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Thematische Bibliographien / Multicriteria decision making / Zeitschriftenartikel

Zeitschriftenartikel zum Thema „Multicriteria decision making“

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Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 5. März 2023

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1

Hendriks, Margriet M. W. B., Jan H. de Boer, Age K. Smilde und Durk A. Doornbos. „Multicriteria decision making“. Chemometrics and Intelligent Laboratory Systems 16, Nr. 3 (Dezember 1992): 175–91. http://dx.doi.org/10.1016/0169-7439(92)80036-4.

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2

Yager, R. R. „Modeling Prioritized Multicriteria Decision Making“. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, Nr. 6 (Dezember 2004): 2396–404. http://dx.doi.org/10.1109/tsmcb.2004.837348.

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3

Novikova, Natalia M., und Irina I. Pospelova. „Multicriteria decision making under uncertainty“. Mathematical Programming 92, Nr. 3 (01.05.2002): 537–54. http://dx.doi.org/10.1007/s101070100289.

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4

Hum Tchemra, Angela. „Adaptivity in Multicriteria Decision Making“. IEEE Latin America Transactions 9, Nr. 2 (April 2011): 160–70. http://dx.doi.org/10.1109/tla.2011.5765569.

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5

Hao, Qinxia, Shah Nazir, Xiaoxu Gao, Li Ma und Muhammad Ilyas. „A Review on Multicriteria Decision Support System and Industrial Internet of Things for Source Code Transformation“. Scientific Programming 2021 (13.01.2021): 1–9. http://dx.doi.org/10.1155/2021/6661272.

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The large scale increase of communication and number of devices in the Industrial Internet of Things (IIoT) has rapidly enabled practitioners to make decisions based on multicriteria. Multicriteria decision support systems (MCDSSs) play an important role in decision-making for a particular situation based on several criteria. Making of decision based on multicriteria is the main issues for research community and practitioners of the IIoT. Several decision support systems (DSSs) are offered for making decisions which have the potentiality to support the activities of the decision-making process. The suggested study shows a review on the existing decision support systems for the IIoT for source code transformation which will enable research community and practitioners of the industry to use the existing methods, tools, approaches, and techniques and to provide novel solutions for the smooth industry of Internet of Things.

6

Piegat, Andrzej, und Wojciech Sałabun. „Identification of a Multicriteria Decision-Making Model Using the Characteristic Objects Method“. Applied Computational Intelligence and Soft Computing 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/536492.

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This paper presents a new, nonlinear, multicriteria, decision-making method: the characteristic objects (COMET). This approach, which can be characterized as a fuzzy reference model, determines a measurement standard for decision-making problems. This model is distinguished by a constant set of specially chosen characteristic objects that are independent of the alternatives. After identifying a multicriteria model, this method can be used to compare any number of decisional objects (alternatives) and select the best one. In the COMET, in contrast to other methods, the rank-reversal phenomenon is not observed. Rank-reversal is a paradoxical feature in the decision-making methods, which is caused by determining the absolute evaluations of considered alternatives on the basis of the alternatives themselves. In the Analytic Hierarchy Process (AHP) method and similar methods, when a new alternative is added to the original alternative set, the evaluation base and the resulting evaluations of all objects change. A great advantage of the COMET is its ability to identify not only linear but also nonlinear multicriteria models of decision makers. This identification is based not on a ranking of component criteria of the multicriterion but on a ranking of a larger set of characteristic objects (characteristic alternatives) that are independent of the small set of alternatives analyzed in a given problem. As a result, the COMET is free of the faults of other methods.

7

Yager, Ronald R. „Multicriteria Decision-Making Using Fuzzy Measures“. Cybernetics and Systems 46, Nr. 3-4 (03.04.2015): 150–71. http://dx.doi.org/10.1080/01969722.2015.1012884.

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8

Janssen, Ron, und Marjan Van Herwijnen. „A toolbox for multicriteria decision-making“. International Journal of Environmental Technology and Management 6, Nr. 1/2 (2006): 20. http://dx.doi.org/10.1504/ijetm.2006.008252.

