Multi-attribute decision making model based on optimal membership and relative entropy

To study the problems of multi-attribute decision making in which the attribute values are given in the form of linguistic fuzzy numbers and the information of attribute weights are incomplete, a new multi-attribute decision making model is presented based on the optimal membership and the relative entropy. Firstly, the definitions of the optimal membership and the relative entropy are given. Secondly, for all alternatives, a set of preference weight vectors are obtained by solving a set of linear programming models whose goals axe all to maximize the optimal membership. Thirdly, a relative entropy model is established to aggregate the preference weight vectors, thus an optimal weight vector is determined. Based on this optimal weight vector, the algorithm of deviation degree minimization is proposed to rank all the alternatives. Finally, a decision making example is given to demonstrate the feasibility and rationality of this new model.

Multi-attribute Decision Making Method Based on the Attribute Difference Degree and Its Application in Rice Breeding

[Objective]The aim was to establish a multi-attribute decision making method and introduce its application in rice breeding.[Method]Based on the defined closeness degree among attributes,the difference degrees among attributes were discussed.Furthermore,the weights of attributes were determined based on the difference degrees among the attributes.[Result]A multi-attribute decision making method based on difference degrees among attributes was established,the feasibility of applying it in rice breeding was also analyzed.[Conclusion]This study enriched the methods to determine attribute weights in multi-attribute decision making and provided the necessary theoretical support for selecting rice varieties scientifically and rationally.

Multi-attribute decision making method for air target threat evaluation based on intuitionistic fuzzy sets

The function of the air target threat evaluation （TE） is the foundation for weapons allocation and senor resources management within the surface air defense. The multi-attribute evaluation methodology is utilized to address the issue of the TE in which the tactic features of the detected target are treated as evaluation attributes. Meanwhile, the intuitionistic fuzzy set （IFS） is employed to deal with information uncertainty in the TE process. Furthermore, on the basis of the entropy weight and inclusion-comparison probability, a hybrid TE method is developed. In order to accommodate the demands of naturalistic decision making, the proposed method allows air defense commanders to express their intuitive opinions besides incorporating into the threat features of the detected target. An illustrative example is provided to indicate the feasibility and advantage of the proposed method.

A Multi-Attribute Decision Making for Investment Decision Based on D Numbers Methods

Investment decision is a traditional multi-attribute decision making (MADM) problem since it has many uncertainty factors and incomplete information such as investment value, cost, sales, etc. D numbers theory is a useful tool to deal with uncertainty factors and incomplete information. In this paper, interval number and D numbers theory are revealed in the uncertain factor and incomplete information of investment decision. The weights of uncertain factors are calculated using entropy weight method. Thus, a new MADM model for investment decision based on D numbers theory is proposed. Numerical example is used to illustrate the efficiency of the proposed method.

Entropy-based procedures for intuitionistic fuzzy multiple attribute decision making

The class of multiple attribute decision making （MADM） problems is studied, where the attribute values are intuitionistic fuzzy numbers, and the information about attribute weights is completely unknown. A score function is first used to calculate the score of each attribute value and a score matrix is constructed, and then it is transformed into a normalized score matrix. Based on the normalized score matrix, an entropy-based procedure is proposed to derive attribute weights. Furthermore, the additive weighted averaging operator is utilized to fuse all the normalized scores into the overall scores of alternatives, by which the ranking of all the given alternatives is obtained. This paper is concluded by extending the above results to interval-valued intuitionistic fuzzy set theory, and an illustrative example is also provided.

An entropy-based multi-criterion group decision analysis method of selecting the optimum weak-subgrade treatment

Proper treatment of weak subgrade soil is very important to building a highway of good quality. We proposed an entropy-based multi-criterion group decision analysis method for a group of experts to evaluate alternatives of weak subgrade treatment, with an aim to select the optimum technique which is technically, economically and socially viable. We used fuzzy theory to analyze multiple experts＇ evaluation on various factors of each alterative treatment. Different experts＇ evaluations are integrated by the group eigenvalue method. An entropy weight is introduced to minimize the negative influences of subjective human factors of experts. The optimum alternative is identified with ideal point diseriminant analysis to calculate the distance of each alternative to the ideal point and prioritize all alternatives according to their distances. A case study on a section of the Shiman Expressway verified that the proposed method can give a rational decision on the optimum method of weak subgrade treatment.

