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Zhang, Hongjun, Chengxiang Yin, Xiuli Qi, Rui Zhang, and Xingdang Kang. "Cognitive Best Worst Method for Multiattribute Decision-Making." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/1092925.
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Pairwise comparison based multiattribute decision-making (MADM) methods are widely used and studied in recent years. However, the perception and cognition towards the semantic representation for the linguistic rating scale and the way in which the pairwise comparisons are executed are still open to discuss. The commonly used ratio scale is likely to produce misapplications and the matrix based comparison style needs too many comparisons and is not able to guarantee the consistency of the matrix when the number of objects involved is large. This research proposes a new MADM method CBWM (Cognitive Best Worst Method) which adopts interval scale to represent the pairwise difference and only compares each object to the best object and the worst object rather than all the other objects. CBWM is a vector based method which only needs 2n-3 pairwise comparisons and is more likely to generate consistent comparisons and reliable results. The theoretical analysis and a real world application demonstrate the effectiveness of CBWM.
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Larichev, O. I., H. M. Moshkovich, and S. B. Rebrik. "Systematic research into human behavior in multiattribute object classification problems." Acta Psychologica 68, no. 1-3 (September 1988): 171–82. http://dx.doi.org/10.1016/0001-6918(88)90053-4.
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Ustinovichius, Leonas. "DETERMINATION OF EFFICIENCY OF INVESTMENTS IN CONSTRUCTION." International Journal of Strategic Property Management 8, no. 1 (March 31, 2004): 25–43. http://dx.doi.org/10.3846/1648715x.2004.9637505.
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Decision making is associated with ranking problems aimed to obtain a set of preference order of solutions. People can make mistakes choosing the best object for investments. Due to high cost of such mistakes, such a choice should be well founded. A major goal of paper is to develop a theoretical basis for creating a decision support system aimed to increase building construction and reconstruction investment efficiency by applying multiattribute decision making approaches and mathematical modelling. To achieve the goal, the following problems have to be solved: to analyse new models currently used in developing investment strategies in building construction and reconstruction, to make a classification of construction investment projects and to describe the stages of determining the efficiency of construction investments, to create a family of multiattribute decision methods to be used in the analysis of investment projects in building construction and reconstruction, to create multiple attribute decision support system based on the multiattribute methods developed for determining the efficiency of construction and reconstruction investment projects.
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Qi, Jie, Tengfei Lin, Tao Zhao, Fangyu Li, and Kurt Marfurt. "Semisupervised multiattribute seismic facies analysis." Interpretation 4, no. 1 (February 1, 2016): SB91—SB106. http://dx.doi.org/10.1190/int-2015-0098.1.
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One of the key components of traditional seismic interpretation is to associate or “label” a specific seismic amplitude package of reflectors with an appropriate seismic or geologic facies. The object of seismic clustering algorithms is to use a computer to accelerate this process, allowing one to generate interpreted facies for large 3D volumes. Determining which attributes best quantify a specific amplitude or morphology component seen by the human interpreter is critical to successful clustering. Unfortunately, many patterns, such as coherence images of salt domes, result in a salt-and-pepper classification. Application of 3D Kuwahara median filters smooths the interior attribute response and sharpens the contrast between neighboring facies, thereby preconditioning the attribute volumes for subsequent clustering. In our workflow, the interpreter manually painted [Formula: see text] target facies using traditional interpretation techniques, resulting in attribute training data for each facies. Candidate attributes were evaluated by crosscorrelating their histogram for each facies with low correlation implying good facies discrimination, and Kuwahara filtering significantly increased this discrimination. Multiattribute voxels for the [Formula: see text] interpreter-painted facies were projected against a generative topographical mapping manifold, resulting in [Formula: see text] probability density functions (PDFs). The Bhattacharyya distance between the PDF of each unlabeled voxel to each of [Formula: see text] facies PDFs resulted in a probability volume of each user-defined facies. We have determined the effectiveness of this workflow to a large 3D seismic volume acquired offshore Louisiana, USA.
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Jin, Jing-Zhong, Yoshiteru Nakamori, and Andrzej P. Wierzbicki. "A Study on Multiattribute Aggregation Approaches to Product Recommendation." Advances in Fuzzy Systems 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/806749.