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9

Grabisch, Michel. „Fuzzy integral in multicriteria decision making“. Fuzzy Sets and Systems 69, Nr. 3 (Februar 1995): 279–98. http://dx.doi.org/10.1016/0165-0114(94)00174-6.

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10

Cziner, Krisztina, Mari Tuomaala und Markku Hurme. „Multicriteria decision making in process integration“. Journal of Cleaner Production 13, Nr. 5 (April 2005): 475–83. http://dx.doi.org/10.1016/j.jclepro.2003.09.003.

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11

Keller, H. R., D. L. Massart und J. P. Brans. „Multicriteria decision making: A case study“. Chemometrics and Intelligent Laboratory Systems 11, Nr. 2 (Oktober 1991): 175–89. http://dx.doi.org/10.1016/0169-7439(91)80064-w.

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12

Mareschal, Bertrand. „Stochastic multicriteria decision making and uncertainty“. European Journal of Operational Research 26, Nr. 1 (Juli 1986): 58–64. http://dx.doi.org/10.1016/0377-2217(86)90159-1.

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13

Liang, Liang, Ren Yan Jiang und Yin Liang. „A Multicriteria Decision Method with Uncertain Information in Financial Credit Loan Decision-Making“. Applied Mechanics and Materials 52-54 (März 2011): 1868–72. http://dx.doi.org/10.4028/www.scientific.net/amm.52-54.1868.

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It is difficult to obtain the correct criteria weights with uncertain information in the multicriteria decision-making. Many methods depend on the subjective estimate. Therefore, a method is presented to evaluate the samples ranking only need the importance order of criteria. It evaluates firstly the overall ranking scores of samples based on the graphical classification and multicriteria hierarchical integrated methods. Subsequently, the relation model between the criteria scores and overall ranking scores of samples is built by linear regression. Finally, a fincial credit loan decision-making problem is presented to describe the way of the multicriteria decisiion making process. The analysis to compare with the AHP method illustrates the proposed method is objective and effective.

14

GRABOŚ, RAFAL. „ANSWER SET PROGRAMMING AND COMBINATORIAL MULTICRITERIA DECISION MAKING“. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 14, Nr. 04 (August 2006): 393–420. http://dx.doi.org/10.1142/s0218488506004096.

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The majority of approaches to multicriteria optimization are based on quantitative representations of preferences of a decision maker, in which numerical procedures of multicriteria analysis are used for aggregation purposes. However, very often qualitative data cannot be known in terms of absolute values so that a qualitative approach is needed. Moreover, the multicriteria methods are directly applicable when alternatives are individuals-then they may be explicitly listed and ordered by an agent. However, sometimes the set of alternatives has combinatorial structure and it must be selected from the set of Cartesian products of value domains of attributes satisfying certain constraints. Then, the space of possible alternatives has a size exponential in the number of variables and ranking all alternatives explicitly is a complex and tedious task. In this paper we propose logic programming with ordered disjunction as a qualitative approach to combinatorial multicriteria decision making, allowing a concise representation of the preference structures, and a human-like form of expressions, being close to natural language, hence providing a good readability and simplicity. A combinatorial multicriteria decision making problem is encoded as a logic program, in which preferences of the decision maker are represented qualitatively. The optimal decision corresponds exactly to the preferred answer set of the program, obtained via the well-known methods of multicriteria analysis.

15

Wibowo, Santoso, und Srimannarayana Grandhi. „Benchmarking knowledge management practices in small and medium enterprises“. Benchmarking: An International Journal 24, Nr. 5 (03.07.2017): 1215–33. http://dx.doi.org/10.1108/bij-01-2016-0013.

