Готові списки джерел за темами / Multicriteria decision making

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Добірка наукової літератури з теми "Multicriteria decision making"

Автор: Grafiati
Опубліковано: 4 червня 2021
Оновлено: 5 березня 2023

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Статті в журналах з теми "Multicriteria decision making"

1

Hendriks, Margriet M. W. B., Jan H. de Boer, Age K. Smilde, and Durk A. Doornbos. "Multicriteria decision making." Chemometrics and Intelligent Laboratory Systems 16, no. 3 (December 1992): 175–91. http://dx.doi.org/10.1016/0169-7439(92)80036-4.

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Yager, R. R. "Modeling Prioritized Multicriteria Decision Making." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, no. 6 (December 2004): 2396–404. http://dx.doi.org/10.1109/tsmcb.2004.837348.

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3

Novikova, Natalia M., and Irina I. Pospelova. "Multicriteria decision making under uncertainty." Mathematical Programming 92, no. 3 (May 1, 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, no. 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, and Muhammad Ilyas. "A Review on Multicriteria Decision Support System and Industrial Internet of Things for Source Code Transformation." Scientific Programming 2021 (January 13, 2021): 1–9. http://dx.doi.org/10.1155/2021/6661272.

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Анотація:
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.
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6

Piegat, Andrzej, and 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.
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Yager, Ronald R. "Multicriteria Decision-Making Using Fuzzy Measures." Cybernetics and Systems 46, no. 3-4 (April 3, 2015): 150–71. http://dx.doi.org/10.1080/01969722.2015.1012884.

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Janssen, Ron, and Marjan Van Herwijnen. "A toolbox for multicriteria decision-making." International Journal of Environmental Technology and Management 6, no. 1/2 (2006): 20. http://dx.doi.org/10.1504/ijetm.2006.008252.

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Grabisch, Michel. "Fuzzy integral in multicriteria decision making." Fuzzy Sets and Systems 69, no. 3 (February 1995): 279–98. http://dx.doi.org/10.1016/0165-0114(94)00174-6.

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Cziner, Krisztina, Mari Tuomaala, and Markku Hurme. "Multicriteria decision making in process integration." Journal of Cleaner Production 13, no. 5 (April 2005): 475–83. http://dx.doi.org/10.1016/j.jclepro.2003.09.003.

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Дисертації з теми "Multicriteria decision making"

1

Mota, Pedro Jorge Gomes. "Comparative analysis of multicriteria decision making methods." Master's thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/11263.

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Анотація:
Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores
The main objective of this dissertation is to perform a Comparative Analysis of different Multicriteria Decision Making Methods applied to real-world problems, in order to produce relevant information to enable the incorporation of those methods on computational platforms. The current document presents a simple case study concerning a decision support application targeted for a real problem regarding retrofitting alternatives of a building with energy efficiency impact. The application process was started with the selection of two Multicriteria Decision Making Methods guided by a preexisting framework, and resulted in the choice of AHP and PROMETHEE II methodologies. These two methods were then combined with three different decision maker profiles (Conservative, Moderate and Aggressive) created by means of risk assessment profiling techniques for portfolio allocation. Afterwards, the chosen decision criteria were disposed in a Risk Pyramid according to their inherent level of risk regarding project evaluation. A match was then performed between the decision maker profiles and each criterion, so as to define a proper set of weights for the decision criteria and preference functions, with corresponding preference and indifference thresholds. Finally, three different sets of results (one for each decision maker profile) were produced using appropriate software, and a Sensitivity Analysis was performed over the criteria to understand their influence on the solution. The general conclusion of this Comparative Analysis is that the increase in the preference modelling ability of the methods brings up the least expected alternatives as recommendations for the decision maker. Besides, we have concluded that the decision profiles that allocate bigger weights to the riskiest criteria are the ones that produce the more dispersed set of results within each method application and within each decision maker profile.
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2

Van, Dyk Theron Van Zyl. "Decision support systems for solving discrete multicriteria decision making problems." Master's thesis, University of Cape Town, 1992. http://hdl.handle.net/11427/14300.

