一种利用信息熵确定属性权重的模糊单因素评价方法

在多属性决策问题中,由于问题的复杂性,属性的权重一般是未知量或者只有部分信息权重,研究如何确定多属性决策问题中属性的权重,以便对已有的方案进行排序或评价,已经成为多属性决策研究的一个重点问题。截止目前,多属性权重确定方法主要包括主观权重确定法和客观权重确定法,主观权重确定法具有受决策主体主观偏好影响的缺点,而客观权重确定方法往往忽略决策主体的参与程度。因此,如何研究将主客观权重复制相结合,提高决策的准确性,具有实际的研究意义。本文总结了多属性决策问题中权重的确定方法,提出了一种主观赋值与客观确定相结合的改进熵值权重确定方法,并通过实例证明了该算法的有效性。     

基于多维决策属性的网络用户行为可信度评估

网络用户行为可信的评估具有不确定性、复杂性等特点。针对已有模型在动态适应性、主观分类权重、决策属性建模粗糙等方面的不足,本文提出了一种新的网络用户行为可信评估模型。采用更完善的决策属性来衡量用户行为可信性,基于AHP原理计算直接可信度,运用信息熵理论客观的分类方法,确定各个决策属性的权重,并通过加权几何平均融合各决策属性。实验结果表明,该模型能够准确评价网络用户行为的可信性,反映网络用户行为可信性的动态变化特性。与传统模型相比,在准确度和安全性方面有了很大提高。     

可信网络中基于多维决策属性的信任量化模型

可信网络中的信任关系模型本质上是最复杂的社会关系之一,涉及假设、期望、行为和环境等多种因子,很难准确地定量表示和预测.综合考虑影响信任关系的多种可能要素,提出了一个新的基于多维决策属性的信任关系量化模型,引入直接信任、风险函数、反馈信任、激励函数和实体活跃度等多个决策属性,从多个角度推理和评估信任关系的复杂性和不确定性,用来解决传统量化模型对环境的动态变化适应能力不足的问题;在多维决策属性的融合计算过程中,通过信息熵理论确立各决策属性的分类权重,克服了过去常用的确定权重的主观判断方法,并可以改善传统方法由于主观分配分类权重而导致的模型自适应性不强的问题.模拟实验表明,与已有同类模型相比,该模型具有更稳健的动态适应性,在模型的安全性方面也有明显的优势.     

基于复合幂函数修正G1法的商户综合评价

针对多指标综合评价问题中主客观权重相悖时客观权重淹没主观权重的问题,以G1法和客观赋权法为基础,提出了复合幂函数修正G1法的组合赋权模型。首先,建立指标体系并通过G1法确定各指标主观排序和主观初始向量;然后,利用客观赋权法计算各指标客观向量;其次,在不改变主观排序的情况下利用复合幂函数算出主客观结合的综合权重;最后,利用各指标标准化后的值和综合权重计算综合评价值。采用大众点评网的商户数据进行综合评价实验：该模型的均方根误差（RMSE）为3.891,均低于G1-熵权法的8.818和标准差修正G1法的4.752,且覆盖率优于两种对比方法;分别修改主观初始向量和主观排序进行对比实验,修改主观排序的均方根误差为5.430,高于修改主观初始向量的1.17。实验结果表明,该模型得到的评价值与大众点评网官方的评分的一致性较高,且该模型弱化了主观初值对评分结果的影响,体现了主观排序的基础作用。     

基于马尔可夫链的可信软件动态评估模型

软件可信度评估受主观经验、主观决策及有限理性的影响,评估结果的精确性和客观性无法得到全面保证。为了解决上述问题,本文利用马尔科夫连,以经典可信软件属性指标体系为基础,从软件层次属性的动态变化考虑提出了一种时间情境下的动态评估模型。通过模糊综合评判和层次分析法进行量化以确定子属性的权重和元属性的相对权重,应用马尔科夫过程构建各层级属性的内部定向转化概率模型,通过模糊算法得出最终的可信评估结果。结论表明,本文构建的模型,在一定程度上更加符合软件应用的实际情形,在逻辑上更具有说服力。实例也说明了本模型的科学性和有效性。     

基于大数据的电网设备供应链溯源管理评价研究

为了加强电网设备质量源头治理,解决设备质量可追溯性问题,实现贯穿设备全寿命周期的全程质量管控。本文从大数据理念出发,首先建立了基于大数据的电网设备供应链溯源管理评价框架模型,指明了电网设备供应链溯源管理评价的技术路线,其次设计采用网络爬虫技术对散落的海量数据进行爬取和处理,从而提取电力设备供应商的信息,然后构建了包含5个一级指标、8个二级指标和两个维度的三级指标的电力物资供应商评价指标体系,分析了指标间的相互关系,最后对如何合理配指标权重进行了探索,对主观赋权法、客观赋权法和模糊综合评价三种权重确定方法进行了对比分析,从而搭建了具有追溯性的电网设备供应链溯源管理评价模型,为电网企业进行供应商的评价优化提供借鉴。     

