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Journal articles on the topic 'Multi-attribute analysis'

To see the other types of publications on this topic, follow the link: Multi-attribute analysis.

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Braglia, Marcello. "MAFMA: multi‐attribute failure mode analysis." International Journal of Quality & Reliability Management 17, no. 9 (December 2000): 1017–33. http://dx.doi.org/10.1108/02656710010353885.

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2

Chang, Kuei-Hu. "Generalized multi-attribute failure mode analysis." Neurocomputing 175 (January 2016): 90–100. http://dx.doi.org/10.1016/j.neucom.2015.10.039.

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3

Jiménez, Antonio, Sixto Ríos-Insua, and Alfonso Mateos. "A generic multi-attribute analysis system." Computers & Operations Research 33, no. 4 (April 2006): 1081–101. http://dx.doi.org/10.1016/j.cor.2004.09.003.

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4

Gratzl, Samuel, Alexander Lex, Nils Gehlenborg, Hanspeter Pfister, and Marc Streit. "LineUp: Visual Analysis of Multi-Attribute Rankings." IEEE Transactions on Visualization and Computer Graphics 19, no. 12 (December 2013): 2277–86. http://dx.doi.org/10.1109/tvcg.2013.173.

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5

Bichler, Martin. "An experimental analysis of multi-attribute auctions." Decision Support Systems 29, no. 3 (October 2000): 249–68. http://dx.doi.org/10.1016/s0167-9236(00)00075-0.

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6

FOLLECO, ANDRES, and TAGHI KHOSHGOFTAAR. "ATTRIBUTE NOISE DETECTION USING MULTI-RESOLUTION ANALYSIS." International Journal of Reliability, Quality and Safety Engineering 13, no. 03 (June 2006): 267–88. http://dx.doi.org/10.1142/s0218539306002252.

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The value of knowledge inferred from information databases is critically dependent on the quality of data. The identification of noisy attributes which can easily corrupt and curtail valuable knowledge and information from a dataset can be very helpful to analysts. We present a novel detection method to identify noisy attributes in datasets of software metrics using multi-resolution transformations based on Discrete Wavelet Transforms. The proposed method has been applied to supervised datasets of scientific full-scale data from NASA's Software Metric Data Program (MDP) and to a military command, control, and communications system (CCCS). Empirical results have been favorably compared to those obtained from the robust Pairwise Attribute Noise Detection Algorithm (PANDA) using the same MDP datasets and with mixed results for the CCCS data. All results were verified with several case studies that included injecting known simulated noise into specific attributes with no class noise.

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7

Roth, Richard, Frank Field, and Joel Clark. "Materials selection and multi-attribute utility analysis." Journal of Computer-Aided Materials Design 1, no. 3 (October 1994): 325–42. http://dx.doi.org/10.1007/bf00712855.

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8

Ding, Li Li, Song Wang, and Wang Lin Kang. "Competitive Analysis of Multi-Attribute On-Line Auction." Advanced Materials Research 204-210 (February 2011): 205–8. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.205.

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The emergence of auction that supports bids characterized by several attributes is one of the most recent evolutions within auction theory. The purpose of this paper is to introduce multi-attribute into the on-line auction mechanism, proposed by Lavi and Nisan. Our mechanism is designed for auctioning multiple units of a good when the bidders arrive and depart dynamically. To incorporate the buyer’s preferences across attributes in an auction setting, we adopt a novel application of a scoring function. Furthermore, we describe a competitive reservation utility strategy (RUS), which helps the buyer decide when and how many goods he should buy. Finally, we also perform numerical examples showing our results.

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9

Amarchinta, Hemanth K., and Ramana V. Grandhi. "Multi-Attribute Structural Optimization Based on Conjoint Analysis." AIAA Journal 46, no. 4 (April 2008): 884–93. http://dx.doi.org/10.2514/1.30530.

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10

Guikema, Seth D., and Mark W. Milke. "Sensitivity analysis for multi-attribute project selection problems." Civil Engineering and Environmental Systems 20, no. 3 (September 2003): 143–62. http://dx.doi.org/10.1080/1028660021000018519.