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Purpose The purpose of this paper is to formulate the process of measuring and benchmarking the performance of knowledge management (KM) practices as a multicriteria group decision-making problem and present a new multicriteria group decision-making approach for effectively evaluating the performance of KM practices to meet the interests of various stakeholders in small and medium enterprises (SMEs). Design/methodology/approach A new multicriteria group decision-making approach is developed for evaluating the performance of KM practices of individual SMEs. Intuitionistic fuzzy numbers are used for representing the subjective assessments of decision makers in evaluating the relative importance of the evaluation criteria and the performance of individual KM practices with respect to specific evaluation criteria. A fuzzy multicriteria group decision-making algorithm is developed for measuring and benchmarking the performance of alternative KM practices. Findings The proposed multicriteria group decision-making approach is capable of effectively evaluating the performance of KM practices through adequately considering the presence of multiple decision makers, the multi-dimensional nature of the evaluation problem, and appropriately modeling the subjectiveness and imprecision of the evaluation process. The presentation of an example shows that the proposed fuzzy multicriteria group decision-making algorithm is simple to use and efficient in computation. Research limitations/implications The outcome of the multicriteria group decision-making approach is highly dependent on the inputs provided by the decision maker. Practical implications The novelty from this research lies in the utilization of a multicriteria group decision-making approach for evaluating the performance of KM practices in an organization. The outcome from the performance evaluation process allows the enterprise to adopt appropriate KM practices for achieving competitive advantages. Social implications The proposed multicriteria group decision-making approach has a significant social implication as it can be used as a decision-making tool for providing various decision makers in SMEs with useful and strategic information concerning the performance of KM practices in a given situation. Originality/value The originality of this paper lies in the development of the multicriteria group decision-making approach for effectively measuring and benchmarking the performance of KM practices of individual SMEs.

16

Limayem, Moez, und Gerardine DeSanctis. „Providing Decisional Guidance for Multicriteria Decision Making in Groups“. Information Systems Research 11, Nr. 4 (Dezember 2000): 386–401. http://dx.doi.org/10.1287/isre.11.4.386.11874.

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17

Kiker, Gregory A., Todd S. Bridges, Arun Varghese, Thomas P. Seager und Igor Linkov. „Application of Multicriteria Decision Analysis in Environmental Decision Making“. Integrated Environmental Assessment and Management 1, Nr. 2 (2005): 95. http://dx.doi.org/10.1897/ieam_2004a-015.1.

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18

Wibowo, Santoso, und Hepu Deng. „Consensus-based decision support for multicriteria group decision making“. Computers & Industrial Engineering 66, Nr. 4 (Dezember 2013): 625–33. http://dx.doi.org/10.1016/j.cie.2013.09.015.

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19

Dubois, D., M. Grabisch, F. Modave und H. Prade. „Relating decision under uncertainty and multicriteria decision making models“. International Journal of Intelligent Systems 15, Nr. 10 (2000): 967–79. http://dx.doi.org/10.1002/1098-111x(200010)15:10<967::aid-int5>3.0.co;2-d.

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20

Martinkutė-Kaulienė, Raimonda, Radvinė Skobaitė, Viktorija Stasytytė und Nijolė Maknickienė. „Comparison of multicriteria decision-making methods in portfolio formation“. Finance, Markets and Valuation 7, Nr. 2 (2021): 60–72. http://dx.doi.org/10.46503/qwpu4486.

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Investors apply various methods to select stocks and construct an investment portfolio. In the majority of methods, the principle of diversification is relevant. Also, sometimes investors‘ behaviour generate biases related to portfolio formation. Multicriteria decision-making methods can overcome such shortcomings of investors' decision-making; thus, they are widely used for portfolio selection. In the performed research, the portfolio is constructed from the stocks of the Spanish stock market. Stocks are selected based on financial indicators. SAW and TOPSIS multicriteria methods are used to range the suitable stocks. Portfolio weights are proportionate to the obtained multicriteria rank. Characteristics of the final selected stocks are presented graphically. Expected portfolio return and risk are also described when comparing two portfolios. The results of the research prove that multicriteria decision-making methods are suitable for portfolio formation. However, such portfolios should be kept for a long time in order to receive a return.

21

Mustafa, Saima, Neelofar Safdar, Murrium Bibi, A. F. Sayed, Muhammad Ghaffar Khan und Zabidin Salleh. „A Study of Bipolar Fuzzy Soft Sets and Its Application in Decision-Making Problems“. Mathematical Problems in Engineering 2021 (19.10.2021): 1–12. http://dx.doi.org/10.1155/2021/5742288.

