Method for multiple attribute decision making based on incomplete linguistic judgment matrix

With respect to the multiple attribute decision making problems with linguistic preference relations on alternatives in the form of incomplete linguistic judgment matrix, a method is proposed to analyze the decision problem. The incomplete linguistic judgment matrix is transformed into incomplete fuzzy judgment matrix and an optimization model is developed on the basis of incomplete fuzzy judgment matrix provided by the decision maker and the decision matrix to determine attribute weights by Lagrange multiplier method. Then the overall values of all alternatives are calculated to rank them. A numerical example is given to illustrate the feasibility and practicality of the proposed method.

Attribute Reduction in Decision Systems Based on Relation Matrix

This paper proposes,from the viewpoint of relation matrix,a new algorithm of attribute reduction for decision systems.Two new and relative reasonable indices are first defined to measure significance of the attributes in decision systems and then a heuristic algorithm of attribute reduction is formulated.Moreover,the time complexity of the algorithm is analyzed and it is proved to be complete.Some numerical experiments are also conducted to access the performance of the presented algorithm and the results demonstrate that it is not only effective but also efficient.

Study on Extracting Information about Settlements Distribution in Western Jilin Province Based on Decision Tree Model

With western Jilin Province as the study region, spectral characteristics and texture features of remote sensing images were taken as the classification basis to construct a Decision Tree Model and extract information about settlements in western Jilin Province, and the manually-extracted information about settlements in western Jilin Province was evaluated by confusion matrix. The results showed that Decision Tree Model was convenient for extracting settlements information by integrating spectral and texture features, and the accuracy of such a method was higher than that of the traditional Maximum Liklihood Method, in addition, calculation methods of extracting settlements information by this mean were concluded.

Decision tree and deep learning based probabilistic model for character recognition

One of the most important methods that finds usefulness in various applications, such as searching historical manuscripts, forensic search, bank check reading, mail sorting, book and handwritten notes transcription, is handwritten character recognition. The common issues in the character recognition are often due to different writing styles, orientation angle, size variation(regarding length and height), etc. This study presents a classification model using a hybrid classifier for the character recognition by combining holoentropy enabled decision tree(HDT) and deep neural network(DNN). In feature extraction, the local gradient features that include histogram oriented gabor feature and grid level feature, and grey level co-occurrence matrix(GLCM) features are extracted. Then, the extracted features are concatenated to encode shape, color, texture, local and statistical information, for the recognition of characters in the image by applying the extracted features to the hybrid classifier. In the experimental analysis, recognition accuracy of 96% is achieved. Thus, it can be suggested that the proposed model intends to provide more accurate character recognition rate compared to that of character recognition techniques used in the literature.

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.

Vibration Based Tool Insert Health Monitoring Using Decision Tree and Fuzzy Logic

The productivity and quality in the turning process can be improved by utilizing the predicted performance of the cutting tools.This research incorporates condition monitoring of a non-carbide tool insert using vibration analysis along with machine learning and fuzzy logic approach.A non-carbide tool insert is considered for the process of cutting operation in a semi-automatic lathe,where the condition of tool is monitored using vibration characteristics.The vibration signals for conditions such as heathy,damaged,thermal and flank were acquired with the help of piezoelectric transducer and data acquisition system.The descriptive statistical features were extracted from the acquired vibration signal using the feature extraction techniques.The extracted statistical features were selected using a feature selection process through J48 decision tree algorithm.The selected features were classified using J48 decision tree and fuzzy to develop the fault diagnosis model for the improved predictive analysis.The decision tree model produced the classification accuracy as 94.78%with five selected features.The developed fuzzy model produced the classification accuracy as 94.02%with five membership functions.Hence,the decision tree has been proposed as a suitable fault diagnosis model for predicting the tool insert health condition under different fault conditions.

