Easy-Interactive Ordering of the Pareto Optimal Set with Imprecise Weights
Commenced in January 2007
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Easy-Interactive Ordering of the Pareto Optimal Set with Imprecise Weights

Authors: Maria Kalinina, Aron Larsson, Leif Olsson

Abstract:

In the multi objective optimization, in the case when generated set of Pareto optimal solutions is large, occurs the problem to select of the best solution from this set. In this paper, is suggested a method to order of Pareto set. Ordering the Pareto optimal set carried out in conformity with the introduced distance function between each solution and selected reference point, where the reference point may be adjusted to represent the preferences of a decision making agent. Preference information about objective weights from a decision maker may be expressed imprecisely. The developed elicitation procedure provides an opportunity to obtain surrogate numerical weights for the objectives, and thus, to manage impreciseness of preference. The proposed method is a scalable to many objectives and can be used independently or as complementary to the various visualization techniques in the multidimensional case.

Keywords: Imprecise weights, Multiple objectives, Pareto optimality, Visualization.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330177

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References:


[1] I. Das, "A preference ordering among various Pareto optimal alternatives," Structural and Multidisciplinary Optimization, vol. 18, no. 1, pp. 30-35, 1999a.
[2] G. K. Kao and S. H. Jacobson, "Finding preferred subsets of Pareto optimal solutions," Computational Optimization and Applications, vol. 40, no. 1, pp. 73-95, 2008.
[3] C. A. Mattson, A. A. Mullur and A. Messac, "Smart Pareto filter: obtaining a minimal representation of multiobjective design space," Engineering Optimization, vol. 36, no. 6, pp. 721-740, 2004.
[4] M. Farrow and M. Goldstein, "Sensitivity of decisions with imprecise utility trade-off parameters using boundary linear utility," International Journal of Approximate Reasoning, vol. 51, no. 9, pp. 1100-1113, 2010.
[5] V. Venkat, S. H. Jacobson and J. A. Stori , "A Post-Optimality Analysis Algorithm for Multi-Objective Optimization," Computational Optimization and Applications, vol. 28, no. 3, pp. 357-372 , 2004.
[6] T. Aittokoski, S. ├äyr├ñmö and K. Miettinen , "Clustering aided approach for decision making in computationally expensive multiobjective optimization," Optimization Methods and Software, vol. 24, no. 2, pp. 157-174, 2009.
[7] A. V. Lotov and K. Miettinen, "Visualizing the Pareto Frontier," in Multiobjective Optimization, Berlin Heidelberg, Springer-Verlag, 2008, pp. 213-243.
[8] P. Korhonen and J. Wallenius, "Visualization in the Multiple Objective Decision-Making Framework," in Multiobjective Optimization, Berlin / Heidelberg, Springer, 2008, pp. 195-212.
[9] R. Efremov, D. R. Insua and A. Lotov, "A framework for participatory decision support using Pareto frontier visualization, goal identification and arbitration," European Journal of Operational Research, vol. 199, no. 2, pp. 459-467, 2009.
[10] X. Bi and B. Li, "The visualization decision-making model of four objectives based on the balance of space vector," Nanchang, Jiangxi, 2012.
[11] K. Deb, Multi-Objective Optimization using Evolutionary Algorithms, United Kingdom: John Wiley & Sons, Ltd, 2009.
[12] K. Miettinen, "Introduction to Multiobjective Optimization: Noninteractive Approach," in Multiobjective Optimization, Berlin Heidelberg, Springer-Verlag, 2008, pp. 1-26.
[13] A. V. Lotov and K. Miettinen, "Visualizing the Pareto Frontier," in Multiobjective Optimization, Berlin Heidelberg, Springer-Verlag, 2008, pp. 213-243.
[14] K. Miettinen, "Introduction to Multiobjective Optimization: Noninteractive Approach," i Multiobjective Optimization, Berlin Heidelberg, Springer-Verlag, 2008, pp. 1-26.
[15] I. Das, "On characterizing the "knee" of the Pareto curve based on Normal-Boundary Intersection," Structural and Multidisciplinary Optimization, vol. 18, no. 2, pp. 107-115, 1999b.
[16] M. Riabacke, M. Danielson och L. Ekenberg, "State-of-the-art in prescriptive criteria weight elicitation," Advances in Decision Sciences, 2012.
[17] T. L. Saaty, The Analytic Hierarcy Process, McGraw Hill International, 1980.
[18] H. F. Barron, "Selecting a Best Multiattribute Alternative with Partial Information about Attribut Weights," Acta Psychologica, vol. 80, no. 1- 3, pp. 91-103, 1992.
[19] H. F. Barron och B. E. Barrett, "Decision quality using ranked attribute weights," Management Science, vol. 42, nr 11, pp. 1515-1523, 1996.
[20] M. Kalinina, A. Larsson and L. Olsson, "Generating and Ordering of Transport Alternatives in Inter-Modal Logistics in the Presence of Cost, Time, and Emission Conflicts," 2012 IEEE Int. Conference on Industrial Engineering and Engineering Management, 2012.