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We live in a society where we have to deal with so many social issues daily, and making a right choice is everyone’s main concern. This study is based on the selection of right university while getting admission which is the core issue for students nowadays. We are concerned with bipolar fuzzy multicriteria decision-making methods. The main purpose is to provide guidance to the students for determining the best university and evaluating the factors that are affecting while getting admission. We thus combined bipolar fuzzy and soft expert sets to give multicriteria decision-making approach that overcomes the issues that arise while taking decisions. This study involves the development of structural hierarchical models of parameters and the implementation of soft expert sets to make the decision-making problem much more precise by introducing a new algorithm. Thus, this model is helpful for multicriteria decision making and can be used for university selection and thus suitable for education sector as well.

22

Gogodze, Joseph. „Ranking Methods for Multicriteria Decision-Making: Application to Benchmarking of Solvers and Problems“. Scientific Programming 2021 (11.07.2021): 1–14. http://dx.doi.org/10.1155/2021/5513860.

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Evaluating the performance assessments of solvers (e.g., for computation programs), known as the solver benchmarking problem, has become a topic of intense study, and various approaches have been discussed in the literature. Such a variety of approaches exist because a benchmark problem is essentially a multicriteria problem. In particular, the appropriate multicriteria decision-making problem can correspond naturally to each benchmark problem and vice versa. In this study, to solve the solver benchmarking problem, we apply the ranking-theory method recently proposed for solving multicriteria decision-making problems. The benchmarking problem of differential evolution algorithms was considered for a case study to illustrate the ability of the proposed method. This problem was solved using ranking methods from different areas of origin. The comparisons revealed that the proposed method is competitive and can be successfully used to solve benchmarking problems and obtain relevant engineering decisions. This study can help practitioners and researchers use multicriteria decision-making approaches for benchmarking problems in different areas, particularly software benchmarking.

23

Denney, Dennis. „Multicriteria Decision-Making in Strategic Reservoir Planning“. Journal of Petroleum Technology 54, Nr. 09 (01.09.2002): 83–84. http://dx.doi.org/10.2118/0902-0083-jpt.

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24

Zavadskas, E., A. Kaklauskas und Z. Turskis. „MULTICRITERIA DECISION-MAKING SYSTEM FOR BUILDING REFURBISHMENT“. Statyba 3, Nr. 12 (Januar 1997): 62–68. http://dx.doi.org/10.1080/13921525.1997.10531368.

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25

Minch, Robert P., und G. Lawrence Sanders. „COMPUTERIZED INFORMATION SYSTEMS SUPPORTING MULTICRITERIA DECISION MAKING“. Decision Sciences 17, Nr. 3 (Juli 1986): 395–413. http://dx.doi.org/10.1111/j.1540-5915.1986.tb00233.x.

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26

VillarinhoRosa, Lúcio, und Assed Naked Haddad. „Building Sustainability Assessment throughout Multicriteria Decision Making“. Journal of Construction Engineering 2013 (30.09.2013): 1–9. http://dx.doi.org/10.1155/2013/578671.

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Opinion and choice strongly influence sustainability concepts. The building construction industry, in particular property developers, has been charged with promoting excess environmental impacts ranging from overuse of resources to pollution generation. This paper presents an application of sustainability concepts to building projects, as well as to the development of practices, methodology, and tools for evaluating existing buildings. This study will detail how current systems to evaluate building performance operate and how to improve them. This approach utilizes the analytic hierarchy process (AHP). AHP is a multicriteria method that evaluates the relative importance of criteria, subcriteria, and families of indicators, used in the proposed system of technical characteristics applied to the local culture. At the same time, AHP makes visible the critical factors involved in evaluation of sustainability of these buildings. A result of the application of this type of modeling is a system for sustainability assessment and evaluation of environmental aspects and socioeconomic perspectives of existing buildings in the state of Rio de Janeiro, Brazil.