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Анотація:
Includes bibliography.
The aim of this study was the design and implementation of an interactive decision support system, assisting a single decision maker in reaching a satisfactory decision when faced by a multicriteria decision making problem. There are clearly two components involved in designing such a system, namely the concept of decision support systems (DSS) and the area of multicriteria decision making (MCDM). The multicriteria decision making environment as well as the definitions of the multicriteria decision making concepts used, are discussed in chapter 1. Chapter 2 gives a brief historical review on MCDM, highlighting the origins of some of the more well-known methods for solving MCDM problems. A detailed discussion of interactive decision making is also given. Chapter 3 is concerned with the DSS concept, including a historical review thereof, a framework for the design of a DSS, various development approaches as well as the components constituting a decision support system. In chapter 4, the possibility of integrating the two concepts, MCDM and DSS, are discussed. A detailed discussion of various methodologies for solving MCDM problems is given in chapter 5. Specific attention is given to identifying the methodologies to be implemented in the DSS. Chapter 6 can be seen as a theoretical description of the system developed, while Chapter 7 is concerned with the evaluation procedures used for testing the system. A final summary and concluding remarks are given in Chapter 8.
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3

Chiu, Gerald Wing Cheong. "Multicriteria decision making models for studying construction safety /." access full-text access abstract and table of contents, 2005. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?mphil-bc-b19887498a.pdf.

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Анотація:
Thesis (M.Phil.)--City University of Hong Kong, 2005.
"Submitted to Department of Building and Construction in partial fulfillment of the requirements for the degree of Master of Philosophy" Includes bibliographical references (leaves 239-241)
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4

Durbach, Ian N. "The treatment of uncertainty in multicriteria decision making." Master's thesis, University of Cape Town, 2003. http://hdl.handle.net/11427/15424.

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Анотація:
Bibliography: leaves 142-149.
The nature of human decision making dictates that a decision must often be considered under conditions of uncertainty. Decisions may be influenced by uncertain future events, doubts regarding the precision of inputs, doubts as to what the decision maker considers important, and many other forms of uncertainty. The multicriteria decision models that are designed to facilitate and aid decision making must therefore consider these uncertainties if they are to be effective. In this thesis, we consider the treatment of uncertainty in multicriteria decision making (MCDM), with a specific view to investigating the types of uncertainty that are most relevant to MCDM, [and] how the uncertainties identified as relevant may be treated by various different MCDM methodologies.
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5

Suhendra. "Integrated improvement of distillation unit using multicriteria decision making analysis." [S.l.] : [s.n.], 2007. http://se6.kobv.de:8000/btu/volltexte/2007/170.

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Heynes, Wynford Gustav. "Selection of multicriteria decision making methodologies in scenario based planning." Master's thesis, University of Cape Town, 1995. http://hdl.handle.net/11427/14722.

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Анотація:
Bibliography: leaves 131-136.
This dissertation investigates the application of Multicriteria Decision Making (MCDM) methodologies to the area of scenario based policy planning. We examine how the tools of MCDM can be used to develop a Decision Support System (DSS) that would allow management or policy planners to resolve conflicting goals and interests. Ideally, the resolution would be obtained by the various decision makers (DMs) in such a manner, that it satisfies all the relevant interest groupings at a maximum level of achievement for all concerned. This is not always possible and compromises need to be made that are fair and equitable to all the relevant interests. Stewart et al. (1993), in a report entitled: "Scenario Based Multicriteria Policy Planning for Water Management in South Africa", develop the principles of a procedure for implementing scenario based multicriteria policy planning. Their iterative procedure is illustrated in figure 2.1, chapter 2, of this paper. In this dissertation, we refine certain parts of this procedure and the two areas in particular that we have looked at are: (1) filtering a large set of policy scenarios (Background Set), that could be a continuum, to form a smaller set (Foreground Set), and (2) further reducing the smaller set to form a solution set of policy scenarios. (The generic terms "Background Set" and "Foreground Set" are defined in section 2.1 of chapter 2.) The main objectives of this study were therefore mainly twofold and are as follows: (1) to determine what MCDM methods are relevant to natural resources management (using water as a case study), and (2) to investigate how these methods need to be adopted for use in an interactive DSS. We address the first objective by surveying the literature in an attempt to identify potential MCDM approaches that are suitable to (i) reduce a large set of alternatives, analogous to the Background Set, to a more manageable smaller set, analogous to the Foreground Set of alternatives, and (ii) refine this Foreground Set in order to present the DMs with a solution set of alternatives from which University of Cape Town they will make their final selection. The literature has until now not dealt explicitly with these two issues and we had to adapt certain MCDM approaches, many of which have been developed in a linear programming context, to suit our purposes.
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Hodgkin, Julie. "Provision of intelligent user support in decision support systems." Thesis, University of Strathclyde, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366908.

