Abstract
In multiobjective optimization problems, the identified Pareto Frontiers and Sets often contain too many solutions, which make it difficult for the decision maker to select a preferred alternative. To facilitate the selection task, decision making support tools can be used in different instances of the multiobjective optimization search to introduce preferences on the objectives or to give a condensed representation of the solutions on the Pareto Frontier, so as to offer to the decision maker a manageable picture of the solution alternatives. This paper presents a comparison of some a priori and a posteriori decision making support methods, aimed at aiding the decision maker in the selection of the preferred solutions. The considered methods are compared with respect to their application to a case study concerning the optimization of the test intervals of the components of a safety system of a nuclear power plant. The engine for the multiobjective optimization search is based on genetic algorithms.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
ATKOSoft, Survey of Visualization Methods and Software Tools, (1997).
X. Blasco, J.M. Herrero and J. Sanchis, M. Martínez, Multiobject. Optim., Inf. Sci., 178, 3908–3924 (2008).
J. Branke, T. Kaubler, and H. Schmeck, Adv. Eng. Software, 32, 499 (2001).
J. Branke, T. Kaubler, and H. Schmeck, Tech. Rep. TR no.399, Institute AIFB, University of Karlsruhe, Germany (2000).
S. Chiu, J. of Intell. & Fuzzy Syst., 2 (3), 1240 (1994).
C.A. Coello Coello, C, in 2000 Congress on Evolut. Comput. (IEEE Service Center, Piscataway NJ, 2000), Vol. 1, p. 30.
W.W. Cooper, L.M. Seiford, and K. Tone, Data Envelopment Analysis: a Comprehensive Text with Models, Applications, References, and DEA-Solver Software (Springer, Berlin, 2006).
L. Fausett, Fundamentals of Neural Networks: Architectures, Algorithms, and Applications (Prentice-Hall, Englewood Cliffs, 1994).
P. Giuggioli Busacca, M. Marseguerra, and E. Zio, Reliab. Eng. Syst. Saf., 72, 59 (2001).
ICRP Publication 60, Annals of the ICRP, 21, 1 (1991).
Z. Li, H. Liao, and D.W. Coit, Reliab. Eng. Syst. Saf., 94, 1585 (2009).
S. Martorell, S. Carlos, A. Sanchez, and V. Serradell, Reliab. Eng. Syst. Saf., 67, 215 (2000).
US Nuclear Regulatory Commission, Rates of Initiating Events at United States Nuclear Power Plants: 1987–1995, NUREG/CR-5750 (1999).
L. Rachmawati and D. Srinivasan, in Congress on Evolut. Comput., 2006 (IEEE Conference Publications, 2006), p. 962–968.
P.J. Rousseeuw, J. Comput. Appl. Math., 20, 53 (1987).
P. Rousseeuw, E. Trauwaert, and L. Kaufman, Belgian J. of Oper. Res., Stat. and Comput. Sci., 29 (3), 35 (1989).
J.E. Yang, M.J. Hwang, T.Y. Sung, and Y. Jin, Reliab. Eng. Syst. Saf., 65, 229 (1999).
E. Zio, P. Baraldi, and N. Pedroni, Reliab. Eng. Syst. Saf., 94, 432 (2009).
E. Zio and R. Bazzo, Submitted to Inf. Sci. (2009).
E. Zio and R. Bazzo, Eur. J. of Oper. Res., 210 (3), 624 (2011).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Atlantis Press
About this chapter
Cite this chapter
Zio, E., Bazzo, R. (2012). A Comparison of Methods for Selecting Preferred Solutions in Multiobjective Decision Making. In: Kahraman, C. (eds) Computational Intelligence Systems in Industrial Engineering. Atlantis Computational Intelligence Systems, vol 6. Atlantis Press, Paris. https://doi.org/10.2991/978-94-91216-77-0_2
Download citation
DOI: https://doi.org/10.2991/978-94-91216-77-0_2
Publisher Name: Atlantis Press, Paris
Print ISBN: 978-94-91216-76-3
Online ISBN: 978-94-91216-77-0
eBook Packages: Computer ScienceComputer Science (R0)