Using Mathematical Models in Decision Making Methodologies to Find Key Nodes in the Noordin Dark Network

A Dark Network is a network that cannot be accessed through tradition means. Once uncovered, to any degree, dark network analysis can be accomplished using the SNA software. The output of SNA software includes many measures and metrics. For each of these measures and metric, the output in ORA additionally provides the ability to obtain a rank ordering of the nodes in terms of these measures. We might use this information in decision making concerning best methods to disrupt or deceive a given dark network. In the Noordin Dark network, different nodes were identified as key nodes based upon the metric used. Our goal in this paper is to use methodologies to identify the key players or nodes in a Dark Network in a similar manner as we previously proposed in social networks. We apply two multi-attribute decision making methods, a hybrid AHP & TOPSIS and an average weighted ranks scheme, to analyze these outputs to find the most influential nodes as a function of the decision makers’ inputs. We compare these methods by illustration using the Noordin Dark Network with seventy-nine nodes. We discuss sensitivity analysis that is applied to the criteria weights in order to measure the change in the ranking of the nodes.

Evaluation of building energy demand forecast models using multi-attribute decision making approach

With the existence of several conventional and advanced building thermal energy demand forecast models to improve the energy efficiency of buildings,it is hard to find an appropriate,convenient,and efficient model.Evaluations based on statistical indexes(MAE,RMSE,MAPE,etc.)that characterize the accuracy of the forecasts do not help in the identification of the efficient building thermal energy demand forecast tool since they do not reflect the efforts entailed in implementation of the forecast model,i.e.,data collection to production/use phase.Hence,this work presents a Gini Index based Measurement of Alternatives and Ranking according to COmpromise Solution(GI-MARCOS),a hybrid Multi Attribute Decision Making(MADM)approach for the identification of the most efficient building energy demand forecast tool.GI-MARCOS employs(i)GI based objective weight method:assigns meaningful objective weights to the attributes in four phases(1:pre-processing,2:implementation,3:post-processing,and 4:use phase)thereby avoiding unnecessary biases in the expert’s opinion on weights and applicable to domains where there is a lack of domain expertise,and(ii)MARCOS:provides a robust and reliable ranking of alternatives in a dynamic environment.A case study with three alternatives evaluated over three to six attributes in four phases of implementation(pre-processing,implementation,post-processing and use)reveals that the use of GI-MARCOS improved the accuracy of alternatives MLR and BM by 6%and 13%,respectively.Moreover,additional validations state that(i)MLR performs best in Phase 1 and 2,while ANN performs best in Phase 3 and 4 with BM providing a mediocre performance in all four phases,(ii)sensitivity analysis:provides robust ranking with interchange of weights across phases and attributes,and(iii)rank correlation:ranks produce by GI-MARCOS has a high correlation with GRA(0.999),COPRAS(0.9786),and ARAS(0.9775).

Nonempirical hybrid multi-attribute decision-making method for design for remanufacturing

Design for remanufacturing(DfRem)is the process of considering remanufacturing characteristics during product design in order to reduce the number of issues during the remanufacturing stage.This decisionmaking in DfRem is influenced by the designers'subjective preferences owing to a lack of explicitly defined remanufacturing knowledge for designers,which can lead to indecisive design schemes.In order to objectively select the optimal design scheme for remanufacturing,a nonempirical hybrid multi-attribute decision-making method is presented to alleviate the impacts of subjective factors.In this method,design characteristics and demand information are characterized through the matter-element theory.Coupled with design principles,some initial design schemes are proposed.Evaluation criteria are established considering the technical,economic,and environmental factors.The entropy weight and vague set are used to determine the optimal design scheme via a multi-attribute decision・making approach.The design of a bearing assembly machine for remanufacturing is taken as an example to illustrate the practicality and validity of the proposed method.The results revealed that the proposed method was effective in the decision-making of DfRem.

基于DEMATEL-Entropy法的城市轨道交通应急预案评价方法研究

在城市轨道交通应急预案评价过程中,往往存在评价指标主观性过强以及评价结果难以量化等问题,因此文中提出了决策实验室法(DEMATEL法)和熵权法(Entropy法)相结合的评价方法。首先建立由4个准则层和12个指标层组成的城市轨道交通应急预案评价指标体系,然后使用DEMATEL法和Entropy法求得指标的主观权重和客观权重,运用博弈论计算综合权重,再通过模糊综合评价法计算待评预案的最终得分和评级等级。最后,选取城市轨道交通某应急预案对模型的可行性进行了验证,为预案评价提供了新思路。

Multi-attribute decision making with Pythagorean fuzzy sets via conversions to intuitionistic fuzzy sets and ORESTE method

The aim of this paper is to study the conversions between Pythagorean fuzzy sets and Atanassov’s intuitionistic fuzzy sets.Besides,an ORESTE method based on multi-attribute decision making with Pythagorean fuzzy sets is developed by utilising the developed conversions.In this paper,according to the geometric representations of Pythagorean fuzzy sets and Atanassov’s intuitionistic fuzzy sets,two types of conversions between the two fuzzy sets are constructed,which are further used to derive information measures include entropy and cross-entropy measures of Pythagorean fuzzy sets.Then,by combining with the ORESTE method,a direct decision procedure for multi-attribute decision making with Pythagorean fuzzy information is developed.Finally,a numerical example of the evaluation of regional energy efficiency is shown to illustrate the feasibility and validity of the developed decision procedure.