27

Bogdanović, Strahinja, und Miroljub Hadžić. „Strategic multicriteria decision-making process in agriculture“. Ekonomika poljoprivrede 66, Nr. 1 (2019): 89–106. http://dx.doi.org/10.5937/ekopolj1901089b.

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28

Rolka, Leszek, Alicja Mieszkowicz-Rolka und Grzegorz Drupka. „Multicriteria decision-making in flight route selection“. Aircraft Engineering and Aerospace Technology 92, Nr. 9 (03.06.2020): 1377–84. http://dx.doi.org/10.1108/aeat-12-2019-0245.

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Purpose This paper aims to present a hybrid logical-arithmetic approach for selecting optimal flight routes. It can be used in the framework of free route airspace (FRA), which is aimed at achieving higher efficiency of air traffic management. Design/methodology/approach At the first stage, an initial subset of flight routes is selected that are promising alternatives with respect to single numerical criteria. At the second stage, a hybrid multicriteria decision model is constructed, consisting of numerical criteria and additional linguistic criteria. At the third stage, the numerical and linguistic criteria are integrated into a crisp decision matrix for determining the final ranking using the technique for order preferences by similarity to an ideal solution (TOPSIS) method. Findings The considered decision-making problem involves different kinds of criteria. Numerical (objective) criteria are given as real numbers. Linguistic (subjective) criteria are expressed with the help of fuzzy linguistic values. In consequence, a (logical) reasoning process prior to performing an (arithmetic) optimization procedure is necessary. Furthermore, a uniform optimization procedure requires a way of combining numerical and linguistic attributes. Practical implications The proposed approach can be applied to solving various multicriteria decision-making problems, where both objective and subjective criteria are taken into account. Originality/value First, a fuzzy information system that includes linguistic condition attributes is constructed. Second, a fuzzy inference system that is necessary for determining the resulting fuzzy criterion “turbulence conditions” for all flight routes is introduced. Finally, a way of combining numerical and linguistic criteria is proposed. This is done by converting values of fuzzy attributes into crisp ones, basing on the preferences of a decision-maker.

29

BOGETOFT, PETER, und JORGEN TIND. „Dual Decomposition Schemes for Multicriteria Decision Making“. IMA Journal of Management Mathematics 2, Nr. 4 (1989): 269–79. http://dx.doi.org/10.1093/imaman/2.4.269.

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30

Peng, Yi, Yong Zhang, Gang Kou, Jun Li und Yong Shi. „Multicriteria Decision Making Approach for Cluster Validation“. Procedia Computer Science 9 (2012): 1283–91. http://dx.doi.org/10.1016/j.procs.2012.04.140.

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31

GRABISCH, Michel, Christophe LABREUCHE und Mustapha RIDAOUI. „On importance indices in multicriteria decision making“. European Journal of Operational Research 277, Nr. 1 (August 2019): 269–83. http://dx.doi.org/10.1016/j.ejor.2019.02.035.

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32

Engau, Alexander, und Margaret M. Wiecek. „2D decision-making for multicriteria design optimization“. Structural and Multidisciplinary Optimization 34, Nr. 4 (12.12.2006): 301–15. http://dx.doi.org/10.1007/s00158-006-0078-y.

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33

Shukla, Vikas, Guillaume Auriol und Keith W. Hipel. „Multicriteria Decision-Making Methodology for Systems Engineering“. IEEE Systems Journal 10, Nr. 1 (März 2016): 4–14. http://dx.doi.org/10.1109/jsyst.2014.2344635.

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34

Kobryń, Andrzej. „Multicriteria Decision Making in Geodetic Network Design“. Journal of Surveying Engineering 146, Nr. 1 (Februar 2020): 04019018. http://dx.doi.org/10.1061/(asce)su.1943-5428.0000301.

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35

Potomkin, M. M. „Evaluating the Validity of Multicriteria Decision-Making“. Cybernetics and Systems Analysis 54, Nr. 6 (November 2018): 930–35. http://dx.doi.org/10.1007/s10559-018-0095-z.

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36

Kahraman, Cengiz, Sezi Cevik Onar und Basar Oztaysi. „Fuzzy Multicriteria Decision-Making: A Literature Review“. International Journal of Computational Intelligence Systems 8, Nr. 4 (Mai 2015): 637–66. http://dx.doi.org/10.1080/18756891.2015.1046325.