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Mostofi, Camare Hooman. "Multicriteria Decision Evaluation of Adaptation Strategies for Vulnerable Coastal Communities." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/20112.

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Анотація:
According to the IPCC (2007) fourth assessment report, small islands and coastal communities have a set of characteristics that makes them very vulnerable to climate change impacts, mainly sea-level rise and storm surges. Coastal hazards including inundation, salinisation of the water supply, and erosion threaten vital infrastructure that support coastal communities. Although Canada has the longest coastline in the world, little work has been done on impacts of climate change and adaptation to these impacts in the Canadian coastal zones. This research is part of an International Community-University Research Alliance (ICURA) C-Change, project to develop a multicriteria decision evaluation and support for the systems analysis of adaptation options for coastal communities toward adapting to environmental changes. This study estimates the vulnerability of coastal communities with respect to their environmental, economic, social, and cultural dimensions. It also applies a group version of the Analytical Hierarchy Process for identifying decisions that various stakeholders make on suggested adaptation strategies. This study develops a methodological framework that is applicable to various coastal and small island contexts. The application of the proposed framework is further discussed in a case study conducted on the communities of Charlottetown, Prince Edward Island (PEI), and Little Anse on Isle Madame, Nova Scotia. Specifically, the state of the Little Anse breakwater is analyzed and new adaptation options are presented and evaluated. This research has illustrated and applied a process of decision evaluation and support that explicitly engages multiple participants and critieria in complex problems situations involving environmental change in coastal communities.
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9

Wagholikar, Amol S., and N/A. "Acquisition of Fuzzy Measures in Multicriteria Decision Making Using Similarity-based Reasoning." Griffith University. School of Information and Communication Technology, 2007. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20071214.152324.

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Анотація:
Continuous development has been occurring in the area of decision support systems. Modern systems focus on applying decision models that can provide intelligent support to the decision maker. These systems focus on modelling the human reasoning process in situations requiring decision. This task may be achieved by using an appropriate decision model. Multicriteria decision making (MCDM) is a common decision making approach. This research investigates and seeks a way to resolve various issues associated with the application of this model. MCDM is a formal and systematic decision making approach that evaluates a given set of alternatives against a given set of criteria. The global evaluation of alternatives is determined through the process of aggregation. It is well established that the aggregation process should consider the importance of criteria while determining the overall worth of an alternative. The importance of individual criteria and of sub-sets of the criteria affects the global evaluation. The aggregation also needs to consider the importance of the sub-set of criteria. Most decision problems involve dependent criteria and the interaction between the criteria needs to be modelled. Traditional aggregation approaches, such as weighted average, do not model the interaction between the criteria. Non-additive measures such as fuzzy measures model the interaction between the criteria. However, determination of non-additive measures in a practical application is problematic. Various approaches have been proposed to resolve the difficulty in acquisition of fuzzy measures. These approaches mainly propose use of past precedents. This research extends this notion and proposes an approach based on similarity-based reasoning. Solutions to the past problems can be used to solve the new decision problems. This is the central idea behind the proposed methodology. The methodology itself applies the theory of reasoning by analogy for solving MCDM problems. This methodology uses a repository of cases of past decision problems. This case base is used to determine the fuzzy measures for the new decision problem. This work also analyses various similarity measures. The illustration of the proposed methodology in a case-based decision support system shows that interactive models are suitable tools for determining fuzzy measures in a given decision problem. This research makes an important contribution by proposing a similarity-based approach for acquisition of fuzzy measures.
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10