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11

Holt, Gary D., Paul O. Olomolaiye, and Frank C. Harris. "Applying multi-attribute analysis to contractor selection decisions." European Journal of Purchasing & Supply Management 1, no. 3 (September 1994): 139–48. http://dx.doi.org/10.1016/0969-7012(94)90003-5.

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12

Roth, Philip L. "Multi-attribute utility analysis using the promes approach." Journal of Business and Psychology 9, no. 1 (September 1994): 69–80. http://dx.doi.org/10.1007/bf02230988.

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13

Pawlak, Zdzisa̵w, and Roman So̵winski. "Rough set approach to multi-attribute decision analysis." European Journal of Operational Research 72, no. 3 (February 1994): 443–59. http://dx.doi.org/10.1016/0377-2217(94)90415-4.

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14

Michener, H. Andrew, Craig L. Abrams, and Robert F. Ebert. "MAUA145: A Pascal program for multi-attribute utility analysis." Behavior Research Methods, Instruments, & Computers 21, no. 6 (November 1989): 651–52. http://dx.doi.org/10.3758/bf03210595.

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15

Crowe, Thomas J., James S. Noble, and Jeevan S. Machimada. "Multi‐attribute analysis of ISO 9000 registration using AHP." International Journal of Quality & Reliability Management 15, no. 2 (March 1998): 205–22. http://dx.doi.org/10.1108/02656719810368495.

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16

Ghandeharizadeh, Shahram, David J. DeWitt, and Waheed Qureshi. "A performance analysis of alternative multi-attribute declustering strategies." ACM SIGMOD Record 21, no. 2 (June 1992): 29–38. http://dx.doi.org/10.1145/141484.130293.

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17

Yue, X. D., L. B. Cao, D. Q. Miao, Y. F. Chen, and B. Xu. "Multi-view attribute reduction model for traffic bottleneck analysis." Knowledge-Based Systems 86 (September 2015): 1–10. http://dx.doi.org/10.1016/j.knosys.2015.03.022.

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18

Malak, Richard J., Jason Matthew Aughenbaugh, and Christiaan J. J. Paredis. "Multi-attribute utility analysis in set-based conceptual design." Computer-Aided Design 41, no. 3 (March 2009): 214–27. http://dx.doi.org/10.1016/j.cad.2008.06.004.

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19

Cheng, KeFei, Fengchi Shan, ZhiWen Song, ZhenNi Zhao, and XiaoYong Guo. "Weibo user attribute analysis method based on multi-feature." Journal of Physics: Conference Series 1544 (May 2020): 012176. http://dx.doi.org/10.1088/1742-6596/1544/1/012176.

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20

Moschoglou, Stylianos, Evangelos Ververas, Yannis Panagakis, Mihalis A. Nicolaou, and Stefanos Zafeiriou. "Multi-Attribute Robust Component Analysis for Facial UV Maps." IEEE Journal of Selected Topics in Signal Processing 12, no. 6 (December 2018): 1324–37. http://dx.doi.org/10.1109/jstsp.2018.2877108.

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21

Yan, Hong-Bin, Van-Nam Huynh, Tieju Ma, and Yoshiteru Nakamori. "Non-additive multi-attribute fuzzy target-oriented decision analysis." Information Sciences 240 (August 2013): 21–44. http://dx.doi.org/10.1016/j.ins.2013.03.050.

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22

Rahman, A. "Multi-attribute utility analysis?a major decision aid technique." Nuclear Energy 42, no. 2 (April 2003): 87–93. http://dx.doi.org/10.1680/nuen.42.2.87.37607.

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23

Hodge, George L., John R. Canada, and Wael R. Masri. "Low Cost Microcomputer Software for Multi-Attribute Decision Analysis." Engineering Economist 37, no. 2 (January 1992): 184–91. http://dx.doi.org/10.1080/00137919208903068.

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24

Valdés, Fabio, and Ralf Hartmut Güting. "A framework for efficient multi-attribute movement data analysis." VLDB Journal 28, no. 4 (October 31, 2018): 427–49. http://dx.doi.org/10.1007/s00778-018-0525-6.