基于组合赋权云模型的塔台管制系统运行安全评估

随着我国民航业的迅猛发展,空中交通流量不断攀升,机场塔台管制系统的运行安全面临严峻考验,因此,正确识别和评估风险成为有效提高塔台管制系统运行安全水平的重要前提。为精准且有效地完成对塔台管制系统运行的安全评估,构建了基于模糊决策实验室分析法(Decision-Making Trial and Evaluation Laboratory,DEMATEL)网络层次分析法(Analytic Network Process,ANP)熵权法云模型的塔台管制系统运行安全评估模型。首先,通过流程图法并结合人机环管的思想建立塔台管制系统运行安全评估指标体系,并采用模糊DEMATEL法确定评估指标间的相互影响关系,绘制评估指标之间的网络结构图;然后,通过ANP法确定各评估指标的主观权重,采用熵权法确定各评估指标的客观权重,并通过博弈论方法计算评估指标的综合权重;最后,将权重结果与评语层云模型结合使用对各级安全评估指标进行安全评估,通过MATLAB软件完成算例的仿真分析。结果表明:该塔台管制系统运行安全等级在一般和良好之间,仍需采取有效措施对某些风险因素进行控制;同时验证了该模型在塔台管制系统运行安全评估中具有可行性和准确性,对塔台管制系统安全评估具有指导意义。

基于多准则决策方法的避难场所选址模型研究

针对避难场所规划选址中的现实问题与需求,从方法易操作和可实施角度出发,构建了面向城市规划业务流程与工作实践的防灾避难场所选址模型。该模型基于多准则决策方法(MCDM),以避难场所选址的安全性和均好性为主要目标。模型综合考虑了人口密度、建筑质量、交通系统和避难场所设施配置四项选址影响因素,并采用AHP-熵权法确定影响要素权重。以江阴市为实证案例,套用模型经过两轮评估及编码者测试,最终明确了备选避难场所资源的优先建设排序,验证了模型的可行性与实用性。研究提出的影响要素和量化指标易于取得,打分方法便于操作,适用于城市规划阶段的避难场所规划选址与建设时序决策。

基于DEMATEL和熵权法的生鲜食品网购决策影响因素研究

随着电子商务生活化的普及,生鲜行业也日趋电商化。生鲜电商企业面对日益增长的市场规模的同时也面临着日益激烈的行业竞争。生鲜电商企业通过识别客户购买决策的影响因素并进行重要性排序,可以有的放矢地改进工作,进而可以提高消费者完成购买的转化率、促进商家的商品销量。通过构建影响客户网购生鲜食品决策的指标体系,采用DEMATEL方法确定各影响要素的地位及其因果关系。为了克服DEMATEL法专家评价主观的局限性,使用客观赋权的熵权法计算出熵权值,利用乘法合成法进行组合赋权,通过组合权重排序分析得出电商平台信誉度、电商平台知名度、售后服务完善度、电商平台管理规范度、生鲜食品品质、生鲜食品品牌知名度、配送的保鲜度、配送快捷性是影响消费者网购生鲜食品决策的重要因素。因此,生鲜电商商家可以从选择优质的第三方平台、优化店铺服务流程、建立生鲜食品质量保证体系等方面加以改进,进一步提升网络消费者生鲜食品的购买转化。

基于轨道站点的接驳公交服务质量评价--以重庆市南岸区接驳公交为例

城市轨道交通的大力建设使居民出行更加便捷,接驳公交可有效实现城市居民公交与轨道的换乘目的,其公交服务质量关乎乘客出行品质,体现城市道路交通发展整体水平。以重庆市南岸区轨道铜元局站324、3103接驳公交为研究对象,从乘客感知服务出发,构建便捷性、可靠性、舒适性、文明性等4个准则层组成的线路服务质量评价指标体系。以调查问卷结果为基础,探寻乘客基本属性,利用熵权法确定主要指标权重,客观赋值减少主观影响,引入模糊决策法求得乘客满意度,加权SERVQUAL模型计算乘客服务质量分值,两种方法综合评价分析使之更全面、精准,进而对比分析两条线路差异。结果表明:乘客尤为看中接驳公交舒适性,且3103线路总体服务质量评价优于324线路,最终针对各角度提出建议对策,为城市公交管理者、规划者提供科学依据。