Wagholikar, Amol S. "Acquisition of Fuzzy Measures in Multicriteria Decision Making Using Similarity-based Reasoning." Thesis, Griffith University, 2007. http://hdl.handle.net/10072/365403.

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Анотація:
Continuous development has been occurring in the area of decision support systems. Modern systems focus on applying decision models that can provide intelligent support to the decision maker. These systems focus on modelling the human reasoning process in situations requiring decision. This task may be achieved by using an appropriate decision model. Multicriteria decision making (MCDM) is a common decision making approach. This research investigates and seeks a way to resolve various issues associated with the application of this model. MCDM is a formal and systematic decision making approach that evaluates a given set of alternatives against a given set of criteria. The global evaluation of alternatives is determined through the process of aggregation. It is well established that the aggregation process should consider the importance of criteria while determining the overall worth of an alternative. The importance of individual criteria and of sub-sets of the criteria affects the global evaluation. The aggregation also needs to consider the importance of the sub-set of criteria. Most decision problems involve dependent criteria and the interaction between the criteria needs to be modelled. Traditional aggregation approaches, such as weighted average, do not model the interaction between the criteria. Non-additive measures such as fuzzy measures model the interaction between the criteria. However, determination of non-additive measures in a practical application is problematic. Various approaches have been proposed to resolve the difficulty in acquisition of fuzzy measures. These approaches mainly propose use of past precedents. This research extends this notion and proposes an approach based on similarity-based reasoning. Solutions to the past problems can be used to solve the new decision problems. This is the central idea behind the proposed methodology. The methodology itself applies the theory of reasoning by analogy for solving MCDM problems. This methodology uses a repository of cases of past decision problems. This case base is used to determine the fuzzy measures for the new decision problem. This work also analyses various similarity measures. The illustration of the proposed methodology in a case-based decision support system shows that interactive models are suitable tools for determining fuzzy measures in a given decision problem. This research makes an important contribution by proposing a similarity-based approach for acquisition of fuzzy measures.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
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Книги з теми "Multicriteria decision making"

1

Gal, Tomas, Theodor J. Stewart, and Thomas Hanne, eds. Multicriteria Decision Making. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5025-9.

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2

Vincke, Philippe. Multicriteria decision-aid. Chichester [England]: Wiley, 1992.

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3

Pedrycz, Witold, Petr Ekel, and Roberta Parreiras. Fuzzy Multicriteria Decision-Making. Chichester, UK: John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470974032.

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4

Multicriteria optimization. 2nd ed. Berlin: Springer, 2005.

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5

Vincke, Philippe. Multicriteria decision-aid. Chichester: Wiley, 1992.

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6

Stewart, Theodor J., and Robin C. van den Honert, eds. Trends in Multicriteria Decision Making. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-45772-2.

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7

Roy, Bernard. Multicriteria methodology for decision aiding. Dordrecht: Kluwer Academic Publishers, 1996.

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Multicriteria Methodology for Decision Aiding. Boston, MA: Springer US, 1996.

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9

International Conference on MCDM (11th 1994 Coimbra, Portugal). Multicriteria analysis. New York: Springer-Verlag, 1997.

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10

Bernard, Roy. Multicriteria methodology for decision aiding. Dordrecht, Netherlands: Kluwer Academic Publishers, 1996.

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Частини книг з теми "Multicriteria decision making"

1

Eiselt, H. A., and C. L. Sandblom. "Multicriteria Decision Making." In Decision Analysis, Location Models, and Scheduling Problems, 23–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24722-7_2.

