination measure between alternatives and the absolute ideal solutions, after a transformation operator has been developed to convert interval neutrosophic numbers into simplified neutrosophic numbers. Finally, an illustrative example is provided, and a comparative analysis is conducted between the approach developed in this paper and other existing methods, to verify the feasibility and effectiveness of the proposed approach.     

基于群决策的供应链伙伴选择与评价过程

分析了供应商选择与评价过程的两个基本阶段静态评价和动态评价,提出了基于群决策的供应链伙伴选择与评价方法,构造了选择矩阵,证明了选择过程的可控性,举例说明了问题的求解过程,结果证明了方法的有效性.     

Feature generation using genetic programming with comparative partner selection for diabetes classification

The ultimate aim of this research is to facilitate the diagnosis of diabetes, a rapidly increasing disease in the world. In this research a genetic programming (GP) based method has been used for diabetes classification. GP has been used to generate new features by making combinations of the existing diabetes features, without prior knowledge of the probability distribution. The proposed method has three stages: features selection is performed at the first stage using t-test, Kolmogorov–Smirnov test, Kullback–Leibler divergence test, F-score selection, and GP. The results of feature selection methods are used to prepare an ordered list of original features where features are arranged in decreasing order of importance. Different subsets of original features are prepared by adding features one by one in each subset using sequential forward selection method according to the ordered list. At the second stage, GP is used to generate new features from each subset of original diabetes features, by making non-linear combinations of the original features. A variation of GP called GP with comparative partner selection (GP-CPS), utilising the strengths and the weaknesses of GP generated features, has been used at the second stage. The performance of GP generated features for classification is tested using the k-nearest neighbor and support vector machine classifiers at the last stage. The results and their comparisons with other methods demonstrate that the proposed method exhibits superior performance over other recent methods.     

An approach to partner selection in agile manufacturing

It is believed that agile manufacturing is the twenty-first century manufacturing enterprise strategy and should be realized by agile virtual enterprise (AVE) which is dynamically configured for the manufacturing of one or more products. Agile manufacturing creates a significant challenge for the partner selection in configuring an AVE. This paper presents a brief introduction to the task decomposition for the manufacturing of a product in multisite environment. A bidding process should be carried out for each task so as to get the information necessary for the configuration of AVE. To minimize the manufacturing cost, an integer programming (IP) formulation is presented for the partner selection problem. The IP formulation is then transformed into a graph-theoretical formulation by taking the advantage of the precedence relationship between the tasks. The graph obtained turns out to be a directed multipartite graph. Based on the graph-theoretical formulation, an efficient solution algorithm is proposed for the problem. It is shown that the algorithm is polynomial bounded. Therefore, it is applicable to large practical problems.     

基于改进直觉模糊熵和信息集成算子的多准则群决策方法

针对直觉模糊环境下准则权重和决策者权重完全未知的群决策问题,提出了一种基于改进直觉模糊熵和信息集成算子的多准则群决策方法.首先,借助直觉模糊数、语言变量法和定量指标值转化公式,将决策者的初始评价准则值规范化;其次,引入改进的直觉模糊熵度量方法确定准则权重和决策者权重;进一步对直觉模糊决策矩阵用直觉模糊加权平均算子和直觉模糊有序加权平均算子进行信息集成,利用新的得分函数对备选方案排序择优.从理论上论证了该方法的可行性,结合算例验证了该方法的有效性.