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25

Kahraman, Cengiz, Nüfer Yasin Ates, Sezi Çevik, and Murat Gülbay. "Fuzzy multi-attribute cost–benefit analysis of e-services." International Journal of Intelligent Systems 22, no. 5 (2007): 547–65. http://dx.doi.org/10.1002/int.20213.

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26

Ringuest, Jeffrey L. "LP-metric sensitivity analysis for single and multi-attribute decision analysis." European Journal of Operational Research 98, no. 3 (May 1997): 563–70. http://dx.doi.org/10.1016/s0377-2217(96)00177-4.

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27

Luo, Dang, and Yuwen Li. "Multi-stage and multi-attribute risk group decision-making method based on grey information." Grey Systems: Theory and Application 5, no. 2 (August 3, 2015): 222–33. http://dx.doi.org/10.1108/gs-01-2015-0002.

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Purpose – For the multi-stage and multi-attribute risk group decision-making problem, the attribute weight, decision-maker weight and time weight are unknown. The attribute value is grey information. The purpose of this paper is to discuss a decision-making method. Design/methodology/approach – Analysis techniques and the theory about distance degree are used to determine the decision-maker weight within single stage. Grey relational analysis method is applied to determine the attribute weight. Moreover, the uncertainty of time weight and the proximity between the attribute value and positive/negative value are taken into account. A multi-objective optimization model is established based on maximum entropy to obtain time weights, so the comprehensive value is determined. Findings – An example shows the effectiveness and practicability. Originality/value – For a decision-making process, the results are different in different periods. This method is computationally very simple, easily comprehensible.

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28

Cheng, Yong Qian, Hong Mei Ma, Qian Wu Song, and Yue Zhang. "Assessment of Water Quality Using Grey Relational Analysis and Principal Component Analysis." Advanced Materials Research 255-260 (May 2011): 2829–35. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.2829.

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This paper investigates the comprehensive assessment of water quality, which is generally a multi-attribute assessment problem. In this context, the grey relational analysis is adopted to settle the no uniformity problem of water quality attributes. The principal component analysis is applied to calculate the weighting values corresponding to various attributes of water quality so that their relative importance can be properly and objectively described. Results of study reveal that grey relational analysis coupled with principal component analysis can effectively solve the multi-attribute water quality assessment. The method is universal and can be a useful tool to improve the comprehensive assessment of water quality.

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29

Monat, Jamie P. "The benefits of global scaling in multi-criteria decision analysis." Judgment and Decision Making 4, no. 6 (October 2009): 492–508. http://dx.doi.org/10.1017/s1930297500004034.

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AbstractWhen there are multiple competing objectives in a decision-making process, Multi-Attribute Choice scoring models are excellent tools, permitting the incorporation of both subjective and objective attributes. However, their accuracy depends upon the subjective techniques used to construct the attribute scales and their concomitant weights. Conventional techniques using local scales tend to overemphasize small differences in attribute measures, which may yield erroneous conclusions. The Range Sensitivity Principle (RSP) is often invoked to adjust attribute weights when local scales are used. In practice, however, decision makers often do not follow the prescriptions of the Range Sensitivity Principle and under-adjust the weights, resulting in potentially poor decisions. Examples are discussed as is a proposed solution: the use of global scales instead of local scales.

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30

Pereira, Tiago, and Yordan Garbatov. "Multi-Attribute Decision-Making Ship Structural Design." Journal of Marine Science and Engineering 10, no. 8 (July 29, 2022): 1046. http://dx.doi.org/10.3390/jmse10081046.

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This study develops a procedure for performing multi-attribute decision-making ship structural design of a multi-purpose ship. The already designed ship is further structurally designed to comply with the requirements of the Classification Societies. The ship hull and structural components are verified against yielding, buckling, and ultimate strength. Based on the ultimate limit state (ULS), the first order reliability method (FORM) is employed to analyse the structural risk in reducing the probability of failure. The costs associated with materials, manufacturing, and labour are estimated. The structural risk analysis is performed, accounting for different hazard issues related to loss of ship, loss of cargo, loss of human life, and accidental spill of fuel and oil. The risk-based analysis is used to identify an optimum level of ship structural safety, i.e., the optimum reliability index, controlling the risk associated with the ship hull design. The study uses a multiple attribute decision-making ship design approach, simultaneously considering several objectives for different scenarios employing the Technique of Order Preference by Similarity to Ideal Solution (TOPSIS). The identified ship design solution is associated with the minimum expected total cost leading to lower construction and operational costs and risk with maximum cargo capacity and energy efficiency. The developed procedure is flexible enough to accommodate different design criteria and possible hazards during the ship’s service life.

