STOCHASTIC MULTI-ATTRIBUTE UTILITY MODEL

Authors

DOI:

https://doi.org/10.7251/ACE1730047R

Keywords:

Stochastic Multi-Attribute Utility Model, value, utility, probability, weights

Abstract

In real situations, the attribute value (mostly variable) can be best represented by introducing the finite number of attribute values level, to which the corresponding probabilities should also be attached. Stochastic Multi-Attribute Utility Model has the ability to analyze such stochastic multi-attribute problems. The choice of one, from the set of available options, is made by choosing the best option based on the maximum expected utility structure. In this paper, we will mention some arguments for the development of the Stochastic Multi-Attribute Utility Model, its advantages (they are closer to reality), disadvantages (analytically difficult technique, subjective assessments of the values of variable attributes), as well as the process of solving the problem.

References

Barbati, M., Greco, S., Kadziński, M., (2018). Optimization of multiple satisfaction levels in portfolio decision analysis. OMEGA, DOI: 10.1016/j.omega.2017.06.013

Garcia, F., Guijarro, F., Moya, I. (2011). A multicriteria approach, Theory of Multiobjective Optimization. Academic Press, Inc., Orlando, USA

Zaras, K. (2004). Rough approximation of a preference relation by a multi-attribute dominance for deterministic, stochastic and fuzzy decision problems. European Journal of Operational Research, DOI: 10.1016/S0377-2217(03)00391-6

Zhang, Y., Fan, Z. P. and Liu, Y. (2010). A method based on stochastic dominance degrees for stochastic multiple criteria decision making. Computers & Industrial Engineering, DOI: 10.1016/j.cie.2009.12.001

Yunna, W., Xu, H., Chuanbo, X., Xinli, X. (2017). An almost stochastic dominance based method for stochastic multiple attributes decision making. North China Electric Power University, Beijing, China. Intelligent Decision Technologies, vol. 11, no. 2, DOI: 10.3233/IDT-170289

Karande, P., Zavadskas, E., & Chakraborty, S. (2016). A study on the ranking performance of some MCDM methods for industrial robot selection problems. International Journal of Industrial Engineering Computations, 7(3), DOI: 10.5267/j.ijiec.2016.1.001

Nowak, M. (2004). Preference and veto thresholds in multicriteria analysis based on stochastic dominance. European Journal of Operational Research, DOI: 10.1016/j.ejor.2003.06.008

Nowak, M. (2007). Aspiration level approach in stochastic MCDM problems. European Journal of Operational Research, DOI: 10.1016/j.ejor.2005.10.003

Shenghai, Z., Xuanhua, X., Zhaohui, L., Zhang, F. (2017). Probability approximation to multi-attribute decision making method with stochastic attribute values. Journal of Intelligent & Fuzzy Systems, vol. 32, no. 3, DOI: 10.3233/JIFS-16511

Triantaphyllou, E. (2008). Multi-Criteria Decision Making Methods: Comparative study. Kluwer Academic Publishers, Dordrecht, The Netherlands

Figueira, J., Greco, S., & Ehrgott, M. (2016). Multicriteria decision analysis: State of the art surveys. New York. NY: Springer-Verlag, ISBN: 978-1-4939-3093-7. DOI:10.1007/B100605

Cables, E., Lamata, M.T., Verdegay, J.L. (2016). RIM-reference ideal method in multicriteria decision making. Information Sciences, 337-338, 1-10, DOI: 10.1016/j.ins.2015.12.011

Downloads

Published

2019-06-30

How to Cite

Račić, K., Račić, Željko, Damjanac, S., & Milić, D. (2019). STOCHASTIC MULTI-ATTRIBUTE UTILITY MODEL. Acta Economica, 17(30), 47–58. https://doi.org/10.7251/ACE1730047R

Issue

Section

Review article