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Robustness and sensitivity analysis in multiple criteria decision problems using rule learner techniques

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  • Rocco S., Claudio M.
  • Hernandez, Elvis

Abstract

In many situations, a decision-maker is interested in assessing a set of alternatives characterized simultaneously by multiple criteria (attributes), and defining a ranking able to synthesize the global characteristics of each alternative, for example, from the best to the worst. This is the case of the assessment of several projects through attributes such as cost, profitability, among others. The behavior of each object, for every criterion, is quantified via numerical or categorical “performance values†. Several multiple criteria decision techniques could be used to this aim. However the base rank could be influenced by uncertain factors associated to specific criteria (e.g., the “ratio Benefit/Cost of a project†could be affected by variations in the interest rate) or by decision-maker preferences. In this situation, the decision-maker could be interested knowing what sets of factors are responsible of specific ranking conditions.

Suggested Citation

  • Rocco S., Claudio M. & Hernandez, Elvis, 2015. "Robustness and sensitivity analysis in multiple criteria decision problems using rule learner techniques," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 297-304.
    • Handle: RePEc:eee:reensy:v:134:y:2015:i:c:p:297-304
    DOI: 10.1016/j.ress.2014.04.022
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    References listed on IDEAS

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    1. Wolters, W. T. M. & Mareschal, B., 1995. "Novel types of sensitivity analysis for additive MCDM methods," European Journal of Operational Research, Elsevier, vol. 81(2), pages 281-290, March.
    2. Yoram Wind & Thomas L. Saaty, 1980. "Marketing Applications of the Analytic Hierarchy Process," Management Science, INFORMS, vol. 26(7), pages 641-658, July.
    3. Insua, David Rios & French, Simon, 1991. "A framework for sensitivity analysis in discrete multi-objective decision-making," European Journal of Operational Research, Elsevier, vol. 54(2), pages 176-190, September.
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    Cited by:

    1. Hernandez-Perdomo, Elvis & Guney, Yilmaz & Rocco, Claudio M., 2019. "A reliability model for assessing corporate governance using machine learning techniques," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 220-231.
    2. Hernandez-Perdomo, Elvis A. & Mun, Johnathan & Rocco S., Claudio M., 2017. "Active management in state-owned energy companies: Integrating a real options approach into multicriteria analysis to make companies sustainable," Applied Energy, Elsevier, vol. 195(C), pages 487-502.

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