Group decision-making method based on entropy and experts cluster analysis

According to the aggregation method of experts＇ evaluation information in group decision-making,the existing methods of determining experts＇ weights based on cluster analysis take into account the expert＇s preferences and the consistency of expert＇s collating vectors,but they lack of the measure of information similarity.So it may occur that although the collating vector is similar to the group consensus,information uncertainty is great of a certain expert.However,it is clustered to a larger group and given a high weight.For this,a new aggregation method based on entropy and cluster analysis in group decision-making process is provided,in which the collating vectors are classified with information similarity coefficient,and the experts＇ weights are determined according to the result of classification,the entropy of collating vectors and the judgment matrix consistency.Finally,a numerical example shows that the method is feasible and effective.

A multi-objective decision-making method for the treatment scheme of landslide hazard

The treatment engineering of landslide hazard is a complicated systemengineering. The selecting treatment scheme is influenced by many factors such as technology,economics, environment, and risk. The decision-making of treatment schemes of landslide hazard is aproblem of comprehensive judgment with multi-hierarchy and multi-objective. The traditional analysishierarchy process needs identity test. The traditional analysis hierarchy process is improved bymeans of optimal transfer matrix here. An improved hierarchy decision-making model for the treatmentof landslide hazard is set up. The judgment matrix obtained by the method can naturally meet therequirement of identity, so the identity test is not necessary. At last, the method is applied tothe treatment decision-making of the dangerous rock mass at the Slate Mountain, and its applicationis discussed in detail.

Attribute reduction algorithm based on discernibility for decision table

The attribute reduction algorithms of decision table based on discernible matrix are required to construct discernible matrix, which reduces efficiency of algorithms. In this paper, the relationship between attribute discernible matrix and its discernibility is first established for general information systems. Based on the idea that the equivalent discernible matrix has a same attribute reduction, existing matrices are modified and a formula of attribute discernibility associated with algebraic reduction for decision table is proposed. A heuristic attribute reduction algorithm based on attribute discernibility is presented. Experimental results indicate that the algorithm can more easily explore an optimal or sub-optimal reduction, and is efficient.

Use of Decision Theory to Predict Dust Storms over New Delhi, India

The north and northwest parts of India experience dust/sandstorms during the pre-monsoon season (April to May). We studied dust storms occurring over New Delhi, India (2001 to 2012) to develop a probabilistic forecast method. A probabilistic forecast method is discussed in this paper as a decision making tool that can be used to meet the needs of the users. The application of decision theory to forecast an event is that the end user of the forecast takes a decision for action on the basis of each forecast. This study stems from an elementary decision theory based on three interlocking procedures to follow viz. 1) identification of meteorological parameters responsible for dust storms, 2) determining the impact of each meteorological parameter in the initiation of a dust storm and 3) finally using the first two steps an action is recommended. Among the meteorological parameters, temperature, wind speed, pressure, number of sunny hours and evaporation had a positive impact on dust storm occurrence as compared to other variables selected. Using the concept of utility, which is an integral part of decision theory, a decision matrix is constructed. This decision matrix contains the threshold value above which a dust storm has occurred followed by each state of weather and the course of action. Thus, in this paper, a different concept of forecasting is discussed and optional rules for decision making based on the availability of a limited amount of meteorological data are presented. This forecast is of the very short range (0 - 3 hours) based on the meteorological conditions just prior to the occurrence of a dust event. We validated our findings with the OMI Aerosol index obtained from AERONET.

Improved Fuzzification Method for Multi-Objective Decision-Making and Its Application in Evaluation of Highway Planning

A new fuzzification method for multi-objective decision-making and selective sorting is proposed on the basis of the fuzzy consistent relation, and the specific algorithm is presented. The method is applied to the evaluation of highway planning of Zhanjiang city. To decrease the subjectivity in the process of decision-making, the LOWA operator is introduced, and a discussion on how to select appropriate weights involved in multi-objective sorting is made. It is concluded that it is feasible to apply the fuzzy consistent relation to multi-objective decision-making analysis, and the improved fuzzication method is workable.

