Cooperative decision making in a knowledge grid environment

A knowledge grid is an intelligent interconnection environment, built on top of a computational grid, to facilitate the creation of virtual organizations. An important feature of a virtual environment is its support for collaborative decision-making. A major difficulty with current approaches is that they cannot easily handle environments where decision makers are added or removed dynamically. In this article, a new approach to alter the number of decision makers dynamically is suggested. The amount of decision accuracy made by each decision maker, for a given subject, is determined subjectively considering the other decision makers’ opinions. The effect of decisions made by each decision maker varies gradually considering its past decisions. Assuming each decision maker provides a fuzzy answer set in response to each decision problem, an operator for fusing of the decision makers’ decision sets is suggested. The aim of the fusion is improvement of the decision quality. The fusing operator provides a fuzzy answer set that is a function of the accuracy possibility of each decision maker and its fuzzy answer set.     

TOPSIS with fuzzy belief structure for group belief multiple criteria decision making

TOPSIS is one of the major techniques in dealing with multiple criteria decision making (MCDM) problems, and Belief Structure (BS) model and Fuzzy BS model have been used successfully for uncertain MCDM with incompleteness, impreciseness or ignorance. In this paper, the TOPSIS method with Fuzzy BS model is proposed to solve Group Belief MCDM problems. Firstly, the Group Belief MCDM problem is structured as a fuzzy belief decision matrix in which the judgments of each decision maker are described as Fuzzy BS models, and then the Evidential Reasoning approach is used for aggregating the multiple decision makers’ judgments. Subsequently, the positive and negative ideal belief solutions are defined with the principle of TOPSIS. In order to measure the separation from the ideal belief solutions, the concept and algorithm of Belief Distance Measure are introduced to compare the difference between Fuzzy BS models. Using the Belief Distance Measure, the relative closeness and ranking index can be calculated for ranking the alternatives. A numerical example is finally given to illustrate the proposed method.     

Expertise-based consensus building for MCGDM with hesitant fuzzy linguistic information

The integration of a consensus reaching process (CRP) becomes paramount to make highly accepted group decisions in complex real-life multi-criteria group decision making (MCGDM) problems. Notwithstanding, existing CRPs for MCGDM do neither exhaustively analyse the diversity in decision makers’ expertise levels, nor they consider that (because of such diversity) individuals might exhibit distinct perceptions on the relative importance of evaluation criteria. In this study, we present a novel expertise-based consensus building model for MCGDM under a hesitant fuzzy linguistic setting. Firstly, an expertise identification approach is devised to objectively determine the expertise degree of each decision maker based on multiple features. The proposed approach allows to dynamically assigning importance weights to the decision makers’ opinions based on their expertise, as well as intelligently combining their individually elicited subjective and objective criteria weights into meaningful expertise-dependent combinative weights. Then, a CRP for MCGDM problems is introduced based on an improved consensus measurement process and an expertise-based feedback mechanism that provides a highly tailored, personalised means of direction rules to guide decision makers during the consensus building process. A numerical example is provided to illustrate the application of the CRP, and a detailed comparison analysis is presented to verify the validity and accuracy of this study’s proposal.     

基于决策者共识偏好的冲突分析图模型

为刻画实际冲突中决策者复杂的决策行为,完善冲突分析图模型稳定性理论的不足,对共识偏好下图模型的稳定性进行研究.首先,将决策者偏好划分为共识偏好和非共识偏好;然后,在4种基本稳定性定义的基础上,将图模型稳定性拓展到8种,拓展后的稳定性不但能深刻反映共赢型、利己型及混合型决策者的决策行为,还能有效预测不同行为模式下冲突局势的均衡结果;最后,通过案例验证所提出方法在解决实际冲突问题中的有效性.     

考虑供应商风险偏好的供应链系统动力学分析

基于前景理论,以供应商缺货损失及供应商备货过剩损失为供应商决策者风险感知来源,建立供应商决策者风险偏好模型。接着构建由供应商、制造商、分销商构成的三级供应链系统动力学模型,通过分析供应商决策者的风险偏好（回避、追求、中立）类型,调整风险感知来源的参数,分析供应商的风险偏好对供应链库存量、积压订单量、发货率等影响程度。研究表明,供应商决策者风险偏好对供应商的期初库存量、发货率以及积压订单量有较大影响,结合对风险感知来源的考虑可以弱化牛鞭效应,减少订单积压问题。仿真实验结果表明模型具有较高的可行性与稳定性,能结合决策者风险偏好对供应链整体运作优化提供理论支持。     

A measurement model for experts knowledge-based systems algorithm using fuzzy analytic network process

This study proposes an experts knowledge-based systems measurement model, the model using fuzzy analytic network process (FANP) to resolve the uncertainty and imprecision of evaluations during pre-negotiation stages, where the comparison judgments of a decision maker are represented as fuzzy triangular numbers. A novel fuzzy prioritization method, which derives crisp priorities (criteria weights and scores of alternatives) from consistent and inconsistent fuzzy comparison matrices, is also proposed. The applicability of the proposed model is demonstrated in a government purchase digital video recorder (DVR) system project study. The stability tests indicate the advantages of the proposal model in determining the value of model. Importantly, the proposed model can provide decision makers a reference material, making it highly applicable for academic and commercial purposes.     