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In today’s increasingly competitive market, consumers usually have to face a huge number of products with different designs but having the same use. Therefore, an important problem for manufacturers is to attract consumers by special designs of the products. This paper aims at the improvement of a consumer-oriented approach in recommending products, and proposing a recommendation system for Japanese traditional crafts based on target-oriented fuzzy method and ontological engineering. Specifically, a target-oriented fuzzy method is used for measuring the fitness of a selected attribute to a certain object. Two aggregation models for dealing with a multiattribute evaluation and ranking are introduced; four ranking methods are also examined for getting a recommendation list. To test the aggregation models and the ranking methods, a recommendation system was developed and a comparison test was conducted.
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Wu, Yanhui, Wei Wang, Guowei Zhu, and Peng Wang. "Application of seismic multiattribute machine learning to determine coal strata thickness." Journal of Geophysics and Engineering 18, no. 6 (December 2021): 834–44. http://dx.doi.org/10.1093/jge/gxab054.
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Abstract The coal mining industry is developing automated and intelligent coal mining processes. Accurate determination of the geological conditions of working faces is an important prerequisite for automated mining. The use of machine learning to extract comprehensive attributes from seismic data and the application of that data to determine the coal strata thickness has become an important area of research in recent years. Conventional coal strata thickness interpretation methods do not meet the application requirements of mines. Determining the coal strata thickness with machine learning solves this problem to a large extent, especially for issues of exploration accuracy. In this study, we use seismic exploration data from the Xingdong coal mine, with the 1225 working face as the research object, and we apply seismic multiattribute machine learning to determine the coal strata thickness. First, through optimal selection, we perform seismic multiattribute extraction and optimal multiparameter selection by selecting the seismic attributes with good responses to the coal strata thickness and extracting training samples. Second, we optimise the model through a trial-and-error method and use machine learning for training. Finally, we illustrate the advantages of this method using actual data. We compare the results of the proposed model with results based on a single attribute, The results show that application of seismic multiattribute machine learning to determine coal strata thickness meets the requirements of geological inspection and has a good application performance and practical significance in complex areas.
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Ebuna, Daniel R., Jared W. Kluesner, Kevin J. Cunningham, and Joel H. Edwards. "Statistical approach to neural network imaging of karst systems in 3D seismic reflection data." Interpretation 6, no. 3 (August 1, 2018): B15—B35. http://dx.doi.org/10.1190/int-2017-0197.1.
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The current lack of a robust standardized technique for geophysical mapping of karst systems can be attributed to the complexity of the environment and prior technological limitations. Abrupt lateral variations in physical properties that are inherent to karst systems generate significant geophysical noise, challenging conventional seismic signal processing and interpretation. The application of neural networks (NNs) to multiattribute seismic interpretation can provide a semiautomated method for identifying and leveraging the nonlinear relationships exhibited among seismic attributes. The ambiguity generally associated with designing NNs for seismic object detection can be reduced via statistical analysis of the extracted attribute data. A data-driven approach to selecting the appropriate set of input seismic attributes, as well as the locations and suggested number of training examples, provides a more objective and computationally efficient method for identifying karst systems using reflection seismology. This statistically optimized NN technique is demonstrated using 3D seismic reflection data collected from the southeastern portion of the Florida carbonate platform. Several dimensionality reduction methods are applied, and the resulting karst probability models are evaluated relative to one another based on quantitative and qualitative criteria. Comparing the preferred model, using quadratic discriminant analysis, with previously available seismic object detection workflows demonstrates the karst-specific nature of the tool. Results suggest that the karst multiattribute workflow presented is capable of approximating the structural boundaries of karst systems with more accuracy and efficiency than a human counterpart or previously presented seismic interpretation schemes. This objective technique, using solely 3D seismic reflection data, is proposed as a practical approach to mapping karst systems for subsequent hydrogeologic modeling.
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Huang, Zhili, Qinglan Chen, Liu Chen, and Qinyuan Liu. "Relative Similarity Programming Model for Uncertain Multiple Attribute Decision-Making Objects and Its Application." Mathematical Problems in Engineering 2021 (March 9, 2021): 1–16. http://dx.doi.org/10.1155/2021/6618333.