基于D-I-M协同模型的企业数字化转型财务管理风险识别研究

数字化正在重塑经济发展的轨道,成为赋能经济增长新动力。精准识别财务管理风险中的关键指标及其相互关系,对企业更好地完成数字化转型尤为关键。融合企业人力、运营、财务、环境4个基本维度,从链式传导视角构建转型背景下企业财务管理风险指标体系。通过决策试验与评价实验室(DEMATL)法,分析各影响因素之间中心度和原因度关系,并利用解释结构模型(ISM),将矩阵进一步分析得到影响因素的层级划分,归纳影响因素之间的内在指向关系,最后运用交叉影响矩阵相乘(MICMAC)法计算各因素的驱动—依赖关系以判断其属性类别。提出通过D-I-M协同分析的方法,识别财务管理风险路径,为企业进行多链融合数字化转型、规避财务管理风险提供参考。

加权区间值犹豫模糊集的群决策方法

为了在区间值犹豫模糊集(IVHFS)中体现出决策专家的重要性,把所有评审专家的综合权重注入IVHFS中,进而定义了加权区间值犹豫模糊集(WIVHFS).将WIVHFS用三维点坐标(由区间值隶属度的中位数、区间值隶属度的清晰度以及评审专家综合权重构成)进行刻画,在此基础上给出了加权区间值犹豫模糊元(WIVHFE)的相关运算;基于WIVHFE的相关运算采用熵值法计算测评准则客观权重,利用0-1优先关系矩阵来获取任意2个备选方案的总测评准则权重优势值与总测评准则权重劣势值,并依据二者之间的大小排序方案.最后,通过一个具体案例说明了文中理论与方法的可行性.

基于属性值变化的动态三支冲突分析

冲突分析有利于信息系统的问题处理。为了促进相关动态学习,将冲突分析模型推广到动态形式信息系统中,并结合三支决策思想研究基于属性值变化的动态三支冲突分析。首先,考虑单行属性值变化,研究距离矩阵的变化,得到关于冲突集、中立集、联合集的三支分析模型的变化结果。其次,类似考虑多行属性值变化,得到对应的三支分析模型的变化规律,并由此设计相关的动态更新算法。进而,针对列属性值变化,基于形式信息系统来转换成行属性值变化,从而调用行值动态更新方法。最后,实例分析说明了所得动态三支冲突分析的性质与算法的有效性。

基于前景提取与多特征决策融合的电力线触树放电痕迹识别

针对电力线路智能化巡检中难以准确辨识电力线触碰树木遗留痕迹,无法为事故防治和责任划分提供可靠依据的难题,提出一种基于前景区域提取与多特征决策融合的电力线放电痕迹识别方法。首先,搭建中压线路触树放电实验平台,采集导线表面放电遗留痕迹,构建图像集。然后,提出融合纹理稀疏性与对数变换的去阴影算法,消除阴影区干扰;进一步提出融合全局阈值和自适应阈值分割的目标初始框选取策略,解决了GrabCut算法中初始框无法自动确定的问题,实现复杂背景下导线痕迹的自动精准分割。最后,提取导线的纹理和颜色特征,通过多SVM决策融合实现导线痕迹识别。实验结果表明,所提方法平均识别准确率达到88.39%,证明了所提识别方法的有效性。

基于测试代价的三支邻域属性约简算法

针对粗糙集属性约简时很少考虑属性自身的测试代价等问题,提出了一种基于测试代价的三支邻域属性约简算法。算法根据各属性在邻域分辨矩阵中出现的频次和比例来计算属性重要性,并结合属性自身的测试代价来构造性价比指标,以此指导属性的甄选。三支决策方法被用于划分属性集,为属性的约简处理提供数据支撑。在7个UCI公共数据集上进行对比实验,结果表明,该算法可得到比对比算法更小的属性约简集合,在分类精度不降低的情况下,该算法具有更少的运行时间和更小的测试代价。基于财政收入的预测应用实例进一步证明了所提算法的有效性和实用性。