面向过程人机交互战略决策支持系统模型研究

文章介绍了人机交互研讨模型的概念和功能。在定性、定量相结合的战略决策支持系统中,通过设计人机交互研讨模型,规范化人机交互模式,将决策者与决策支持系统有机结合起来,在统一的过程模型视图下,以信息服务方式为决策者的定性判断提供支持,同时将人的决策引入支持系统。     

An integrated fuzzy multi criteria group decision making approach for ERP system selection

This paper aims to ease group decision-making by using an integration of fuzzy AHP (analytic hierarchy process) and fuzzy TOPSIS (technique for order preference by similarity to ideal solution) and its application to software selection of an electronic firm. Firstly, priority values of criteria in software selection problem have been determined by using fuzzy extension of AHP method. Fuzzy extension of AHP is suggested in this paper because of little computation time and much simpler than other fuzzy AHP procedures. Then, the result of the fuzzy TOPSIS model can be employed to define the most appropriate alternative with regard to this firm's goals in uncertain environment. Fuzzy numbers are presented in all phases in order to overcome any vagueness in decision making process. The final decision depends on the degree of importance of each decision maker so that wrong degree of importance causes the mistaken result. The researchers generally determine the degrees of importance of each decision maker according to special characteristics of each decision maker as subjectivity. In order to overcome this subjectivity in this paper, the judgments of decision makers are degraded to unique decision by using an attribute based aggregation technique. There is no study about software selection using integrated fuzzy AHP-fuzzy TOPSIS approach with group decision-making based on an attribute based aggregation technique. The results of the proposed approach and the other approaches are compared. Results indicate that our methodology allows decreasing the uncertainty and the information loss in group decision making and thus, ensures a robust solution to the firm.     

The Importance of Active Exploration,Optical Flow,and Task Alignment for Spatial Learning in Desktop 3D Environments

The Decision Support Systems (DSS) field has grown rapidly, drawing technology from many disciplines and pursuing applications in a variety of domains but developing little underlying theoretical structure and poor linkage between research and practice. This article presents a classification scheme for DSS techniques that provides a common theoretical framework for DSS research and also structures and simplifies the process of designing application systems. The classification system is functional, grouping DSS techniques according to their ability to provide similar kinds of support (i.e., functions) to a human decision maker. It is also cognitively based, defining the kinds of support that decision makers need in terms of architectural features and procedural aspects of human cognition. The classification is expressed as a taxonomy, encompassing six primary classes of decision support techniques representing the six general kinds of cognitive support that human decision makers need. The six classes are process models, which assist in projecting the future course of complex processes; choice models, which support integration of decision criteria across aspects and/or alternatives; information control techniques, which help in storage, retrieval, organization, and integration of data and knowledge; analysis and reasoning techniques, which support application of problem-specific expert reasoning procedures; representation aids, which assist in expression and manipulation of a specific representation of a decision problem; and judgment amplification/refinement techniques, which help in quantification and debiasing of heuristic judgments. Additional distinctions are provided to distinguish the individual techniques in each of these primary categories. The taxonomy also has practical use as a design aid for decision support systems. The kinds of decision support needs represented by the taxonomy are general and can be used to guide the analysis and decomposition of a given decision prior to decision aid design. Specific needs for assistance can then be tied to specific computational techniques in the taxonomy. Methodological suggestions for using the taxonomy as a design aid are given.     

Interactive fuzzy random cooperative two-level linear programming through level sets based probability maximization

In this paper, assuming cooperative behavior of the decision makers, two-level linear programming problems under fuzzy random environments are considered. To deal with the formulated fuzzy random two-level linear programming problems, α-level sets of fuzzy random variables are introduced and an α-stochastic two-level linear programming problem is defined for guaranteeing the degree of realization of the problem. Taking into account vagueness of judgments of decision makers, fuzzy goals are introduced and the α-stochastic two-level linear programming problem is transformed into the problem to maximize the satisfaction degree for each fuzzy goal. Through probability maximization, the transformed stochastic two-level programming problem can be reduced to a deterministic one. Interactive fuzzy programming to derive a satisfactory solution for the decision maker at the upper level in consideration of the cooperative relation between decision makers is presented. An illustrative numerical example is provided to demonstrate the feasibility and efficiency of the proposed method.     

Does positive affect influence the effective usage of a Decision Support System?

This study examines the impact of positive mood on the effective usage of a Decision Support System (DSS). Using current cognitive theories, a theoretical argument about DSS usage is developed. This argument is then investigated via a lab experiment. The results of the lab experiment show that decision makers in positive mood used a greater number of informational cues provided by the DSS and made more accurate judgments.     