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This paper is concerned with the uncertain multiattribute decision-making (UMADM) of which the attribute value is triangular fuzzy number. Firstly, the max-relative similarity degree and min-relative similarity degree of alternative decision-making objects are given based on the relative similarity degree of triangular fuzzy number, the advantage relation theories to comparative relative similarity degree of triangular fuzzy number are proposed, and some good properties, relations, and conclusions are derived. Secondly, in order to determine the attribute weight vector, a triangular fuzzy number-based decision-making object relative similarity programming model is established with the help of maximizing possibility degree algorithm rules in the cooperative game theory. Subsequently, by aggregating the comparison overall relative similarity degree values of all decision-making objects, we could pick over and sort the set of alternative objects and gather a new model algorithm for the relative similarity programming of triangular fuzzy number-based multiple attribute decision-making alternatives. Finally, an example is given to illustrate the feasibility and practicability of the model algorithm presented in this paper.
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Paganelli, Federica, and David Parlanti. "A DHT-Based Discovery Service for the Internet of Things." Journal of Computer Networks and Communications 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/107041.
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Current trends towards the Future Internet are envisaging the conception of novel services endowed with context-aware and autonomic capabilities to improve end users’ quality of life. The Internet of Things paradigm is expected to contribute towards this ambitious vision by proposing models and mechanisms enabling the creation of networks of “smart things” on a large scale. It is widely recognized that efficient mechanisms for discovering available resources and capabilities are required to realize such vision. The contribution of this work consists in a novel discovery service for the Internet of Things. The proposed solution adopts a peer-to-peer approach for guaranteeing scalability, robustness, and easy maintenance of the overall system. While most existing peer-to-peer discovery services proposed for the IoT support solely exact match queries on a single attribute (i.e., the object identifier), our solution can handle multiattribute and range queries. We defined a layered approach by distinguishing three main aspects: multiattribute indexing, range query support, peer-to-peer routing. We chose to adopt an over-DHT indexing scheme to guarantee ease of design and implementation principles. We report on the implementation of a Proof of Concept in a dangerous goods monitoring scenario, and, finally, we discuss test results for structural properties and query performance evaluation.
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Zhao, Gang, Jifa Wang, and Huibin Shi. "Research on Multiattribute Comprehensive Evaluation of Intelligent Judicial Decision System." Discrete Dynamics in Nature and Society 2021 (September 3, 2021): 1–8. http://dx.doi.org/10.1155/2021/5713870.
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When dealing with cases, judges must consult a large number of relevant materials and carefully consider before they can write the final judgment. So, we want to use intelligent systems to assist the judicial system in handling cases. The essence of the system is automatic text classification. The system can predict the judgment result according to the previous prediction and can also provide support for judicial judgment and individual litigation. Because the evaluation of intelligent judicial decision-making system has the characteristics of complexity and fuzziness, we establish a comprehensive evaluation model of intelligent judicial decision-making system with subjective and objective combination by introducing the TOPSIS model. In the experiment, firstly, we use nine multiattribute comprehensive evaluation index systems such as acquisition cost and use cost to grade the indexes. Secondly, AHP and entropy weight methods are used to calculate the subjective weight and objective weight of the index, respectively; the combined weight of the index is determined according to the expert forced scoring method, the attribute measurement function of a single index is constructed according to the classification of the index, the comprehensive attribute measurement is calculated, and the comprehensive evaluation grade is judged according to the attribute identification standard. Finally, taking the intelligent judicial decision-making system as the research object, combined with the system report and expert score, this paper makes a multiattribute comprehensive evaluation and analysis of the intelligent judicial decision-making system and analyzes the results. The final experimental results show that the evaluation results of the model are reasonable and consistent with the actual situation, which verifies the adaptability of the combined weighted attribute recognition model in the multiattribute comprehensive evaluation of intelligent judicial judgment system. This result provides ideas and theoretical follow-up work for the intelligent judgment of judicial cases and has certain significance for the development of the field of judicial judgment.
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Pelletier, Gabriel, Nadav Aridan, Lesley K. Fellows, and Tom Schonberg. "A Preferential Role for Ventromedial Prefrontal Cortex in Assessing “the Value of the Whole” in Multiattribute Object Evaluation." Journal of Neuroscience 41, no. 23 (April 27, 2021): 5056–68. http://dx.doi.org/10.1523/jneurosci.0241-21.2021.
