一种具有区间数信息的多属性大群体决策方法

针对属性值为区间数的多属性多方案大群体决策问题,提出一种区间数聚类算法.通过该聚类算法将方案的属性值聚类,得到方案的群体偏好矩阵,再利用诱导有序加权平均算子集结确定属性的权重,根据方案的综合评价值给出方案排序,进而提出大群体决策方法.该方法较好地避免聚类阈值选取的经验性,提高决策结果的可信度.实例分析验证了方法的有效性和实用性.     

Decision Making with Ordinal Payoffs Under Dempster–Shafer Type Uncertainty

Our focus is on decision making in uncertain environments. We first introduce the Dempster–Shafer framework to model the uncertainty associated with possible outcomes. We then describe an approach for decision making when our uncertainty is captured using the Dempster–Shafer model and where the payoffs are numeric values. An important part of this approach is the role of the decision attitude as well as the aggregation of the possible payoffs. We then look at the situation where the payoffs, rather than being numbers, are values drawn from an ordinal scale. This requires us to provide appropriate operations for combining payoffs drawn from an ordinal scale.     

群决策中两类三端点区间数判断矩阵的集结方法

研究群决策过程中三端点区间数互反判断矩阵和三端点区间数互补判断矩阵的集结. 采用OWA(Ordered weighted averaging) 方法将决策者的偏好信息集结为两个三端点区间数判断矩阵. 基于三端点区间数判断矩阵的完全一致性概念, 建立三端点区间数判断矩阵的权重求解模型. 根据群决策背景下专家群最大一致的目标, 建立求解专家群体偏好权重的模型. 在第二阶段建立群偏好权重分布范围估计模型, 最后通过可能度方法以排定各方案的最终优劣顺序.     

区间数信息下的多属性群决策研究

随着决策系统的不断扩大以及复杂化,评价指标以实数值描述稍显欠妥。研究属性权重为实数值、区间数信息下的多属性群决策问题,建立集结区间数信息的非线性规划模型,采用模拟植物生长算法集结各方案中多位专家给出的区间数偏好,并利用得到的偏好区间数构成群决策偏好矩阵。结合已知权重,引入投影理论得到各方案的综合评价值,从而选出最优决策方案。算例结果表明,模拟植物生长算法不仅计算简便、灵活,并且尊重每个专家的意见,较好地保留了信息的完整性,显示出人工智能算法在信息集结方面的有效性。     

区间灰色不确定语言多属性群决策方法

针对属性值为区间灰色不确定语言信息的多属性群决策问题,在定义区间灰色不确定语言变量及其运算规则的基础上,给出了3种几何加权集结算子,由区间灰色不确定语言几何加权算子集结各决策者给出的决策矩阵得到群体决策矩阵。在属性权重已知的情形下,基于该算子集结单个决策者给出的属性权重向量得到群体属性权重向量;在属性权重完全未知的情形下,采用信息熵法确定属性权重向量。采用区间灰色不确定语言混合几何加权算子集结各属性评价信息,得到各方案的综合评价值,基于区间灰色不确定语言变量大小比较的方法得到方案排序结果。算例分析表明了该方法的有效性与可行性。     

The Effect of Mixed American–Chinese Group Composition on Computer‐Mediated Group Decision Making

The objective of this study was to investigate the influences of cultural diversity and group composition on computer‐mediated group decision making for mixed American–Chinese groups. Seventy‐five Chinese and fifty Americans participated in a laboratory experiment. Twenty‐five five‐person groups were formed. Each group performed four preference decision‐making tasks with different levels of impact and complexity. Group‐level measurements of group outcomes and individual‐level measurements of group processes were collected. The results indicated that cultural diversity had both positive and negative effects on group decision making. Culturally heterogeneous groups made better decisions than did culturally homogeneous groups, but took longer to make decisions. Moreover, in terms of improvement of the quality of decisions, groups with a balanced representation from each culture outperformed unbalanced groups. Meanwhile, participants in the balanced groups had greater sensitivity to leader emergence and perceived less conflict. The results of this study also revealed the effects of self‐construal and self‐efficacy in communication on group performance. Implications for intercultural management were provided. © 2012 Wiley Periodicals, Inc.     