曳引电梯部件故障相关性分析

针对电梯部件之间的故障影响,提出将决策试验与评价实验室法和解释结构模型结合建立电梯部件故障层次结构模型,并改进直接影响矩阵的确定方式。首先从多方面获取电梯部件故障关联评价,其次使用熵权法确定评价的权重并计算直接影响矩阵,最后使用建立的电梯部件故障相关模型得到电梯部件故障因果关系和故障传递规则。结果表明:电动机、制动器、限速器为强原因部件,曳引钢丝绳、轿厢以及导向系统的部件为强结果部件;电梯部件故障关联规则体现为同层级或跨层级传递。通过分析部件故障机理,所提方法得到的部件故障传递规则与电梯系统实际运行情况相符。

一种基于RCM的矿井提升机维修方式决策方法

针对矿井提升机因复杂环境和长期运行导致的故障和隐患问题,提出了一种基于RCM的故障诊断方法,用于指导维修方式的决策。首先,采用层次分析法和熵权法相结合的组合赋权法,对矿井提升机的各项指标进行权重分析;其次,运用灰色关联分析法进行维修方式的决策。实验结果表明,该方法能够为矿井提升机的维修方式提供科学有效的决策支持。

基于群体决策熵优化-CRITIC算法评价灌溉水源适宜性

干旱半干旱地区缺乏针对性的灌溉水源适宜性评价方法,且评价方法适用性不强,指标较为单一,无法运用至多种水源,故以干旱半干旱地区为研究对象,探究能适用于不同灌溉水源适宜性的评价方法。综合考虑灌溉水源可供应量、灌溉水供应及时程度、提水能耗费、灌溉水温等因素构建灌溉水源适宜性评价指标体系,引入群体决策熵优法-CRITIC算法确定评价指标的组合权重,以宁夏平罗县3种可利用的灌溉水源为典型案例,结合构建的评价指标体系及权重确定方法,采用TOPSIS法对3种不同水源4-9月的灌溉适宜性进行综合评价。平罗县黄河水与排水4-9月的贴近程度值皆大于0.31,均适宜灌溉。地下水4月的贴近程度值仅为0.2412,不适宜灌溉,但地下水5-8月的贴近程度值皆大于0.31,适宜灌溉,与宁夏银北灌区实际情况相符。构建的评价指标体系和引入的组合权重确定方法(群体决策熵优化-CRITIC算法)适用于干旱半干旱区的灌溉水源适宜性评价。

基于加权复杂度的SMOTE算法及其在软件缺陷预测中的应用

近年来,SMOTE被广泛应用于软件缺陷预测中不平衡数据的处理。然而,现有的SMOTE算法普遍忽视了不同样本的复杂度存在很大差异这一问题。事实上,在缺陷预测时样本的复杂度与其是否具有缺陷之间存在着密切的联系,因此,在进行过采样时,有必要利用样本的复杂度来辅助新样本的合成,从而提高缺陷预测的性能。如何度量样本的复杂度非常重要,论文在计算样本复杂度时充分考虑到每一个条件属性的权重,从而得到一种加权复杂度的概念。基于加权复杂度,提出一种新的SMOTE算法——WCP-SMOTE,并将其应用于软件缺陷预测。WCP-SMOTE算法首先利用粗糙集中的粒度决策熵来计算决策表中每个条件属性的重要性和权重;其次,通过对样本在所有属性上的取值进行加权求和,从而得到该样本的加权复杂度;第三,根据加权复杂度对少数类样本进行升序排序,并从头到尾对相邻的两个少数类样本求平均来不断地合成新的样本,直到获得一个平衡的数据集。在多个缺陷预测数据集上的实验表明,利用WCP-SMOTE算法来处理不平衡数据能够获得更好的软件缺陷预测性能。

融合功能性副语言比例系数的语音情感识别

语言中的非言语发声如笑声、叹息、抽泣等,称为功能性副语言,对情感表达起重要作用,但现有研究很少考虑多种功能性副语言在一种情感中的协同作用.针对该问题,提出了融合功能性副语言比例系数(functional paralanguage proportion coefficient,FPPC)的情感识别系统.首先,提取能体现多种功能性副语言在情感语句中出现的频率快慢和持续时间长短的FPPC特征;然后,搭建基于注意力机制的集成学习(attention stacking)为不同的基分类器赋予不同权重,并对FPPC特征进行训练;最后,通过自适应熵权重决策融合方法将传统语音情感识别与基于FPPC特征情感识别进行融合.实验结果显示,融合了FPPC特征后的情感识别结果提高了16.84%,证明融合FPPC特征能有效提高系统整体识别率.