基于模糊测度的模糊分支时态逻辑模型检测

针对具有模糊性和不确定性的复杂系统的验证问题,提出一种基于模糊测度的模糊分支时态逻辑模型检测算法。首先,在模糊决策过程模型的基础上引入模糊分支时态逻辑的语法和语义。然后,给出模糊分支时态逻辑模型检测算法,该算法将模型检测问题转化为矩阵运算,具有计算方式简洁、复杂度较低的优点。最后,通过医疗专家系统的实例说明了该模型检测算法的有效性。

基于正域向量的决策粗糙集属性约简

在决策粗糙集模型中,现有划分层的正域都是通过集合求并所得,但基于该方法的保正域不变的属性约简与基于差别矩阵方法所得约简结果并不一致。提出了一种基于正域向量的决策粗糙集属性约简方法,该方法与基于差别矩阵的约简方法所得结果是一致的。最后给出一个算例说明其一致性。

Multi-criteria decision-making method for evaluation of investment in enhanced geothermal systems projects

Deep geothermal energy presents large untapped renewable energy potential could significantly contribute to global energy needs. However, developing geothermal projects involves uncertainties regarding adequate geothermal brine extraction and huge costs related to preparation phases and consequently drilling and stimulation activities. Therefore, evaluating utilization alternatives of such projects is a complex decision-making problem effectively addressed using multi-criteria decision-making (MCDM) methods. This study introduces the MCDM method utilizing analytic hierarchy process (AHP) and weighted decision matrix (WDM) to assess different utilization alternatives (electricity generation, direct heat use and cogeneration). The AHP method determines the weight of each criterion and sub-criterion, while the WDM calculates the final project grade. Five criteria groups - technological, geological, economic, societal and environmental – comprising twenty-eight influencing factors were selected and used for the assessment of investment in Enhanced Geothermal Systems (EGS) projects. The AHP-WDM method was used by 38 experts from six categories: industry, educational institution, research and technology organization (RTO), small- and medium-sized enterprises (SME), local community and other. These diverse expert inputs aimed to capture varying perspectives and knowledge influence investment decisions in geothermal energy. The results were analysed accordingly. The results underscore the importance of incorporating different viewpoints to develop robust, credible, and effective investment strategies for EGS projects. Therefore, this method will contribute to more efficient EGS project development, enabling thus a greater penetration of the EGS into the market. Additionally, the proposed AHP-WDM method was implemented for a case study examining two locations. Locations were assessed and compared on scenario-based evaluation. The results confirmed the method's adequacy for assessing various end uses and comparing project feasibility across different locations.

An Evaluation of the Critical Success Factors in Sustainable Food Supply Chains in Developing Countries

Food is one of the biggest industries in developed and underdeveloped countries. Supply chain sustainability is essential in established and emerging economies because of the rising acceptance of cost-based outsourcing and the growing technological, social, and environmental concerns. The food business faces serious sustainability and growth challenges in developing countries. A comprehensive analysis of the critical success factors (CSFs) influencing the performance outcome and the sustainable supply chain management (SSCM) process. A theoretical framework is established to explain how they are used to examine the organizational aspect of the food supply chain life cycle analysis. This study examined the CSFs and revealed the relationships between them using a methodology that included a review of literature, interpretative structural modeling (ISM), and cross-impact matrix multiplication applied in classification (MICMAC) tool analysis of soil liquefaction factors. The findings of this research demonstrate that the quality and safety of food are important factors and have a direct effect on other factors. To make sustainable food supply chain management more adequate, legislators, managers, and experts need to pay attention to this factor. In this work. It also shows that companies aiming to create a sustainable business model must make sustainability a fundamental tenet of their organization. Practitioners and managers may devise effective long-term plans for establishing a sustainable food supply chain utilizing the recommended methodology.