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Michalewicz, Zbigniew, and David B. Fogel. "Multicriteria Decision-Making." In How to Solve It: Modern Heuristics, 435–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-07807-5_16.

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Chan, Yupo. "Multicriteria Decision Making." In Location Theory and Decision Analysis, 213–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-15663-2_5.

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Eiselt, H. A., and Carl-Louis Sandblom. "Multicriteria Decision Making." In Springer Texts in Business and Economics, 333–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31054-6_10.

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Gonzales, Christophe, and Patrice Perny. "Multicriteria Decision Making." In A Guided Tour of Artificial Intelligence Research, 519–48. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-06164-7_16.

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Eiselt, H. A., Vladimir Marianov, and Joyendu Bhadury. "Multicriteria Decision Making." In International Series in Operations Research & Management Science, 37–70. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-23876-5_3.

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Carlsson, Christer, and Robert Fuller. "Interdependence In Multiple Criteria Decision Making." In Multicriteria Analysis, 25–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60667-0_4.

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Stewart, Theodor J. "Scenario Analysis and Multicriteria Decision Making." In Multicriteria Analysis, 519–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60667-0_49.

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Chakhar, Salem, and Vincent Mousseau. "Multicriteria Decision Making, Spatial." In Encyclopedia of GIS, 747–53. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_839.

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Chakhar, Salem, and Vincent Mousseau. "Multicriteria Decision Making, Spatial." In Encyclopedia of GIS, 1–9. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23519-6_839-2.

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Тези доповідей конференцій з теми "Multicriteria decision making"

1

Tinguaro Rodriguez, J., Begona Vitoriano, Javier Montero, and Daniel Gomez. "Modelling bipolar multicriteria decision making." In 2009 IEEE Symposium on Computational Intelligence in Milti-Criteria Decision-Making (MCDM). IEEE, 2009. http://dx.doi.org/10.1109/mcdm.2009.4938837.

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Ali, Rekik, Temani Moncef, and Ghedira Khaled. "Fuzzy model for multicriteria decision making." In 2014 Information and Communication Technologies Innovation and Application (ICTIA). IEEE, 2014. http://dx.doi.org/10.1109/ictia.2014.7883753.

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Wang, Chih-Huang, and Shyi-Ming Chen. "A Generalized Model for Multicriteria Decision Making." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370442.

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Homenda, Wladyslaw, Agnieszka Jastrzebska, Witold Pedrycz, Fusheng Yu, and Yihan Wang. "Multicriteria Decision Making: Scale, Polarity, Symmetry, Interpretability." In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2020. http://dx.doi.org/10.1109/fuzz48607.2020.9177705.

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Andrienko, Gennady L., and Natalia V. Andrienko. "Interactive visual tools for spatial multicriteria decision making." In the Working Conference. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/1556262.1556282.

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Dror-Rein, Elana, and Harvey B. Mitchell. "Fuzzy multicriteria decision making in the assignment problem." In Optical Science, Engineering and Instrumentation '97, edited by Oliver E. Drummond. SPIE, 1997. http://dx.doi.org/10.1117/12.279535.

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Zaabar, I., Y. Beauregard, and M. Paquet. "Using Multicriteria Decision Making Methods to Manage Systems Obsolescence." In 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2018. http://dx.doi.org/10.1109/ieem.2018.8607784.

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Kong, Feng, and Hongyan Liu. "The Relative Grey Relation Closeness Multicriteria Decision Making Method." In Sixth International Conference on Intelligent Systems Design and Applications. IEEE, 2006. http://dx.doi.org/10.1109/isda.2006.51.

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"MULTICRITERIA DECISION MAKING IN BALANCED MODEL OF FUZZY SETS." In 4th International Conference on Informatics in Control, Automation and Robotics. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0001620500400046.

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Poleshchuk, Olga M. "Novel Approach to Multicriteria Decision Making Under Z-Information." In 2019 International Russian Automation Conference. IEEE, 2019. http://dx.doi.org/10.1109/rusautocon.2019.8867607.

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