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Zhang, Maoyin, Tingting Zheng, Wanrong Zheng, and Ligang Zhou. "Interval-Valued Pythagorean Hesitant Fuzzy Set and Its Application to Multiattribute Group Decision-Making." Complexity 2020 (February 13, 2020): 1–26. http://dx.doi.org/10.1155/2020/1724943.
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Pythagorean hesitant fuzzy sets are widely watched because of their excellent ability to deal with uncertainty, imprecise and vague information. This paper extends Pythagorean hesitant fuzzy environments to interval-valued Pythagorean hesitant fuzzy environments and proposes the concept of interval-valued Pythagorean hesitant fuzzy set (IVPHFS), which allows the membership of each object to be a set of several pairs of possible interval-valued Pythagorean fuzzy elements. Furthermore, we develop a series of aggregation operators for interval-valued Pythagorean hesitant fuzzy information and apply them to multiattribute group decision-making (MAGDM) problems. Then, some desired operational laws and properties of IVPHFSs are studied. Especially, considering an interval-valued Pythagorean fuzzy element (IVPHFE) is formed by several pairs of interval values, this paper proposes the concepts of score function and accuracy function in the form of two interval numbers which can retain interval-valued Pythagorean fuzzy information as much as possible. Then, the relationship among these operators is discussed by comparing the interval numbers. Eventually, an illustrative example fully shows the feasibility, practicality, and effectiveness of the proposed approach.
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Holbrook, Morris B., and William J. Havlena. "Assessing the Real-to-Artificial Generalizability of Multiattribute Attitude Models in Tests of New Product Designs." Journal of Marketing Research 25, no. 1 (February 1988): 25–35. http://dx.doi.org/10.1177/002224378802500103.
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The authors investigate the neglected problem of generalizing from multiattribute attitude models based on data for actual brands of real products to reach conclusions on affective responses toward artificial objects or hypothetical concepts, such as those that might be used to develop new product designs. They propose an approach involving four stages of analysis to examine the intervening role of multiattribute attitude models for real products in accounting for the effects of artificial design features on affective responses. The method decomposes the overall relationship of affect to artificial design features into (1) the mediating effects of a multiattribute attitude model based on real products, (2) other indirect effects, and (3) a residual direct effect. This general approach is illustrated and supported by an application to data on responses to male vocal recordings (real products) and alternative recording mixes (artificial objects). The results suggest the need to proceed with caution in using multiattribute attitude models based on real products to guide the design of artificial objects in laboratory tests oriented toward new product development.
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Wang, Yuhong, Xiaojuan Shi, Jihong Sun, and Wuyong Qian. "A Grey Interval Relational Degree-Based Dynamic Multiattribute Decision Making Method and Its Application in Investment Decision Making." Mathematical Problems in Engineering 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/607016.
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The purpose of this paper is to propose a three-dimensional grey interval relational degree model for dynamic Multiattribute decision making. In the model, the observed values are interval grey numbers. Elements are selected in the system as the points in anm-dimensional linear space. Then observation data of each element to different time and objects are as the coordinates of point. An optimization model is employed to obtain each scheme’s affiliate degree for the positive and negative ideal schemes. And a three-dimensional grey interval relational degree model based on time, index, and scheme is constructed in the paper. The result shows that the three-dimensional grey relational degree simplifies the traditional dynamic multiattribute decision making method and can better resolve the dynamic multiattribute decision making problem of interval numbers. The example illustrates that the method presented in the paper can be used to deal with problems of uncertainty such as dynamic multiattribute decision making.
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Li, Jing, Yue Sun, Lingling Gong, Nana Chai, and Yanfei Yin. "Multiattribute Fuzzy Decision Evaluation Approach and Its Application in Enterprise Competitiveness Evaluation." Mathematical Problems in Engineering 2021 (March 13, 2021): 1–11. http://dx.doi.org/10.1155/2021/8867752.
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Multiattribute decision-making approach is one of the key complex system evaluation technologies which has attracted high attention of academic research studies. This paper establishes a novel multiattribute decision evaluation approach. First, we propose a high-dimensional data attribute reduction model based on partial correlation analysis and factor analysis methods. Second, based on the attribute weights calculated by multiple weighting methods, the corresponding multiple evaluation score vectors of the objects evaluated can be obtained. The final scoring vector can be determined by combining the quadratic combination weighting and the Spearman consistency test. Third, we use fuzzy C-means algorithm to grade evaluated objects. Finally, the established evaluation approach in this paper is verified by using the 107 observations in China. This approach also provides a decision-making example for attribute reduction of high-dimensional data, scoring of complex system evaluation, and clustering analysis when conducting evaluation in other fields.