一类考虑阶段赋权的多阶段三端点区间数型群决策方法

基于区间数相离度理论和熵值理论, 探讨了一类多阶段多属性三端点区间数型群决策中的动态属性权重、动态专家权重和阶段权重问题, 提出了多阶段属性权重确定方法和阶段内专家权重的计算方法. 计算出属性权重、阶段内专家权重和阶段权重, 并利用区间数贴近度方法生成最终的群决策方案排序. 应用实例分析结果表明, 所提出的决策方法具有较好的可行性和合理性.

     

基于改进的粗糙集与AHP法的组合权重确定方法

为了更好地发挥主观赋权法与客观赋权法这两类评价方法的优势,但又要避免这两类评价方法各自的缺陷,达到两者优势互补、扬长避短的目的,利用层次分析法(AHP)来计算出主观权重,经过改进的粗糙集(RS)法来计算出客观权重,然后通过建立最优模型给出一种可以综合专家经验与客观数据的组合权重计算方法,进而可以确定出主观权重与客观权重相结合的最优组合权重.最后通过对一个实例的应用,表明了该方法是科学、有效的,可以使决策的准确性得到提高,具备实用性与推广价值.     

基于冲突主体不确定证据融合的灰靶决策方法

研究基于冲突利益主体不完全确定权重信息情景下的群决策方法.把不完全确定的权重信息作为证据,建立了所有证据之间的距离最小测算模型,提出了不完全确定权重数值上、下限范围估计模型;为解决冲突证据的融合问题,提出了基于证据相似性的证据协调加权因子;建立了基于灰靶决策的靶心距分布范围确定模型,以排定方案优劣顺序.最后基于该方法探讨了某棕地开发方案的评价应用.     

基于经验数据评价的非参数系统分析方法

针对以往权重确定型评价方法中存在权重确定困难、忽视指标个性差异等弱点,以及传统数据包络分析方法难于评价非效率问题,给出了一种基于样本评价决策单元整体绩效的非参数方法,构造了相应的数学模型,并对模型的含义、模型性质以及模型的求解方法进行了分析.同时探讨了该方法在决策单元的有效性度量与排序、决策单元的无效原因分析中的应用.最后,应用该方法分析了中国西部地区工业企业经济效益状况.     

Optimal input/output reduction in production processes

While conventional Data Envelopment Analysis (DEA) models set targets for each operational unit, this paper considers the problem of input/output reduction in a centralized decision making environment. The purpose of this paper is to develop an approach to input/output reduction problem that typically occurs in organizations with a centralized decision-making environment. This paper shows that DEA can make an important contribution to this problem and discusses how DEA-based model can be used to determine an optimal input/output reduction plan. An application in banking sector with limitation in IT investment shows the usefulness of the proposed method.     

Fuzzy dynamic multi-objective Data Envelopment Analysis model

Data Envelopment Analysis (DEA) is a managerial powerful tool to evaluate the relative efficiency of each decision making unit (DMU). Nowadays, multi-objective DEA models in static environment are an attractive technique for evaluation quantity and quality aspects of performance analysis because there is some weakness in single objective DEA such as one-dimensional performance analysis and also it is important to consider the decision maker(s) preference over the potential adjustments of various inputs and outputs when DEA is employed. In this paper, a fuzzy dynamic multi-objective DEA model is presented in which data are changing sequentially. This paper assesses the performance of the railways using presented model as a numerical example to evaluate the results of the model. Results indicate that the multiple objective program model improves discriminating power of classical DEA models with just one time calculation of the efficiency achievement for all DUMs.     

Common weights data envelopment analysis with uncertain data: A robust optimization approach

One of the primary issues on data envelopment analysis (DEA) models is the reduction of weights flexibility. There are literally several studies to determine common weights in DEA but none of them considers uncertainty in data. This paper introduces a robust optimization approach to find common weights in DEA with uncertain data. The uncertainty is considered in both inputs and outputs and a suitable robust counterpart of DEA model is developed. The proposed robust DEA model is solved and the ideal solution is found for each decision making units (DMUs). Then, the common weights are found for all DMUs by utilizing the goal programming technique. To illustrate the performance of the proposed model, a numerical example is solved. Also, the proposed model of this paper is implemented by using some actual data from provincial gas companies in Iran.     

