SMAA methods and their applications: a literature review and future research directions
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Stochastic multicriteria acceptability analysis (SMAA) is a family of multiple criteria decision making (MCDM) methods dealing with incomplete, imprecise, and uncertain information on the evaluations and preference model parameters. As it provides a general framework that has extensions to deal with various specificities in MCDM problems, the development of SMAA methods and their applications in real-life decision-making problems have been increased over the recent years. This paper provides an up-to-date literature review of different SMAA methods and their applications in various areas. First, we selected, from different on-line data base, 118 articles published between 1998 and 2017. We categorized the selected papers into theoretical and applied. While the theoretical papers were analyzed based on the method’s aggregation procedure, type of problem, type of method’s outputs and inputs, the applied papers were separated and analyzed by application areas. Then, we provide some descriptive statistics, analyzing the papers regarding to publication year and journals of publication. Finally, we provide some guidelines to assist decision-makers in the choice of a SMAA method on a specific decision-making context and some future research directions.
KeywordsUncertainty Imprecision Multiple criteria decision making (MCDM) Stochastic multicriteria acceptability analysis (SMAA) Simulation
This research was supported by CAPES, the Brazilian Government Agency that supports Higher Education Personnel seeking to enhance their academic qualifications.
- Angilella, S., Corrente, S., & Greco, S. (2012). SMAA-Choquet: Stochastic multicriteria acceptability analysis for the choquet integral. In S. Greco, B. Bouchon-Meunier, G. Coletti, M. Fedrizzi, R. Matarazzo, & R. Yager (Eds.), Advances in computational intelligence (pp. 248–257). Berlin: Springer.CrossRefGoogle Scholar
- Corrente, S., Figueira, J. R., Greco, S., & Słowiński, R. (2017). A robust ranking method extending ELECTRE III to hierarchy of interacting criteria, imprecise weights and stochastic analysis. Omega (United Kingdom), 73, 1–17.Google Scholar
- De Graaf, G., Postmus, D., Buskens, E. (2015). Using multicriteria decision analysis to support research priority setting in biomedical translational research projects. BioMed Research International, 12, 1–9.Google Scholar
- Dias, L., Passeira, C., Malça, J., & Freire, F. (2016). Integrating life-cycle assessment and multi-criteria decision analysis to compare alternative biodiesel chains. Annals of Operations Research, https://doi.org/10.1007/s10479-016-2329-7.
- Eroglu, O., & Gencer, C. (2017). Integrating fuzzy dematel and smaa-2 for maintenance expenses. International Journal of Engineering Science Invention, 6(1), 2319–6726.Google Scholar
- Fazlollahtabar, H., & Aghasi, E. (2014). An integrated stochastic multi-criteria acceptability analysis and mathematical optimisation for service marketing. International Journal of Services and Operations Management, 17(1).Google Scholar
- González-Neira, E. M., García-Cáceres, R. G., Caballero-Villalobos, J. P., Molina-Sánchez, L. P., & Montoya-Torres, J. R. (2016). Stochastic flexible flow shop scheduling problem under quantitative and qualitative decision criteria. Computers & Industrial Engineering, 101, 128–144.CrossRefGoogle Scholar
- Govindan, K., Kadziński, M., Ehling, R., Miebs G. (2018). Selection of a sustainable third-party reverse logistics provider based on the robustness analysis of an outranking graph kernel conducted with ELECTRE I and SMAA. Omega (United Kingdom). https://doi.org/10.1016/j.omega.2018.05.007.
- Hallgreen, C., van den Ham, H., Mt-Isa, S., Ashworth, S., Hermann, R., Hobbiger, S., et al. (2014). Benefit-risk assessment in a post-market setting: A case study integrating real-life experience into benefit-risk methodology. Pharmacoepidemiology and Drug Safety, 23(9), 974–983.CrossRefGoogle Scholar
- Kangas, J., Hokkanen, J., Kangas, A. S., Lahdelma, R., & Salminen, P. (2003a). Applying stochastic multicriteria acceptability analysis to forest ecosystem management with both cardinal and ordinal criteria. Forest Science, 6(6), 928–937.Google Scholar
- Keeney, L., & Raiffa, H. (1976). Decisions with multiple objectives: Preferences and value trade-offs. New York: Wiley.Google Scholar
- Lahdelma, R., & Salminen, P. (2010a). A method for ordinal classification in multicriteria decision making. International Conference on Artificial Intelligence and Applications, 674, 420–425.Google Scholar
- Lahdelma, R., & Salminen, P. (2016). SMAA in robustness analysis. In Robustness analysis in decision aiding, optimization, and analytics (pp. 1–20). Berlin: Springer.Google Scholar
- Lahdelma, R., Miettinen, K., Salminen, P., Tervonen, T. (2004). Computational methods for stochastic multicriteria acceptability analysis. In European congress on computational methods in applied sciences and engineering.Google Scholar
- Li, Z., Wu, X., Liu, F., Fu, Y., & Chen, K. (2017). Multicriteria ABC inventory classification using acceptability analysis. International Transactions in Operational Research. https://doi.org/10.1111/itor.12412.
- Liu, J., Liao, X., Huang, W., & Liao X. (2018). Market segmentation: A multiple criteria approach combining preference analysis and segmentation decision. Omega (United Kingdom). https://doi.org/10.1016/j.omega.2018.01.008.
- Mendecka, B., Lombardi, L., & Kozioł, J. (2016). Probabilistic multi-criteria analysis for evaluation of biodiesel production technologies from used cooking oil. Renewable Energy. https://doi.org/10.1016/j.renene.2017.05.037.
- Roy, B. (1978). Electre iii : Un algorithme de classements fond sur une reprsentation floue des prfrences en prsence de critres multiples. Cahiers du CERO, 20(1), 324.Google Scholar
- Saaty, T. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 24(6), 19–43.Google Scholar
- Saint-Hilary, G., Cadour, S., Robert, V., & Gasparini, M. (2017). A simple way to unify multicriteria decision analysis (MCDA) and stochastic multicriteria acceptability analysis (SMAA) using a Dirichlet distribution in benefitrisk assessment. Biometrical Journal, 59(3), 567–578.CrossRefGoogle Scholar
- Soheilirad, S., Govindan, K., Mardani, A., Zavadskas, E. K., Nilashi, M., & Zakuan, N. (2017). Application of data envelopment analysis models in supply chain management: A systematic review and meta-analysis. Annals of Operations Research, 271(2), 915–969. https://doi.org/10.1007/s10479-017-2605-1.CrossRefGoogle Scholar
- Wang, H., Lahdelma, R., Salminen, P. (2018). Stochastic multicriteria evaluation of district heating systems considering the uncertainties. Science and Technology for the Built Environment, 1–9. https://doi.org/10.1080/23744731.2018.1457399.
- Xia, M. (2015). Stochastic multicriteria acceptability analysis based on choquet integral. Journal of Applied Mathematics. https://doi.org/10.1155/2015/315340.
- Yu, W. (1992). ELECTRE TRI—Aspects mthodologiques et guide d’utilisation. In Document du LAMSADE (p. 74). France: Université Paris Dauphine.Google Scholar
- Yu, Y., Zhu, W., & Zhang Q. (2017). DEA cross-efficiency evaluation and ranking method based on interval data. Annals of Operations Research, 1–17. https://doi.org/10.1007/s10479-017-2669-y.
- Zhou, H., Wang, J.-q., & Zhang H.-y. (2017). Stochastic multicriteria decision-making approach based on SMAA-ELECTRE with extended gray numbers. International Transactions in Operational Research. https://doi.org/10.1111/itor.12380.