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Ladner, Travis R., Ashly C. Westrick, John C. Wellons, and Chevis N. Shannon. "Health-related quality of life in pediatric Chiari Type I malformation: the Chiari Health Index for Pediatrics." Journal of Neurosurgery: Pediatrics 17, no. 1 (January 2016): 76–85. http://dx.doi.org/10.3171/2015.5.peds1513.
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OBJECT The purpose of this study was to design and validate a patient-reported health-related quality of life (HRQOL) instrument for pediatric Chiari Type I malformation (CM-I), the Chiari Health Index for Pediatrics (CHIP). METHODS The CHIP has 45 items with 4 components making up 2 domain scores, physical (pain frequency, pain severity, nonpain symptoms) and psychosocial; physical and psychosocial scores are combined to create an overall HRQOL score. Increasing scores (0 to 1) represent increasing HRQOL. Fifty-five patients with CM-I (mean age 12 ± 4 years, 53% male) were enrolled and completed the CHIP and Health Utilities Index Mark 3 (HUI3). Twenty-five healthy controls (mean age 11.9 ± 4 years, 40% male) also completed the CHIP. CHIP scores were compared between these groups via the Mann-Whitney U-test. For CHIP discriminative function, subscore versus presence of CM-I was compared via receiver operating characteristic curve analysis. CHIP scores in the CM-I group were stratified by symptomatology (asymptomatic, headaches, and paresthesias) and compared via Kruskal-Wallis test with Mann-Whitney U-test with Bonferroni correction (p < 0.0167). CHIP was compared with HUI3 (Health Utilities Index Mark 3) via univariate and multivariate linear regression. RESULTS CHIP physical and psychosocial subscores were, respectively, 24% and 18% lower in CM-I patients than in controls (p < 0.001); the overall HRQOL score was 23% lower as well (p < 0.001). The area under the curve (AUC) for CHIP physical subscore versus presence of CM-I was 0.809. CHIP physical subscore varied significantly with symptomatology (p = 0.001) and HUI3 pain-related quality of life (R2 = 0.311, p < 0.001). The AUC for CHIP psychosocial subscore versus presence of CM-I was 0.754. CHIP psychosocial subscore varied significantly with HUI3 cognitive- (R2 = 0.324, p < 0.001) and emotion-related (R2 = 0.155, p = 0.003) quality of life. The AUC for CHIP HRQOL versus presence of CM-I was 0.820. Overall CHIP HRQOL score varied significantly with symptomatology (p = 0.001) and HUI3 multiattribute composite HRQOL score (R2 = 0.440, p < 0.001). CONCLUSIONS The CHIP is a patient-reported, CM-I-specific HRQOL instrument, with construct validity in assessing pain-, cognitive-, and emotion-related quality of life, as well as symptomatic features unique to CM-I. It holds promise as a discriminative HRQOL index in CM-I outcomes assessment.
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Wang, Xin, and Lei Zhang. "A Combined Weighting Model Based on Maximizing Deviation for Multiple Attribute Decision-Making." Advances in Materials Science and Engineering 2022 (March 15, 2022): 1–8. http://dx.doi.org/10.1155/2022/7679851.
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Multiattribute decision-making is an important part of decision-making theory and modern scientific decision-making. It is widely used in engineering design, economic management, and so on. It is an important part of modern decision science to sort decision objects when considering multiple attributes. Due to time pressure and lack of understanding of decision-making problems, it is difficult for decision makers to accurately express judgment information. Decision-makers’ judgment information is more suitable to be expressed by intuitionistic fuzzy sets rather than deterministic numbers or linguistic variables. In the multiattribute decision-making problem, the size of attribute weight reflects the relative importance of each attribute. The research on attribute weight determination method is one of the core problems of multiattribute decision-making. Whether it is the subjective weighting method, the objective weighting method, or the combined weighting method, the research mainly focuses on deterministic multiattribute decision-making, mostly transforming fuzzy information into deterministic information for decision-making, which will lose a lot of information. Due to the differences of objective information data, a combined weighting method in different cases was proposed in this study. The original weight information and the prior information of standardized evaluation can be fully utilized in this model. The results indicate that when decision makers have preferences for different weighting methods, the combined weighting method can be determined according to the preference information of decision makers.