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37

Zaychenko, Olena Yu, und Yuriy P. Zaychenko. „Multicriteria decision-making problems under fuzzy conditions“. System research and information technologies, Nr. 4 (15.11.2016): 79–87. http://dx.doi.org/10.20535/srit.2308-8893.2016.4.08.

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38

San Cristóbal, José Ramón. „Contractor Selection Using Multicriteria Decision-Making Methods“. Journal of Construction Engineering and Management 138, Nr. 6 (Juni 2012): 751–58. http://dx.doi.org/10.1061/(asce)co.1943-7862.0000488.

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39

Yager, Ronald R., und Naif Alajlan. „Multicriteria Decision-Making With Imprecise Importance Weights“. IEEE Transactions on Fuzzy Systems 22, Nr. 4 (August 2014): 882–91. http://dx.doi.org/10.1109/tfuzz.2013.2277734.

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40

Yager, Ronald R. „Pythagorean Membership Grades in Multicriteria Decision Making“. IEEE Transactions on Fuzzy Systems 22, Nr. 4 (August 2014): 958–65. http://dx.doi.org/10.1109/tfuzz.2013.2278989.

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41

Adetunji, Oluwatomi, John Bischoff und Christopher J. Willy. „Managing system obsolescence via multicriteria decision making“. Systems Engineering 21, Nr. 4 (30.04.2018): 307–21. http://dx.doi.org/10.1002/sys.21436.

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42

Satyadas, Antony, und H. C. Chen. „Multicriteria multigoal decision making - the fuzzy paradigm“. Computers & Industrial Engineering 23, Nr. 1-4 (November 1992): 393–96. http://dx.doi.org/10.1016/0360-8352(92)90144-9.

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43

Aggarwal, Manish. „Linguistic Discriminative Aggregation in Multicriteria Decision Making“. International Journal of Intelligent Systems 31, Nr. 6 (06.01.2016): 529–55. http://dx.doi.org/10.1002/int.21793.

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44

Aggarwal, Manish. „Soft information set for multicriteria decision making“. International Journal of Intelligent Systems 34, Nr. 12 (09.10.2019): 3241–59. http://dx.doi.org/10.1002/int.22192.

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45

Mosavi, A. „A Multicriteria Decision Making Environment for Engineering Design and Production Decision-Making“. International Journal of Computer Applications 69, Nr. 1 (17.05.2013): 26–38. http://dx.doi.org/10.5120/11807-7457.

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46

Mitkus, Sigitas, und Eva Trinkūnienė. „MODELS OF INDICATOR SYSTEMS OF CONSTRUCTION CONTRACTION AGREEMENTS“. JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 12, Nr. 4 (31.12.2006): 327–35. http://dx.doi.org/10.3846/13923730.2006.9636410.

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A number of multicriteria decisions must be made during construction investment processes. A number of support systems for multicriteria tasks of construction investment processes are available. A part of them is reviewed in this article. Effectiveness of the construction investment process is greatly influenced by an appropriate formulation of a construction contraction agreement (CCA). In order to formulate the effective contraction agreements, multicriteria techniques for evaluating and comparing CCAs must be created. Besides technical, organisational and economic aspects of construction, legal aspects of a CCA must be also analysed in order to prepare such techniques. Therefore, legal decision making systems are also reviewed in the article. A conclusion can be made from the review that legal decision making systems for CCAs are not available currently. One of the main tasks in the creation of multicriteria support systems is the formation of a multicriteria indicator system. Three models of multicriteria indicator systems of CCAs are analysed in the article, and the best model for creating multicriteria evaluation technique is determined. On the basis of this model, significance of indicators should be determined and a multicriteria decision support system should be created in further research stages.

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Hu, Jianmiao, und Chong Chen. „Exploration of Normal Stochastic Multicriteria Decision-Making and Legal Dilemma in Project Investment“. Mathematical Problems in Engineering 2021 (19.11.2021): 1–10. http://dx.doi.org/10.1155/2021/1082186.