一种基于灰色关联分析和超效率DEA的MCDM模型

针对多属性决策问题（MCDM）权重难以客观确定的缺陷,提出一种基于灰色关联分析和超效率数据包络分析（DEA）的混合算法研究MCDM。对MCDM进行建模,利用灰色关联分析计算各属性的点关联度,为了得到各属性的相对最优灰关联度并将其充分排序,利用超效率DEA改进灰色关联分析模型。该混合算法能够更加客观地确定权重向量,并且突破权重和为1的限制,使得均一的权重更加灵活;同时,可以得到各属性的相对最优灰色关联度,超效率DEA能够增强模型对灰关联度的分辨能力。通过北京市商品房空置影响因素的实例分析验证了该混合算法的有     

动态模糊形式化关系群体决策方法

群体决策问题是决策科学的核心问题之一。基于动态模糊理论,从动态角度研究群体决策问题,提出了一种动态模糊形式化关系决策方法。从个体偏好信息表达、个体偏好数据分析、个体偏好集结、方案选择和意见反馈五个阶段探讨了动态模糊群体决策模型,并通过实例验证了该模型的可行性和合理性。     

一类环形生产系统的DEA效率评价模型

环形生产系统内部的生产过程处于循环状态,针对这一不同于其他网络系统的突出特点,分析其单个动态循环阶段,建立评价单阶段环形生产过程的DEA（数据包络分析）模型;将其推广到整体循环过程,进行环形生产系统的DEA效率评价,定义了此网络决策单元的DEA有效性。通过实例证明了该模型的可行性和实用性。     

Measuring efficiencies of multi-period and multi-division systems associated with DEA: An application to OECD countries’ national innovation systems

The efficiency-oriented performance evaluation of multi-period and multi-division systems (MPMDS) becomes increasingly important for complex investment and management decisions. This paper proposes a new formulation approach for dynamic network DEA (DN–DEA) models based on system thinking to measure and decompose the overall efficiency of MPMDS. The proposed approach is general and maintains the objective property of DEA evaluation, which not only does not need the pre-specified weights to subjectively combine component efficiencies into overall efficiency, but also is applicable to both radial and non-radial measures. More attracting, it presents a weighted average decomposition of the overall efficiency score into component ones by a set of endogenous weight sets which are most favorable for the overall efficiency of the tested entire MPMDS and ensures consistency in the comparison between overall and component efficiency scores. This study makes two contributions to the existing literature. First, it not only makes the structured decision making of MPMDS possible but also helps us realize semi- and non-structural decision making from an expert and intelligent systems point of view. Second, it evaluated the innovation efficiency of OECD countries in the multi-period and multi-division context, which presents an analytical technique and some systemic evidence for national innovation investment decisions in the long run.     

Analyzing the solutions of DEA through information visualization and data mining techniques: SmartDEA framework

Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA results are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the results of basic DEA models. The paper formally shows how the results of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, SmartDEA, is designed and developed in accordance with the proposed analysis framework. The developed software provides DEA results which are consistent with the framework and are ready-to-analyze with data mining tools, thanks to their specially designed table-based structures. The developed framework is tested and applied in a real world project for benchmarking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework.     

Information tradeoffs in model building: A network routing application

An integral part of the model-building process is the modeler's choice of how much information to gather and encode in the decision model. Obtaining more detailed and accurate information enables a more precise problem representation which, in turn, leads to more effective decision making. However, acquiring extensive and accurate information entails higher costs and delays. This paper uses a network routing decision context to illustrate the tradeoff between model precision and decision effectiveness, and explores a formal decision-theoretic approach to determine an appropriate model specification that balances information gathering costs and decision quality. We propose optimal and heuristic methods for generating good information search strategies, and report computational results based on random test problems. Our results highlight the importance of simultaneously considering information costs and decision payoffs for constructing a decision model to support routing decisions. The issues raised in this paper are especially significant for modeling dynamic, real-time decision contexts where delays induced by information gathering activities could have significant economic impact.     

基于深度强化学习的车辆边缘计算任务卸载方法

车辆边缘计算允许车辆将计算任务卸载到边缘服务器,从而满足车辆爆炸式增长的计算资源需求。但是如何进行卸载决策与计算资源分配仍然是亟待解决的关键问题。并且,运动车辆在连续时间内进行任务卸载很少被提及,尤其对车辆任务到达随机性考虑不足。针对上述问题,建立动态车辆边缘计算模型,描述为7状态2动作空间的Markov决策过程,并建立一个分布式深度强化学习模型来解决问题。另外,针对离散—连续混合决策问题导致的效果欠佳,将输入层与一阶决策网络嵌套,提出一种分阶决策的深度强化学习算法。仿真结果表明,所提算法相较于对比算法,在能耗上保持了较低水平,并且在任务完成率、时延和奖励方面都具备明显优势,这为车辆边缘计算中的卸载决策与计算资源分配问题提供了一种有效的解决方案。