An examination of decision-makers' utilization of decision support system output

A study was conducted of 18 decision support systems in major U.S. corporations in order to examine their methods of operation and the ways in which the DSS contribute to the decision making process. These DSS were studied with respect to organizational level of the decision maker, phases of the decision making process, interaction among decision makers using the DSS, requirement for and regularity of DSS use, impact on job tasks and performance, and perceived value of the DSS. Study results provide substantial support for generalized conceptualizations in the literature. Decision support is primarily for upper and/or middle management working in interaction. Use of the DSS is at the discretion of the decision maker and the majority of DSS users are highly satisfied with their systems.     

基于双层主从决策的多机协同关键设施攻击模型研究

现代战争中,多架战机协同攻击已成为一种重要的作战模式。面对复杂的敌方网络系统,目标选择及战机资源的优化分配是决定空袭效果的关键因素。针对此类问题,通过分析空袭环境下攻守双方的主要特征,建立了基于双层主从决策的多机协同关键设施攻击模型,其中上层决策者以中断敌方网络系统关键服务设施节点为目标,下层决策者以提高己方机群战场生存率和目标击中率为目标。通过求解该模型,对选定目标节点的战机分配策略进行综合优化。最后,通过实例分析和仿真实验,验证了该模型的可行性和有效性。     

User oriented techniques to support interaction and decision making with large educational databases

Information Technology is developing rapidly and providing policy/decision makers with large amounts of information that require processing and analysis. Decision support systems (DSS) aim to provide tools that not only help such analyses, but enable the decision maker to experiment and simulate the effects of different policies and selection strategies. The specific context of this research, set in Saudi Arabia, is administrative decision making using large educational databases.     

群决策中两类判断矩阵的一种集成方法

研究群决策中不同偏好信息形式的集成方法。根据多个决策者给出关于方案的两类偏好信息－Fuzzy判断矩阵和AHP判断矩阵,建立了能够集成这两类偏好信息的最优化模型,通过求解该模型可直接得到每个方案的参考排序列,并使方案的排序结果最大程度地反映每个决策者的偏好。     

基于BP神经网络的维修任务优先级分类方法

在任务执行期合理、科学地确定维修任务的优先级别对于有序、高效地组织维修保障活动具有重要意义。提出了一种基于BP神经网络的维修任务优先级分类方法。详细介绍了神经网络模型的建模过程,其中重点介绍了模型设计,包括输入数据准备、输出数据准备与神经网络结构。所建立的神经网络模型通过对输入与输出的训练,可以学习准则与维修任务优先级之间的复杂关系,获得并表示决策者的偏好,有效地辅助决策者对维修任务优先级进行分类。     

Optimal expansion of competence sets with multilevel skills

The purpose of competence set expansion is to find an optimal expansion process at the minimal cost and then obtain the required competence set from the acquired competence set to solve a problem. Several models have been proposed to address the competence set expansion problem of only a single decision maker or multiple decision makers without considering multilevel skills. However, a practical competence set expansion model should involve multiple decision makers and multilevel skills. This study discusses an optimal expansion model of incorporating competence sets of group decision makers with multilevel skills. The proposed method not only obtains the optimal competence set expansion of all decision makers with the maximal total benefit but finds all optimal alternatives of the competence set expansion model. A cooperative alliance problem is solved to illustrate the usefulness of the proposed method.     

Modelling decision support and uncertainty for large transport infrastructure projects: The CLG-DSS model of the Øresund Fixed Link

This paper presents a decision support system, named the CLG-DSS model, which makes it possible for decision makers to assess various uncertainties in project appraisal in a systematic and explicit way. This model, a decision support system (DSS) developed within the Danish Centre for Logistics and Freight Transport (CLG) is based on cost-benefit analysis (CBA) embedded in a wider multi-criteria analysis (MCA) by the use of some principles for composite modelling assessment (COSIMA). The CLG-DSS model is set up to make use of scenario analysis (SA) and Monte Carlo simulation (MCS). A particular concern in the model is the handling of varying information across the assessment criteria and the application of SA to inform the MCS parameter setting. After the presentation of the modelling principles some ex-post case calculations for the Øresund Fixed Link are illuminating different aspects of appraisal uncertainty and thereby, at the same time, demonstrate the features of the CLG-DSS model as a useful decision support tool. It is finally concluded that appraisal of large infrastructure projects can be effectively supported by dealing with uncertainty issues in accordance with the principles described.     

Interactive fuzzy programming for random fuzzy two-level programming problems through possibility-based fractile model

This paper focuses on interactive decision making methods for random fuzzy two-level linear programming problems. Considering the probabilities that the decision makers’ objective function values are smaller than or equal to target variables, fuzzy goals of the decision makers are introduced. Using the fractile model to optimize the target variables under the condition that the degrees of possibility with respect to the attained probabilities are greater than or equal to certain permissible levels, the original random fuzzy two-level programming problems are reduced to deterministic ones. Interactive fuzzy nonlinear programming to obtain a satisfactory solution for the decision maker at the upper level in consideration of the cooperative relation between decision makers is presented. An illustrative numerical example demonstrates the feasibility and efficiency of the proposed method.