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The purpose is to help enterprise managers make more accurate, scientific, and objective decisions on the optimal supplier in project investment, improve investment return and overall efficiency, and reduce investment risk. Following an introduction of normal stochastic multicriteria decision-making, a normal stochastic multicriteria decision-making method is proposed based on Bayesian Network- (BN-) Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to help investors select suppliers. First, product cost, product quality, and product service are selected as evaluation indexes, and then, a supplier evaluation model is built based on the BN concept for expert evaluation. Second, the evaluation information is integrated. Based on the TOPSIS normal stochastic multicriteria decision-making method, the optimal supplier is selected under the comparison of the fit degree between each supplier and the best and worst solution. Finally, the optimal solution is determined and verified by a simulation example. The results show that in the problem of supplier selection in the retail industry, the BN model can truly provide feedback the product quality, cost, and service situation of each supplier and fully consider their behavior diversity. The normal stochastic multicriteria decision-making method based on TOPSIS can select the suppliers that are most in line with the investors’ development goals, especially in the face of good and bad suppliers. BN-based TOPSIS normal stochastic multicriteria decision-making method has strong logic and efficiency, is easy to operate, and has high practical value. Furthermore, the relevant laws of project investment decision-making are discussed through consultation on the relevant literature, thus providing help for the construction of relevant laws in the future.

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Kottemann, Jeffrey E., und Fred D. Davis. „Decisional Conflict and User Acceptance of Multicriteria Decision-Making Aids“. Decision Sciences 22, Nr. 4 (September 1991): 918–26. http://dx.doi.org/10.1111/j.1540-5915.1991.tb00371.x.

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Nesterenko, Oleksandr, Igor Netesin, Valery Polischuk und Yuri Selin. „Graph-based decision making for varying complexity multicriteria problems“. Computer Science Journal of Moldova 30, Nr. 3(90) (Dezember 2022): 391–412. http://dx.doi.org/10.56415/csjm.v30.21.

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In the modern world in various spheres of activity, the number of problems that need multi-criteria decision-making (MCDM) is constantly increasing. Researchers and experts offer a number of approaches to MCDM process in such tasks; in particular, most of them are based on expert methods. However, in practice, these methods require significant intellectual effort of experts and organizational and technical workload during the expert survey, and also usually take a long time. At the same time, it is not always possible for experts to use certain characteristics of alternatives, which also carries the risk of making decisions based on unfounded expert opinions. Therefore, such methods and tools should be clear and informative and at the same time easy to use to ensure the efficiency and effectiveness of their use. We offer a graph-based approach to expert decision-making and information visualization processes that meets these requirements and can significantly improve the efficiency of decision-making in multi-criteria selection tasks.

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Freitas, Antonio Luiz Freitas, Murilo Alves de Albuquerque Damasceno, Cristiany Raab de Souza, Joana Karolyni Cabral Peixoto, Thomas Edson Espíndola Gonçalo und Eric Amaral Ferreira. „GIS Multicriteria decision making model for traffic accidents management“. Revista Produção e Desenvolvimento 8, Nr. 1 (27.12.2022): e613. http://dx.doi.org/10.32358/rpd.2022.v8.613.

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Objective: this study suggests a multicriteria model for decision-making assistance that, by using spatial representation, can provide decision aid for the traffic management process in a county, hence facilitating improved decision-making. The decision-making process can be supported by multicriteria decision aid technologies and geographic information systems (GIS). Methodology/Approach: A multicriteria decision-making model is developed to categorize the city's roads according to their importance to the population's safety. For classification, the PROMSORT method (PROMETHEE Sorting) is proposed, and the results are reflected in the GIS. Results: It presents a numerical application for the city of Mossoró/RN, rating the roads and providing information for public managers' decision-making. Limitations: the model application is contingent upon the efforts of managers who can begin working in the traffic and urban mobility management sector after each public election to comprehend its significance. Originality/Value of the article: The GIS application generates a viable study method by facilitating the decision-making process and allowing the geographic visualization of data pertaining to the region under study.