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Doyle, J. R., R. H. Green, and W. D. Cook. "Upper and Lower Bound Evaluation of Multiattribute Objects: Comparison Models Using Linear Programming." Organizational Behavior and Human Decision Processes 64, no. 3 (December 1995): 261–73. http://dx.doi.org/10.1006/obhd.1995.1104.
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Liu, Zhege, Junxing Cao, Yujia Lu, Shuna Chen, and Jianli Liu. "A seismic facies classification method based on the convolutional neural network and the probabilistic framework for seismic attributes and spatial classification." Interpretation 7, no. 3 (August 1, 2019): SE225—SE236. http://dx.doi.org/10.1190/int-2018-0238.1.
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In the early stage of oil and gas exploration, due to the lack of available drilling data, the automatic seismic facies classification technology mainly relies on the unsupervised clustering method combined with the seismic multiattribute. However, the clustering results are unstable and have no clear geologic significance. The supervised classification method based on manual interpretation can provide corresponding geologic significance, but there are still some problems such as the discrete classification results and low accuracy. To solve these problems, inspired by hyperspectral and spatial probability distribution technology, we have developed a classification framework called the probabilistic framework for seismic attributes and spatial classification (PFSSC). It can improve the continuity of the classification results by combining the classification probability and the spatial partial probability of the classifier output. In addition, the convolutional neural network (CNN) is a typical classification algorithm in deep learning. By convolution and pooling, we could use it to extract features of complex nonlinear objects for classification. Taking advantage of the combination of PFSSC and CNN, we could effectively solve the existing problems mentioned above in seismic facies classification. It is worth mentioning that we select seismic the multiattribute by maximal information coefficient (MIC) before the seismic facies classification. Finally, using the CNN-PFSSC and MIC methods, we can obtain high accuracy in the test set, reasonable continuity within the same seismic facies, clear boundaries between different seismic facies, and seismic facies classification results consistent with sedimentological laws.
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Li, Jian, and Wan-ming Chen. "An Improved Grey Clustering Model with Multiattribute Spatial-Temporal Feature for Panel Data and Its Application." Mathematical Problems in Engineering 2020 (January 30, 2020): 1–9. http://dx.doi.org/10.1155/2020/6761597.
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Due to the complexity and uncertainty of the objective world and the limitation of cognition, it is difficult to extract the information and rules contained in the panel data effectively based on the traditional panel data clustering method. Given this, considering that the absolute amount level, increasing amount level, and volatility level are the main indicators to represent the spatial-temporal feature of the panel data, a novel grey clustering model with the multiattribute spatial-temporal feature of panel data is established, and then it is applied in the regional high-tech industrialization in China. The results show that the proposed model can make full use of the spatial-temporal feature information of the panel data, identify the problems existing in the clustering objects, and make the clustering results more objective and practical.
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Garg, Harish, Zeeshan Ali, Jeonghwan Gwak, Tahir Mahmood, and Sultan Aljahdali. "Some Complex Intuitionistic Uncertain Linguistic Heronian Mean Operators and Their Application in Multiattribute Group Decision Making." Journal of Mathematics 2021 (May 18, 2021): 1–31. http://dx.doi.org/10.1155/2021/9986704.
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In this paper, a new decision-making algorithm has been presented in the context of a complex intuitionistic uncertain linguistic set (CIULS) environment. CIULS integrates the concept the complex of a intuitionistic fuzzy set (CIFS) and uncertain linguistic set (ULS) to deal with uncertain and imprecise information in a more proactive manner. To investigate the interrelation between the pairs of CIULSs, we combine the concept of the Heronian mean (HM) and the complex intuitionistic uncertain linguistic (CIUL) to describe some new operators, namely, CIUL arithmetic HM (CIULAHM), CIUL weighted arithmetic HM (CIULWAHM), CIUL geometric HM (CIULGHM), and CIUL weighted geometric HM (CIULWGHM). The main advantage of these suggested operators is that they considered the interaction between pairs of objects during the formulation process. Also, a number of distinct brief cases and properties of the operators are analyzed. In addition, based on these operators, we have stated a MAGDM (“multiattribute group decision-making”) problem-solving algorithm. The consistency of the algorithm is illustrated by a computational example that compares the effects of the algorithm with a number of well-known existing methods.