考虑心理行为的区间二元语义动态群决策方法

针对当前属性值为区间语言信息的群决策方法的不足,提出了一种考虑决策者心理行为的区间二元语义动态多属性群决策方法。定义了一种更接近决策者思维习惯的区间二元语义新的距离公式;引入时间度概念,结合考虑专家评价的综合一致性,建立优化模型求解时序权重;并基于前景理论,构建前景偏差值最小化的权重优化模型;采用基于新距离测度的交互式多属性决策（TODIM）方法计算方案的综合总体优势度,以此对方案进行排序。并以供应链协同合作伙伴的选择与评价为例,验证了所提方法的有效性和合理性。     

决策信息为区间二元语义的双向专家权重确定方法

针对区间二元语义多属性群决策中的专家客观赋权问题,提出了一种区间二元语义群决策的双向专家权重确定方法。首先设置专家初始权重,通过专家个体与群体决策矩阵的偏差距离计算专家的偏离权重,再通过专家的直觉模糊熵计算专家的模糊熵权重,结合偏离权重和模糊熵权重,经过多次迭代后得到稳定的专家双向权重。该权重既反映了专家偏好信息与群体偏好信息的一致性,同时也反映了专家对决策问题的了解程度。最后,实例验证了该算法的可行性与有效性。     

Efficient Two-Stage Group Testing Algorithms for Genetic Screening

Efficient two-stage group testing algorithms that are particularly suited for rapid and less-expensive DNA library screening and other large scale biological group testing efforts are investigated in this paper. The main focus is on novel combinatorial constructions in order to minimize the number of individual tests at the second stage of a two-stage disjunctive testing procedure. Building on recent work by Levenshtein (Discrete Math., 266:293–309, 2003) and Tonchev (J. Comb. Optim., 15:1–6, 2008), several new infinite classes of such combinatorial designs are presented.     

语言型群决策中群体一致性赋权优化模型研究

语言评价矩阵一致性分析和专家客观权重确定是语言型多属性群决策中的2个重要问题,将两者视为一个整体进行研究。基于语言评价矩阵偏差度概念对评价矩阵一致度进行定义和度量,结合群体一致性最优思想和智能优化算法,构建基于语言型决策矩阵的专家客观权重赋权优化模型并给出求解方法,在此基础上提出基于客观权重的语言型多属性群决策方法。通过实例分析说明该模型得到的专家客观权重满足一致性条件。     

考虑专家偏好关联的群决策方法及其应用

通过分析群决策过程,提出使用模糊测度描述专家偏好之间可能存在的关联关系,并给出了一种考虑专家偏好关联的群决策方法.该方法从参评专家知识结构的相似性及判断结果的相似性出发,通过计算得到相应的2-可加模糊测度来描述专家的重要程度,并使用Choquet积分将多个专家的偏好信息聚合为群体的判断结果.最后,通过一个潜艇装备论证的例子验证了所提出方法的可行性和合理性.     

Adaptive Primal–Dual Genetic Algorithms in Dynamic Environments

Recently, there has been an increasing interest in applying genetic algorithms (GAs) in dynamic environments. Inspired by the complementary and dominance mechanisms in nature, a primal-dual GA (PDGA) has been proposed for dynamic optimization problems (DOPs). In this paper, an important operator in PDGA, i.e., the primal-dual mapping (PDM) scheme, is further investigated to improve the robustness and adaptability of PDGA in dynamic environments. In the improved scheme, two different probability-based PDM operators, where the mapping probability of each allele in the chromosome string is calculated through the statistical information of the distribution of alleles in the corresponding gene locus over the population, are effectively combined according to an adaptive Lamarckian learning mechanism. In addition, an adaptive dominant replacement scheme, which can probabilistically accept inferior chromosomes, is also introduced into the proposed algorithm to enhance the diversity level of the population. Experimental results on a series of dynamic problems generated from several stationary benchmark problems show that the proposed algorithm is a good optimizer for DOPs.     

Bonferroni Distances and Their Application in Group Decision Making

Abstract

The aim of the paper is to develop new aggregation operators using Bonferroni means, ordered weighted averaging (OWA) operators and some distance measures. We introduce the Bonferroni-Hamming weighted distance (BON-HWD), Bonferroni OWA distance (BON-OWAD), Bonferroni OWA adequacy coefficient (BON-OWAAC) and Bonferroni distances with OWA operators and weighted averages (BON-IWOWAD). The main advantages of using these operators are that they allow the consideration of different aggregations contexts to be considered and multiple comparison between each argument and distance measures in the same formulation. An application is developed using these new algorithms in combination with Pichat algorithm to solve a group decision-making problem. Creative personality is taken as an example for forming creative groups. The results show fuzzy dissimilarity relations in order to establish the maximum similarity subrelations and find groups according to each individual’s creative personality similarities.     

考虑偏好反转的区间不确定多属性决策方法

针对实际决策中的不确定性和偏好反转问题, 提出一种区间不确定多属性决策方法. 该方法先用证据推理方法集结区间不确定评估信息, 采用累积前景理论代替主观期望效用理论以构建方案的综合前景价值, 从而应对不确定环境下可能的决策偏好反转, 最后区间可能度用于方案综合前景价值排序. 介绍了决策过程, 给出求解方案综合前景价值的非线性规划模型, 并通过实例验证了方法的可行性、合理性和有效性.     

