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SMAA in Robustness Analysis

In: Robustness Analysis in Decision Aiding, Optimization, and Analytics

Author

Listed:
  • Risto Lahdelma

    (Aalto University)

  • Pekka Salminen

    (University of Jyväskylä)

Abstract

Stochastic multicriteria acceptability analysis (SMAA) is a simulation based method for discrete multicriteria decision aiding problems where information is uncertain, imprecise, or partially missing. In SMAA, different kind of uncertain information is represented by probability distributions. Because SMAA considers simultaneously the uncertainty in all parameters, it is particularly useful for robustness analysis. Depending on the problem setting, SMAA determines all possible rankings or classifications for the alternatives, and quantifies the possible results in terms of probabilities. This chapter describes SMAA in robustness analysis using a real-life decision problem as an example. Basic robustness analysis is demonstrated with respect to uncertainty in criteria and preference measurements. Then the analysis is extended to consider also the structure of the decision model.

Suggested Citation

  • Risto Lahdelma & Pekka Salminen, 2016. "SMAA in Robustness Analysis," International Series in Operations Research & Management Science, in: Michael Doumpos & Constantin Zopounidis & Evangelos Grigoroudis (ed.), Robustness Analysis in Decision Aiding, Optimization, and Analytics, chapter 0, pages 1-20, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-33121-8_1
    DOI: 10.1007/978-3-319-33121-8_1
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    Citations

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    Cited by:

    1. R. Pelissari & M. C. Oliveira & S. Ben Amor & A. Kandakoglu & A. L. Helleno, 2020. "SMAA methods and their applications: a literature review and future research directions," Annals of Operations Research, Springer, vol. 293(2), pages 433-493, October.
    2. Paweł Ziemba, 2021. "Selection of Electric Vehicles for the Needs of Sustainable Transport under Conditions of Uncertainty—A Comparative Study on Fuzzy MCDA Methods," Energies, MDPI, vol. 14(22), pages 1-25, November.
    3. Ziemba, Paweł, 2022. "Uncertain Multi-Criteria analysis of offshore wind farms projects investments – Case study of the Polish Economic Zone of the Baltic Sea," Applied Energy, Elsevier, vol. 309(C).
    4. Athanasios P. Vavatsikos & Efstratios Tsesmetzis & Georgios Koulinas & Dimitrios Koulouriotis, 2022. "A robust group decision making framework using fuzzy TOPSIS and Monte Carlo simulation for wind energy projects multicriteria evaluation," Operational Research, Springer, vol. 22(5), pages 6055-6073, November.
    5. Pelissari, Renata & Oliveira, Maria Célia & Ben Amor, Sarah & Abackerli, Alvaro José, 2019. "A new FlowSort-based method to deal with information imperfections in sorting decision-making problems," European Journal of Operational Research, Elsevier, vol. 276(1), pages 235-246.

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