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31

Lü, Wen Sheng, and Bin Zhang. "Set Pair Analysis Method of Containing Target Constraint Mixed Interval Multi-Attribute Decision-Making." Applied Mechanics and Materials 226-228 (November 2012): 2222–26. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.2222.

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In view of target attribute value for different sector number, moreover, also attaches a target constraint condition kind of mix sector multi-attribute decision making question, this paper presents set pair analysis decision-making method. Firstly this paper puts forward three typical interval type attribute value representation; Then using set pair analysis theory, the interval type attribute value unified convert the correlate form, Finally has given complex decision-making criterion function, which collected Conformity degree criteria and Criteria for membership degree. Through the construction plan changes decision-making example analysis shows that this method is a simple and effective method for solving multiple attribute decision making.

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32

Ma, Benjiang, Zhongmin Zhou, Muhammad Farhan Bashir, and Yuanji Huang. "A Multi-Attribute Reverse Auction Model on Margin Bidding." Asia-Pacific Journal of Operational Research 37, no. 06 (September 15, 2020): 2050032. http://dx.doi.org/10.1142/s0217595920500323.

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Multi-attribute reverse auction has many advantages for the buyer with the multi-dimensional attribute requirements. However, it is hard to design an optimal auction mechanism for the scenario in practice. Therefore, this paper presents a method transforming multi-attribute auction into single-attribute auction by bidding in deposit. The analysis indicates that our method can reduce not only the transaction risk caused by the supplier’s bid abandonment but also the operating cost and complexity of the multi-attribute auction. Besides, our method meets the incentive compatibility and participation constraint conditions by promising that the highest bidding supplier is the winner in the Auction and can obtain higher expected profits than traditional auctions for the buyer.

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33

YUE Rui, WANG Zhongbin, and PENG Anhua. "Multi-Attribute Group Decision Making Based on Set Pair Analysis." International Journal of Advancements in Computing Technology 4, no. 10 (June 30, 2012): 205–13. http://dx.doi.org/10.4156/ijact.vol4.issue10.24.

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34

Whitlock, Drury D., Todd Williams, Tim Shea, Tom Heinemann, Scott Higbee, Roger Scott, and Ben Thayer. "Using Multi-Attribute Utility Analysis to Facilitate Biosolids Master Planning." Proceedings of the Water Environment Federation 2008, no. 3 (January 1, 2008): 395–403. http://dx.doi.org/10.2175/193864708788806511.

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35

Meng, Fanyong, Xiaohong Chen, and Qiang Zhang. "Multi-attribute decision analysis under a linguistic hesitant fuzzy environment." Information Sciences 267 (May 2014): 287–305. http://dx.doi.org/10.1016/j.ins.2014.02.012.

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36

Hwang, Heung-Suk. "Web-based multi-attribute analysis model for engineering project evaluation." Computers & Industrial Engineering 46, no. 4 (July 2004): 669–78. http://dx.doi.org/10.1016/j.cie.2004.05.016.

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37

Kelly, M., and M. C. Thorne. "An approach to multi-attribute utility analysis under parametric uncertainty." Annals of Nuclear Energy 28, no. 9 (June 2001): 875–93. http://dx.doi.org/10.1016/s0306-4549(00)00092-x.

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38

Zhao, Yong, Chongyin Huang, and Yang Chen. "Weight sensitivity measurement, analysis, and application in multi-attribute evaluation." International Journal of General Systems 42, no. 8 (November 2013): 860–69. http://dx.doi.org/10.1080/03081079.2013.819862.

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39

Närman, Per, Markus Buschle, and Mathias Ekstedt. "An enterprise architecture framework for multi-attribute information systems analysis." Software & Systems Modeling 13, no. 3 (November 16, 2012): 1085–116. http://dx.doi.org/10.1007/s10270-012-0288-2.