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Ullah, Kifayat. "Picture Fuzzy Maclaurin Symmetric Mean Operators and Their Applications in Solving Multiattribute Decision-Making Problems." Mathematical Problems in Engineering 2021 (October 4, 2021): 1–13. http://dx.doi.org/10.1155/2021/1098631.
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To evaluate objects under uncertainty, many fuzzy frameworks have been designed and investigated so far. Among them, the frame of picture fuzzy set (PFS) is of considerable significance which can describe the four possible aspects of expert’s opinion using a degree of membership (DM), degree of nonmembership (DNM), degree of abstinence (DA), and degree of refusal (DR) in a certain range. Aggregation of information is always challenging especially when the input arguments are interrelated. To deal with such cases, the goal of this study is to develop the notion of the Maclaurin symmetric mean (MSM) operator as it aggregates information under uncertain environments and considers the relationship of the input arguments, which make it unique. In this paper, we studied the theory of MSM operators in the layout of PFSs and discussed their applications in the selection of the most suitable enterprise resource management (ERP) scheme for engineering purposes. We developed picture fuzzy MSM (PFMSM) operators and investigated their validity. We developed the multiattribute decision-making (MADM) algorithm based on the PFMSM operators to examine the performance of the ERP systems using picture fuzzy information. A numerical example to evaluate the performance of ERP systems is studied, and the effects of the associated parameters are discussed. The proposed aggregated results using PFMSM operators are found to be reliable as it takes into account the interrelationship of the input information, unlike traditional aggregation operators. A comparative study of the proposed PFMSM operators is also studied.
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Hao, Xiumei, Mingwei Li, and Yuting Chen. "China's overcapacity industry evaluation based on TOPSIS grey relational projection method with mixed attributes." Grey Systems: Theory and Application ahead-of-print, ahead-of-print (July 28, 2020). http://dx.doi.org/10.1108/gs-03-2020-0033.
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PurposeThis paper takes the seven overcapacity industries such as the textile industry, electricity and heat, steel, coal, automobile manufacturing, nonferrous metals and petrochemical industry as research objects and proposes a TOPSIS grey relational projection group decision method with mixed multiattributes, which is used for the ranking of the seven industries with overcapacity and provided relevant departments with a basis for decision-making.Design/methodology/approachFirst, an evaluation index system from four aspects is established. Secondly, the attributes of linguistic information are converted into two-dimensional interval numbers and triangular fuzzy numbers, and an evaluation matrix is constructed and normalized. This paper uses the AHP method to determine the subjective weights and uses the coefficient of variation method to determine the objective weights. Moreover, this paper sets up the optimization model with the largest comprehensive evaluation value to determine the combined weights. Finally, the TOPSIS grey relational projection method is proposed to calculate the closeness of grey relational projections and to rank them.FindingsThis paper analyzes the problem of overcapacity in seven industries with the TOPSIS grey relational projection method. The results show that the four industries of automobile manufacturing, textile, coal and petrochemical are all in serious overcapacity levels, while the three industries of steel, nonferrous metals and electric power are relatively in weak overcapacity level in the three years of 2016–2018. TOPSIS grey relational projection method ranks the overcapacity degree of the seven major overcapacity industries, making the relative overcapacity degree of each industry more clear and providing a reference for the government to formulate targeted policies and measures for each industry.Practical implicationsBy using TOPSIS grey relational projection method to evaluate the overcapacity of the seven major overcapacity industries, on the one hand, it makes the relative overcapacity degree of each industry more clear, on the other hand, it can provides the basis for the government and decision-making departments. This helps them promote better the healthy and orderly economic development of the seven major industries and avoid resource waste caused by overcapacity.Originality/valueThis article solves the single evaluation method caused by the limited indicators in the past, combines TOPSIS and the grey relational projection method and applies it to the overcapacity evaluation of the industry, not only applies it to the evaluation of overcapacity for the first time but also involves novel problems and methods, which expands the scope of application of the model.