Abstract
The main objective of multiple criteria decision making models is to select an alternative, from a finite number, regarding a set of pre-defined criteria. Usually, this type of problems includes two main tasks, rating the alternatives regarding each criterion and then ranking them. Once a decision is made (alternative selected) the problem is solved. However, for situations involving reaching consensus or requiring several steps before reaching a final decision, we must consider a dynamic and adaptable decision model, which considers previous solutions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Bibliography
E. Triantaphyllou, Multi criteria Decision Making Method: a Comparative Study, vol. 44, Applied Optimization Series, Kluwer Academic Publishers, (2002).
T. Ross, Fuzzy Logic with Engineering Applications, John Wiley & Sons, (2004).
R. Ribeiro, Fuzzy multiple attribute decision making: a review and new preference elicitation techniques, Fuzzy Sets and Systems, 78(2), 155–181, (1996).
J. Busemeyer and J. Townsend, Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment, Psychological Review, 100, 432–432, (1993).
G. P. Richardson and A. L. Pugh III, Introduction to System DynamicsModeling with DYNAMO, Productivity Press, Portland, Oregon, (1981).
K. Cios, W. Pedrycz, and R. W. Swiniarski, Data Mining Methods for Knowledge Discovery, Kluwer Academic Publishers, (1998).
S.-J. Chen and C.-L. Hwang, Fuzzy Multiple Attribute Decision Making, Methods and Applications, Number 375 in Lectures Notes in Economics andMathematical Systems, Springer-Verlag, (1992).
T. C. Pais, R. A. Ribeiro, Y. Devouassoux, and S. Reynaud, Dynamic ranking algorithm for landing site selection, In eds. L. Magdalena, M. Ojeda-Aciego, and J. L. Verdegay, Proceedings of the 12th International Conference on Information Processing and Manage ment of Uncertainty in Knowledge-Base Systems (IPMU), pp. 608–613, (2008).ISBN 978-84-612-3061-7. URL: http://www.gimac.uma.es/ipmu08/proceedings/papers/080-PaisEtAl.pdf.
R. Roe, J. Busemeyer, and J. Townsend, Multialternative decision field theory: a dynamic connectionist model of decision making, Psychol Rev., 108(2), 370–92, (2001).
A. Diederich, Mdft account of decision making under time pressure, Psychonomic Bulletin & Review, 10(1), 157–166, (2003).
A. Diederich, Dynamic stochastic models for decision making under time constraints, Journal of Mathematical Psychology, 41(3), 260–274, (1997).
Y. Devouassoux, S. Reynaud, G. Jonniaux, R. A. Ribeiro, and T. C. Pais, Hazard avoidance developments for planetary exploration, In GNC 2008: 7th International ESA Conference on Guidance, Navigation & Control Systems, (2008).
N. Viana, A. Pereira, R. A. Ribeiro, and A. Donati, Handling missing values in solar array performance degradation forecasting, In Proceedings of the 15th Mini-EURO conference on Managing Uncertainty in Decision Support Models (MUDSM 2004), Coimbra, Portugal (September, 2004).
R. Ribeiro and R. Marques Pereira, Generalized mixture operators using weighting functions: A comparative study with wa and owa, European Journal of Operational Research, 145 (2), 329–342, (2003).
R. Pereira and R. Ribeiro, Aggregation with generalized mixture operators using weighting functions, Fuzzy Sets and Systems, 137(1), 43–58, (2003).
R. R. Yager and A. Rybalov, Uninorm aggregation operators, Fuzzy Sets Systems, 80(1), 111–120, (1996).ISSN 0165-0114. http://dx.doi.org/10.1016/0165-0114(95)00133-6.
R. Yager and A. Rybalov, Full reinforcement operators in aggregation techniques, IEEE Transactions on Systems, Man and Cybernetics, Part B, 28 (6), 757–769, (1998).
H. Zimmermann, Fuzzy Set Theory–and Its Applications, Kluwer Academic Publishers, (2001).
T. Jean-Marius and S. Strandmoe, Integrated vision and navigation for a planetary lander, In 49th International Astronautical Congress, Melbourne, Australia, Sept-Oct, (1998).
T. C. Pais, R. A. Ribeiro, Y. Devouassoux, and S. Reynaud, Regions rating for selecting spacecraft landing sites, In eds. D. Ruan, J. Montero, J. Lu, L. Mart´ınez, P. D’hondt, and E. E. Kerre, Computational Intelligence in Decision and Control – Proceedings of the 8th International FLINS Conference, vol. 1, World Scientific Proceedings Series on ComputerEngineering and Information Science, pp. 1039–1044, Singapore (Aug, 2008), World Scientific. ISBN 978-981-279-946-3.
R. M. Dawes, Social selection based on multidimensional criteria, Journal of Abnormal and Social Psychology, 68(1), 104–109, (1964).
T. Pais and R. A. Ribeiro, Contributions for dynamic multicriteria decision making models, In International Fuzzy Systems Association World Congress – IFSA, Lisbon, Portugal, (2009)
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2010 Atlantis Press/World Scientific
About this chapter
Cite this chapter
Pais, T.C., Ribeiro, R.A., Simões, L.F. (2010). Uncertainty in Dynamically Changing Input Data. In: Computational Intelligence in Complex Decision Systems. Atlantis Computational Intelligence Systems, vol 2. Atlantis Press. https://doi.org/10.2991/978-94-91216-29-9_2
Download citation
DOI: https://doi.org/10.2991/978-94-91216-29-9_2
Publisher Name: Atlantis Press
Online ISBN: 978-94-91216-29-9
eBook Packages: Computer ScienceComputer Science (R0)