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40

Ahn, Byeong Seok. "Extreme point-based multi-attribute decision analysis with incomplete information." European Journal of Operational Research 240, no. 3 (February 2015): 748–55. http://dx.doi.org/10.1016/j.ejor.2014.07.037.

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41

Carretero-Gómez, José M., and Elizabeth F. Cabrera. "An Empirical Evaluation of Training Using Multi-attribute Utility Analysis." Journal of Business and Psychology 27, no. 2 (July 9, 2011): 223–41. http://dx.doi.org/10.1007/s10869-011-9241-6.

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42

Muñoz-Herrera, Sebastián, and Karol Suchan. "Local Optima Network Analysis of Multi-Attribute Vehicle Routing Problems." Mathematics 10, no. 24 (December 8, 2022): 4644. http://dx.doi.org/10.3390/math10244644.

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Multi-Attribute Vehicle Routing Problems (MAVRP) are variants of Vehicle Routing Problems (VRP) in which, besides the original constraint on vehicle capacity present in Capacitated Vehicle Routing Problem (CVRP), other attributes that model diverse real-life system characteristics are present. Among the most common attributes studied in the literature are vehicle capacity and route duration constraints. The influence of these restrictions on the overall structure of the problem and the performance of local search algorithms used to solve it has yet to be well known. This paper aims to explain the impact of constraints present in different variants of VRP through the alterations of the structure of the underlying search space that they cause. We focus on Local Optima Network Analysis (LONA) for multiple Traveling Salesman Problem (m-TSP) and VRP with vehicle capacity (CVRP), route duration (DVRP), and both (DCVRP) constraints. We present results that indicate that measures obtained for a sample of local optima provide valuable information on the behavior of the landscape under modifications in the problem’s constraints. Additionally, we use the LONA measures to explain the difficulty of VRP instances for solving by local search algorithms.

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43

Jukic, Tina, Jože Bencina, and Mirko Vintar. "Multi-Attribute Evaluation of E-Government Projects." International Journal of Information Communication Technologies and Human Development 4, no. 1 (January 2012): 82–92. http://dx.doi.org/10.4018/jicthd.2012010106.

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This paper presents comparative review of existent (inter-)national approaches to e-government projects evaluation with the stress on intangible/hidden costs and public value dimension which are the two components usually missed out in traditional investments evaluation methods (e.g., net present value, cost/benefit analysis, return on investment). Based on the literature review and weaknesses identified through comparative review, the authors developed models for ex-ante and ex-post evaluation of e-government projects for Slovenian public administration. The models were developed together with representatives of Ministry of Public Administration.

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44

Tian, Man-Wen, Xiu-Zhen Liao, Lian-Xiang Wu, Lin-Hua Peng, and Shu-Rong Yan. "Multi-attribute decision making of target enterprises in mergers and acquisitions." Journal of Interdisciplinary Mathematics 21, no. 5 (July 4, 2018): 1103–8. http://dx.doi.org/10.1080/09720502.2018.1493037.

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45

Rahman, Atiqe Ur, Muhammad Saeed, Khuram Ali Khan, and Rostin Matendo Mabela. "Set-Theoretic Inequalities Based on Convex Multi-Argument Approximate Functions via Set Inclusion." Journal of Function Spaces 2022 (March 9, 2022): 1–10. http://dx.doi.org/10.1155/2022/6998104.

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Hypersoft set is a novel area of study which is established as an extension of soft set to handle its limitations. It employs a new approximate mapping called multi-argument approximate function which considers the Cartesian product of attribute-valued disjoint sets as its domain and the power set of universe as its co-domain. The domain of this function is broader as compared to the domain of soft approximate function. It is capable of handling the scenario where sub-attribute-valued sets are considered more significant than taking merely single set of attributes. In this study, notions of set inclusion, convex (concave) sets, strongly convex (concave) sets, strictly convex (concave) sets, convex hull, and convex cone are conceptualized for the multi-argument approximate function. Based on these characterized notions, some set-theoretic inequalities are established with generalized properties and results. The set-theoretic version of classical Jensen’s type inequalities is also discussed with the help of proposed notions.