Power-Geometric Operators and Their Use in Group Decision Making

The power-average (PA) operator and the power-ordered-weighted-average (POWA) operator are the two nonlinear weighted-average aggregation tools whose weighting vectors depend on the input arguments. In this paper, we develop a power-geometric (PG) operator and its weighted form, which are on the basis of the PA operator and the geometric mean, and develop a power-ordered-geometric (POG) operator and a power-ordered-weighted-geometric (POWG) operator, which are on the basis of the POWA operator and the geometric mean, and study some of their properties. We also discuss the relationship between the PA and PG operators and the relationship between the POWA and POWG operators. Then, we extend the PG and POWG operators to uncertain environments, i.e., develop an uncertain PG (UPG) operator and its weighted form, and an uncertain power-ordered-weighted-geometric (UPOWG) operator to aggregate the input arguments taking the form of interval of numerical values. Furthermore, we utilize the weighted PG and POWG operators, respectively, to develop an approach to group decision making based on multiplicative preference relations and utilize the weighted UPG and UPOWG operators, respectively, to develop an approach to group decision making based on uncertain multiplicative preference relations. Finally, we apply both the developed approaches to broadband Internet-service selection.      

实体异构性下证据链融合推理的多属性群决策

针对多属性群决策中可解释性证据融合推理的实体异构性问题,给出了一个实体异构性下证据链融合推理的多属性群决策方法.基于证据推理理论,引入证据链关联的概念,从多数据表提供的数据矩阵中获取可区分的近邻证据集,推导了各数据表的相似度矩阵,并构建半正定矩阵的二次优化模型,共享群决策专家的经验知识.使用Dempster正交规则,论证了异构实体之间可解释性推理中可信度融合的合理性,并使用证据融合规则集成各个数据表的近邻证据中获得的可信度,验证了调和多源异构数据中不一致信息的有效性.通过具有实体异构性的心脏病多决策数据诊断实例说明了方法的可行性与合理性.     

A Consensus Model for Group Decision Making With Incomplete Fuzzy Preference Relations

Two processes are necessary to solve group decision making problems: A consensus process and a selection process. The consensus reaching process is necessary to obtain a final solution with a certain level of agreement between the experts; and the selection process is necessary to obtain such a final solution. In a previous paper, we present a selection process to deal with group decision making problems with incomplete fuzzy preference relations, which uses consistency measures to estimate the incomplete fuzzy preference relations. In this paper we present a consensus model. The main novelty of this consensus model is that of being guided by both consensus and consistency measures. Also, the consensus reaching process is guided automatically, without moderator, through both consensus and consistency criteria. To do that, a feedback mechanism is developed to generate advice on how experts should change or complete their preferences in order to reach a solution with high consensus and consistency degrees. In each consensus round, experts are given information on how to change their preferences, and to estimate missing values if their corresponding preference relation is incomplete. Additionally, a consensus and consistency based induced ordered weighted averaging operator to aggregate the experts' preferences is introduced, which can be used in consensus models as well as in selection processes. The main improvement of this consensus model is that it supports the management of incomplete information and it allows to achieve consistent solutions with a great level of agreement.     

Fuzzy Algorithm for Group Decision Making With Participants Having Finite Discriminating Abilities

A fuzzy inference-based algorithm with rules using the Nash solution is proposed for group decision making considering the finite discriminating abilities of real decision makers (DMs). It provides a solution that can capture and incorporate the imprecision of real people at declaring their preferences, and hence, it reflects more faithfully the DMs' opinions. The algorithm is applied to a purchase project of a storage area network with two DMs and three options. It shows how the algorithm can provide a unique solution whereas customary crisp methods are either unable to do it or reveal a risk of choosing, in 16.5% of the cases, an option that does not match with the preferences declared by the group of DMs as a whole. The algorithm aims for processes where the options are difficult to evaluate, circumstance that makes clear that human beings cannot provide unreal crisp values, and that the solution changes if preference information is only partially taken.      

基于群决策的道岔控制电路故障诊断方法

高速铁路道岔是与高速列车直接接触的重要信号设备,其控制电路的故障检测手段目前仍停留在简单仪器与人的经验相结合的方式.为了实现道岔控制电路故障的智能诊断,提高故障诊断的准确率并降低单一诊断方法带来的不确定性,本文提出一种基于群决策的诊断方法：首先根据道岔控制电路的特点,总结了典型的11个故障模式和对应的8个故障特征;其次,分别采用模糊理论、神经网络和支持向量机（Support vector machine,SVM）对道岔控制电路进行故障诊断;然后引入群决策理论将三种方法视为决策专家,通过群基数效应集结方式实现决策级上的信息融合从而得到群专家综合评判的诊断结果.从仿真数据的验证来看,该方法比单一方法的故障诊断的准确率要高,表明了本文所提方法能够实现三种方法的互补融合,也提高了故障诊断的准确率,在该领域有着良好的应用前景.