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46

Aberšek, Boris. "SCIENCE, CRITICAL THINKING, MULTI-ATTRIBUTE DECISION MAKING." Problems of Education in the 21st Century 80, no. 4 (August 25, 2022): 494–98. http://dx.doi.org/10.33225/pec/22.80.494.

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Science is the pursuit and application of knowledge and understanding of the natural and social world following a systematic methodology based on evidence. Science includes scientific methods such as objective observation, which refers to measurement and data (possibly, although not necessarily, using mathematics as a tool), evidence, experiment, critical analysis, and verification. The four major branches of science are: mathematics and logic, biological science, physical science, and social science. The key words that best define science are thus: objective observation + evidence-based approach + application of scientific methods = general truths The basic problem of such an explanation of science lies in the very definition of what general truths are, what kind of evidence is required to prove them, and how to arrive to this kind of evidence through objective observation. The simplest premise to answer these questions would be: through logic and critical thinking.

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47

Kong, Yunmi, Isabelle Perrigne, and Quang Vuong. "Multidimensional Auctions of Contracts: An Empirical Analysis." American Economic Review 112, no. 5 (May 1, 2022): 1703–36. http://dx.doi.org/10.1257/aer.20200864.

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In this paper, we conduct a structural analysis of multi-attribute auctions of contracts with a general allocation rule when private information is multidimensional. Upon modeling bidders’ contract value that accounts for their endogenous ex post actions, we nonparametrically identify bidders’ private information from their bids and estimate their joint distribution. Analyzing cash-royalty auctions of Louisiana oil leases, we find government revenue worse and development rates no better than in a cash auction with a fixed royalty in view of adverse selection and moral hazard. Our findings revise conventional wisdom on the optimality of multi-attribute auctions. (JEL D44, D82, D86, H82, Q35)

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48

Liu, Guo Feng, Li Guo, and Yue Bao. "Multi-Attribute Decision Making Based on the Similarity Degree of Ternary Interval Numbers." Advanced Materials Research 926-930 (May 2014): 3092–95. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3092.

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For multi-attribute decision making problems whose attribute values are ternary interval numbers and their weights and attribute values are not fully, this paper proposes a decision-making method based on the similarity degree of ternary interval numbers. Firstly, utilizing the closeness theory of fuzzy sets, we give the corresponding axiomatic definition of ternary interval numbers and obtain the corresponding calculation formula of similarity degree. Then, according to the basic idea of traditional TOPSIS method, we establish the multi-attribute decision making and give the general steps of solving multi-attribute decision making problems. Finally, the paper demonstrates the effectiveness and practicality of the method by the example analysis.

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49

Sylvia, Gilbert, and Sherry L. Larkin. "Firm-level Intermediate Demand for Pacific Whiting Products: A Multi-attribute, Multi-sector Analysis." Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 43, no. 3 (November 1995): 501–18. http://dx.doi.org/10.1111/j.1744-7976.1995.tb00138.x.

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50

Grassi, Rosanna, Paolo Bartesaghi, Stefano Benati, and Gian Paolo Clemente. "Multi-Attribute Community Detection in International Trade Network." Networks and Spatial Economics 21, no. 3 (July 14, 2021): 707–33. http://dx.doi.org/10.1007/s11067-021-09547-4.

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AbstractUnderstanding the structure of communities in a network has a great importance in the economic analysis. Communities are indeed characterized by specific properties, that are different from those of both the individual nodes and the whole network, and they can affect various processes on the network. In the International Trade Network, community detection aims to search sets of countries (or of trade sectors) which have a high intra-cluster connectivity and a low inter-cluster connectivity. In general, exchanges among countries occur according to preferential economic relationships ranging over different sectors. In this paper, we combine community detection with specific topological indicators, such as centrality measures. As a result, a new weighted network is constructed from the original one, in which weights are determined taking into account all the topological indicators in a multi-criteria approach. To solve the resulting Clique Partitioning Problem and find homogeneous group of nations, we use a new fast algorithm, based on quick descents to a local optimal solution. The analysis allows to cluster countries by interconnections, economic power and intensity of trade, giving an important overview on the international trade patterns.

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